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Tag Archives: U.S. Navy
US Navy to Evaluate Kraken Sonar
Kraken Robotics Inc.'s wholly owned subsidiary, Kraken Robotic Systems Inc., has been selected by the U.S. Office of the Secretary of Defense, Comparative Test Office, to participate in a Foreign Comparative Testing (FCT) program in support of a U.S. Navy Program of Record managed by the Naval Sea Systems Command, EOD Program Office. The FCT is designed to test selected
So, This is Ocean Tech?
By Viviana Castillo, APL-UW The humid air penetrates my pores as I sit over the bridge on a massive ship. The sun is setting, I can hear the engines roaring as the water splashes the sides of the hull. I put my headphones on to try and drown out the noise. It doesn't work, but I pretend it does. I'm
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U.S. Coast Guard Discusses Its Developing Role in the Arctic
SEATTLE — The U.S. Coast Guard's Pacific Area commander discussed the service's strategic outlook for the Arctic, its new class of polar security cutters, and its efforts to improve maritime domain awareness in the high latitudes. The keynote speech given by Vice Adm. Linda Fagan was during the Arctic Encounter Symposium in Seattle, Washington. The U.S. Coast Guard (USCG) recently
Market Report Discusses Trends in Anti-Submarine Warfare
Anti-submarine warfare has once again risen high on the agenda for NATO's maritime forces. Ahead of the inaugural Anti-Submarine Warfare conference, Defence IQ compiled a market report detailing ongoing ASW and submarine programs from nations around the world. The report offers information about: · Key ASW programs and requirements in countries including: Australia, Belgium, France, India, Italy, Malaysia, Russia, Singapore
US Navy Updates Protocol for Reporting UFOs
The U.S. Navy is drafting new guidelines for personnel to report unauthorized and unidentified aircraft sightings—what were previously called "unidentified flying objects" or UFOs. The Navy said, in a statement to Politico, that it is not "endorsing the idea that its sailors have encountered alien spacecraft" but rather "acknowledging that there have been enough strange aerial sightings by credible and
Amphibious Transport Dock Ship Participates in Exercise in Thailand
An amphibious transport dock ship commissioned by the U.S. Navy participated in the Cobra Gold exercise in Thailand Feb. 12-23, 2019. During the exercises, the ship, USS Green Bay, launched assault amphibious vehicles (AAVs) from its well deck. USS Green Bay is forward deployed to Sasebo, Japan as part of Commander, Amphibious Force 7th Fleet and is operating in the


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BookTech Expo Registration Jumps More Than 30%
Celebrating its ninth year, the 2005 BookTech Conference and Expo, held at the Hilton New York, March 7-9, opened on a spring-like day that might have seen people heading to the Bronx for the Yankees home opener were it actually spring. Although the balmy 60-degree day didn't last, the cold winds and snow that swept through Manhattan on the conference's second day weren't enough to keep book publishers from coming to see the latest offerings in production and workflow tools. Registration to the Expo was up more than 30 percent over 2004.
A New Setup
The Conference and Expo started with a new twist. Day one, which in past years featured the keynote and the opening of the exhibit floor, was set-up day for expo exhibitors this year, while conference attendees were treated to a morning publishers' workshop on negotiating print contracts, and an afternoon track conducted by IDEAlliance, a nonprofit membership organization dedicated to advancing user-driven, cross-industry technology solutions for the publishing industry, that focused on how standards and best practices simplify an end-to-end supply chain in the publishing environment.
"We're happy to have a partnership with IDEAlliance to develop content on standards and best practices in the industry," says Mark Hertzog, publisher of BookTech Magazine. "[IDEAlliance] discussed standards in the industry that were of interest to magazine publishers, books publishers and ad agencies."
The doors to the expo floor opened on Tuesday morning, where 118 exhibitors displayed a panoply of tools and resources ranging from color-management solutions, cover-board materials and book-publishing components. Printers were also on hand, offering full-service programs; paper companies offered leading-edge paper products; and others offered a range of publishing-related solutions. Interest piqued that first morning and held steady through the rest of the show.
"We're having a great show," said Joe Dunham, an account manager with LBS, a Des Moines, Iowa-based manufacturer and converter of binding materials. "We had more visitors in the first four hours [of the show] than we had all of last year."
IDEAlliance
Visual Systems, Inc.

redpajama_set_name: RedPajamaCommonCrawl


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Indirect costs are costs of an organization that are not readily assignable to a particular project, but are necessary to the operation of the organization and the performance of the project. Examples of costs usually treated as indirect include those incurred for facility operation and maintenance, depreciation, and administrative salaries.
Transferring funds into or out of the indirect cost category is not allowable without prior approval from the awarding agency. A budget modification is required.


Introducing the 2000 Ford Ranger. Smooth gearshifts are achieved thanks to the refined 6 cylinder engine, providing a spirited, yet composed ride and drive. Four wheel drive allows you to go places you've only imagined. All of the premium features expected of a Ford are offered, including: a tachometer, variably intermittent wipers, a rear step bumper, a front bench seat, and much more. Ford ensures the safety and security of its passengers with equipment such as: dual front impact airbags, ignition disabling, and ABS brakes. It also arrives with a Carfax history report, providing you peace of mind with detailed information. Our sales reps are extremely helpful & knowledgeable. They'll work with you to find the right vehicle at a price you can afford. Stop by our dealership or give us a call for more information.


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Long-Discredited Positions on the Monetary System
Thomas L. Hogan
In a recent New York Times opinion column, former Treasury secretary Steven Rattner criticized the nomination of Judy Shelton to the Federal Reserve Board of Governors. Rattner accuses Ms. Shelton of taking "long-discredited positions on the monetary system."
Specifically, Mr. Rattner claims that the gold standard magnified economic volatility, resulted in more frequent banking crises, and was "a significant culprit in deepening the Great Depression." Such beliefs may be common to those unfamiliar with monetary history, but they are not consistent with the findings of modern economic research.
Economic Stability
Mr. Rattner wrongly argues that "a gold standard exaggerates economic swings" and "magnifies [price movements], as a comparison of the pre-Depression period to the post-World War II era makes clear." While such claims were commonly believed in the 1960s, economists have long since overturned those older ideas.
Research by Christina Romer, who served as chair of the Council of Economic Advisers under President Obama, shows that increased stability in the post-World War II period is merely "a figment of the data" and that "traditional estimates of prewar GNP exaggerate the size of cycles." She also finds that postwar employment stability "is an artifact of improvements in data collection procedure."
A number of studies have built on Romer's research. Joseph Davis finds that the Fed has not reduced the frequency or duration of industrial fluctuations. George Selgin, William Lastrapes, and Lawrence White conclude that the Fed has failed to achieve its stated goals of reducing monetary and financial instability. In my own research, I find that economic volatility did not improve under the Fed until the Great Moderation of the 1980s, while rates of economic growth and price inflation were both better on the pre-Fed gold standard.
Bank Failures and Crises
The narrative that the gold standard led to increased bank failures and financial crises is also inconsistent with historical evidence.
Banking crises have occurred throughout US history, but such problems were not caused by the gold standard. Branch banking restrictions made it difficult for banks to diversify investments, thereby increasing their likelihood of failure. Requirements to back banknotes with government bonds prevented banks from accommodating seasonal changes in money demand, resulting in reserve drains that significantly reduced financial stability. The blame for bank failures and crises thus lies not with the gold standard but on regulatory restrictions that destabilized the banking system.
Nor has the Fed curtailed financial crises, which were actually less frequent under the gold standard. "Since 1973," Barry Eichengreen and coauthors write, "crisis frequency has been double that of the Bretton Woods and classical gold standard periods." The admission is especially noteworthy, as Eichengreen is an outspoken critic of the gold standard.
The Fed and the Great Depression
Economists long ago abandoned Rattner's view that the gold standard caused the Great Depression in the United States. In their 1963 treatise A Monetary History of the United States: 1867-1960, Milton Friedman and Anna Schwartz famously demonstrated that the Fed's mismanagement—not the gold standard—was to blame.
Further research has confirmed the findings of Friedman and Schwartz. For example, former Fed Chair Ben Bernanke and coauthor Harold James find that, rather than being constrained by the gold standard, "the initial [monetary] contractions in the United States and France were largely self-inflicted wounds" due to policy mistakes by their respective central banks.
At a 2002 conference held in honor of Friedman, Bernanke acknowledged the Fed's error. "Regarding the Great Depression," Bernanke said, "You're right, we did it. We're very sorry."
Considering the evidence, it appears to be Mr. Rattner, rather than Ms. Shelton, who holds "long-discredited positions on the monetary system." Historical bank failures and crises, particularly during the Great Depression, are generally attributable to interference by regulators and central bankers, not to the gold standard.
Perhaps such mistakes could be avoided in the future with Ms. Shelton's guidance.
Thomas L. Hogan, Ph.D., is senior research faculty at AIER. He was formerly the chief economist for the U.S. Senate Committee on Banking, Housing and Urban Affairs. He has also worked at Rice University's Baker Institute for Public Policy, Troy University, West Texas A&M University, the Cato Institute, the World Bank, Merrill Lynch's commodity trading group and for investment firms in the U.S. and Europe. Dr. Hogan's research has been published in academic journals such as the Journal of Macroeconomics and the Journal of Money, Credit and Banking. He has appeared on programs such as BBC World News, Stossel TV, and Bloomberg Radio and has been quoted by news outlets including CNN Business, American Banker, and the National Review.
"Bank Lending and Interest on Excess Reserves: An Empirical Investigation," Journal of Macroeconomics, Forthcoming.
"The Calculus of Dissent: Bias and Diversity in FOMC Projections," Public Choice 19: 105-135 (2022).
"Hayek, Cassel, and the Origins of the Great Depression," (with Lawrence H. White) Journal of Economic Behavior & Organization, 181: 241-251 (2021).
"Has Dodd-Frank Affected Bank Expenses?" (with Scott Burns) Journal of Regulatory Economics, 55(2): 214–236 (2019).
"Ben Bernanke and Bagehot's Rules" (with Linh Le and Alexander William Salter) Journal of Money, Credit & Banking 47(2-3): 333-348 (2015).
Get notified of new articles from Thomas L. Hogan and AIER.
Related Articles – Central Banking, Gold Standard, Monetary Policy
Peter C. Earle
Rising Use of the Fed's Discount Window Raises Questions
Kate Wand
Beware the Soft Landing Narrative (Video)
Welcome the Disinflation, Beware the Narrative
William J. Luther
FOMC Ratchets Up Inflation Projection


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Svend Aage Sørensen (født 3. august 1926 i Øster Hassing, død 27. september 2002) var en dansk bokser, der deltog i OL 1948 i London i fjervægt. Han tabte sin første kamp til peruvianeren Pedro García på point og var dermed færdig i konkurrencen.

Svend Aage Sørensen boksede for IF Sparta, og han blev i 1947 valgt som Årets fund i dansk sport.

Noter

Eksterne henvisninger

Boksere fra Danmark
Olympiske boksere fra Danmark
Boksere ved sommer-OL 1948
Deltagere for Danmark ved sommer-OL 1948
Årets fund i dansk idræt

redpajama_set_name: RedPajamaWikipedia


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\section{Introduction}
The rapid increase of cloud services brings remarkable convenience to our daily life and promotes the Internet economy. However, it is faced with abundant threats from malicious attackers. According to the data from the annual report of Micro Focus~\cite{MICROFOCUS2018report}, there are almost 51\% growth of disclosed vulnerabilities that are related with Web applications in 2017, and nearly 95\% of Web applications are vulnerable to sensitive data exposure, which would cause great harm to the usage of cloud services. Therefore, it is highly expected to accurately detect anomalies in network traffic. To this end, a variety of methods have been developed, which could be roughly classified into the following categories, namely, rule-based methods, flow-based methods, and packet-based methods.

As one typical method of rule-based anomaly detection, Carmen et al.~\cite{torrano2011applying} applied feature selection called Generic-Feature-Selection to construct domain specific rules for Web application firewall. By adopting and integrating these technology~\cite{torrano2015combining,lin2004constructing}, a number of powerful tools have been developed for constructing domain specific rules from known threats, such as Suricata~\cite{suricata} and Snort~\cite{roesch1999snort}. These tools use a highly efficient engine to discover malicious traffic by comparing the extracted signatures with the predefined rules. If malicious traffic is detected, actions can be taken to protect the cloud services. Although the rules-based methods are effective for the known threats, they heavily depend on in-depth expert knowledge, e.g., signatures describing anomalies.

Recently, some machine learning methods have been proposed to detect traffic anomaly. There are two popular directions, which use flow-based information and packet-based information to detect anomalies respectively. Flow-based anomaly detection usually treats the representation of network traffic as a type of time series \cite{zhou2019dual,zhang2018adaptive,ren2017piecewise}. \cite{xu2005profiling} used the five-tuple to construct comprehensive behavior profiles of network traffic in terms of communication patterns of end-hosts and services. The anomalies are detected by exploring the correlation between the traffic behaviors and the corresponding characteristics. \cite{da2016atlantic} presented a framework called ATLANTIC which uses similarities of flows to detect threats in traffic flows. These methods could achieve a competitive performance when the flow-based behaviors are presented. However, they do not perform well for some kinds of attacks, e.g., shell-code and SQL injection, which do not express abnormal characteristics in flow-based information.

Packet-based anomaly detection can unveil anomalies by inspecting the packet payload, which refers to the user data of network packet. The objective of packet-based anomaly detection is to discover the possible attacks that have potential abnormal characteristics in the packet payload. The anomalies might appear as a number of specific strings. For example, as one of the most common attacks, i.e., SQL injection, which injects anomalous codes, such as `` ' or 1=1 - -'', into conditional statements of SQL queries to make them always be true. To detect this kind of anomalies from packet payload, a variety of methods have been proposed. The PAYL was proposed in \cite{wang2004anomalous}, which used 1-gram frequency distribution of the packet payload as features to detect network anomalies. McPAD was then proposed in \cite{perdisci2009mcpad}, which developed a modified feature extraction method for accurate anomaly detection. More recently, deep learning technology is explored for payload anomaly detection. Several literature \cite{marin2018rawpower,zhang2017a,qin2018attentional} investigated by using raw measurements to detect payload anomalies. These methods use deep learning technologies to automatically extract features from packet payload. However, the performance of payload anomaly detection is still undesirable due to the incomplete representation of features. As shown in Figure \ref{longtermlabel}, there are two types of packet payload anomalies that have different distributions of anomalous bytes. Unlike the short-term packet payload anomalies whose anomalous bytes are concentrated, the anomalous bytes for long-term are scattered and their abnormal characteristics can not be addressed by existing works. Most of the existing detection methods ignore the long-term dependency relationships among the anomalous bytes.

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{long_term.png}
\centering
\caption{Two examples of the packet payload anomalies that have different distributions of anomalous bytes. For the short-term packet payload anomaly, the potential anomalous bytes are concentrated and their abnormal characteristics may be obvious, which could be detected by existing methods. In contrast, for the long-term packet payload anomaly, the potential anomalous bytes are scattered and their abnormal characteristics may not be addressed by existing works. Long-term anomalies in packet payload are more difficult to be detected than short-term ones for the existing methods.}
\label{longtermlabel}
\end{figure}

To tackle this, we propose a payload anomaly detection framework, which consists of two parts. The former part of the proposed framework is a feature engineering method, which consists of two steps. First, it introduces a sliding block to construct block sequences from packet payload. Second, the low-frequency items of block sequences are filtrated by a dictionary and the high-frequency items are encoded into the low-dimension embedded vectors by a self-learning block embedding layer. The proposed feature engineering method constructs the block-based features, which contain both the high-dimension information and the underlying sequential information to reveal the characteristics of payload. The latter part of the proposed framework is a detection model, which has an LSTM and CNN based neural network for learning both the potential long-term and short-term dependency relationships among the block-based features and an MLP based classifier to discover potential attacks. The major contributions of this paper could be summarized as follows:
\begin{itemize}
	\item We propose a feature engineering method that constructs block-based features of the packet payload, which could reveal the long-term dependency relationships among the anomalous bytes in packet payload. Our feature engineering method are not dependent on in-depth expert knowledge. To the best of our knowledge, this could be the first work that explores the long-term dependency relationships among the anomalous bytes for the payload anomaly detection.
	\item We design a detection model that contains an LSTM and CNN based neural network to learn both the long-term and short-term dependency relationships in the block-based features and an MLP based classifier to discover potential attacks in the packet payload.
	\item We evaluate the proposed framework that integrates the feature engineering method and the detection model by using three public datasets.
\end{itemize}

The rest of the paper is organized as follows. Section 2 introduces the related work, including the traditional technology and the deep learning technology for network anomaly detection. Section 3 presents the proposed framework, which integrates a feature engineering method and a detection model. In Section 4, we evaluate the proposed framework by using three public datasets. We conclude the paper in Section 5.

\section{Related work}

\subsection{Network Anomaly Detection}

{Network anomaly detection is a fundamental task for the quality of service (QoS) of Internet. A lot of previous work focused on the anomaly detection of low-level network flows or high-level backbone networks. To detect anomalies through flow-based information, ATLANTIC\cite{da2016atlantic} used deviations in the entropy of traffic flow tables to detect threats in traffic flows. In \cite{xu2005profiling}, K. Xu et al. detected anomalies by exploring the correlation between traffic behaviors and the corresponding characteristics in backbone networks. These methods could achieve a high detection accuracy for flow-based anomalies, but it is unlikely to detect attacks that insert anomalies in packet payloads, e.g., shell-code and SQL injection. Packet-based anomaly detection methods focus on inspecting the abnormal information in the packet payload. K. Wang et al. \cite{wang2004anomalous} proposed PAYL which uses the 1-gram frequency distribution of the payload as features to detect anomalies. R. Perdisci et al. \cite{perdisci2009mcpad} proposed McPAD to construct modified 2-gram features that contain abundant information for accurate anomaly detection. However, the accuracy of these methods heavily depend on feature construction that is complex and requires in-depth expert knowledge.}

\subsection{Deep Learning Methods for Network Anomaly Detection}

{Deep learning technology, which could automatically learns representation of data, was recently explored to address the limitations of the traditional machine learning methods. To detect the flow-based threats in network traffic, many studies investigated the power of deep learning for flow-based anomaly detection. Kim et al.\cite{kim2018web} proposed C-LSTM neural network for effectively modeling the spatial and temporal information contained in raw data to detect anomalies in traffic. Tang et al. \cite{tang2016deep} proposed a flow-based Deep Neural Network (DNN) model for intrusion detection in a software defined networking environment. To detect payload-based attacks, several detection models using the raw payload data as input have been investigated in the literature. Gonzalo et al. \cite{marin2018rawpower} applied deep CNN and LSTM neural networks for network intrusion detection with different representations of payload data. Arne et al.\cite{bochem2017streamlined} applied LSTM neural networks to learn latent characteristics of normal requests. H.Liu et al.\cite{liu2019cnn} implemented an end-to-end deep learning detection models using raw payload data. Wei et al.\cite{wang2017hast} proposed hierarchical spatial-temporal features-based intrusion detection system, which applied deep CNN to learn the low-level spatial features of network traffic and used LSTM to learn the high-level temporal features. Sheraz N.et al.\cite{naseer2018enhanced} developed several neural networks to build network anomaly detection models, including CNN, auto encoders and recurrent neural networks (RNN).}

\subsection{Summary}
The most related work to our paper is \cite{qin2018attentional}, which proposed a RNN model with the attention mechanism called ATPAD to detect anomalies in the packet payload. The ATPAD employs the word embedding and RNN to extract features, which are used at the attention calculation stage to capture the correlation between potential byte of payload and the detection results. Different from the ATPAD model, we propose a novel feature engineering method which utilizes the raw packet payload data to construct the block-based features. The block-based features contain two different kinds of information that retain both long-term and short-term dependency relationships among the packet payload. We also employs a neural network based on LSTM and CNN rather than the RNN model with the attention mechanism to capture the long-term dependency relationships among the anomalous bytes. To the best of our knowledge, our model achieves state-of-the-art performance on the CSIC 2010 dataset\cite{csic}.

\section{Proposed framework}

The proposed anomaly detection framework is shown in Figure \ref{frameworklabel}. There are four modules in this framework. The first two modules make up the former part of the proposed framework, which aims to construct block-based features for efficient feature extraction. In the first module, the payload is extracted and labeled through a preprocessing process. Then, the block sequence is constructed by the sliding block and in order to remove redundant information, the high-frequency items in the block sequence are selected by a dictionary. In the block embedding process, the block-based features are constructed by encoding each item in block sequence into an embedded vector. The last two modules form the latter part of the proposed framework, which aims to adaptively detect anomalies for packet payload. Specifically, a neural network based on the LSTM and the CNN is designed to learn both the long-term and short-term dependency relationships in the block-based features and an MLP is adopted as a classifier to detect anomalies in each sample. In order to better understand how the framework works, the framework is described in details in the following subsections.

\begin{figure}[!h]
\centering
\includegraphics[width=\textwidth]{framework.pdf}
\centering
\caption{Overview of the proposed framework. The proposed framework contains four modules. First, the payload is extracted and labeled through a preprocessing process. Then, the block-based features are constructed for each payload. In the last two modules, a detection model based on the LSTM, CNN and MLP is designed for packet payload anomaly detection.}
\label{frameworklabel}
\end{figure}

\subsection{Packet Payload Preprocessing}
The objective of the packet payload preprocessing is to extract the payload from the packet and to convert the payload into a suitable form for the following feature engineering method. The payload extraction is conducted by packet parsing based on the low-level communication protocols. The following process will try to construct efficient expression for the extracted payload. Thus, instead of employing the encoding method, e.g., popular one-hot encoding, to transform the extracted payload to an embedding vector with fixed length and possible zero padding, we directly process the whole payload to a byte stream, which is a string with variable length. The byte stream and the label with respect to the same packet make up a sample for the preprocessed packet payload data.

\subsection{Block-Based Feature Extraction}
Instead of using the payload byte stream as features, we proposed a feature engineering method to extract the block-based features which contain the high-dimension information and the underlying sequential information for anomaly detection. The block-based feature extraction has two steps, i.e., block sequence construction and block embedding. Firstly, a block sequence is constructed by using the sliding block to extract numerous items that could be considered as short subsequences. For retaining the sequential information, the items are arranged in order. Secondly, to reduce the redundant information unrelated to anomalous bytes in block sequences, the high-frequency items of each block sequence are selected by a dictionary and encoded into embedded vectors through block embedding process.
\begin{figure}
\centering
\includegraphics[width=\textwidth]{block_sequences.pdf}
\centering
\caption{An example for the process of block sequence construction. With a sliding block of length 3 and a fixed stride, the blocks extracted from a packet payload form a block sequence.}
\label{blocksequenceslabel}
\end{figure}

The process of block sequence extraction is shown in Figure \ref{blocksequenceslabel}. A sliding block of specific length slides on each sample consecutively. When the sliding block slides to a certain position, an item would be extracted, then the sliding block would move with a fixed stride to extract items repeatedly. Finally, the block sequence is constructed by arranging blocks in a sequence according to the order of extraction process.

As mentioned above, the high-dimension information and underlying sequential information are retained in the block sequences, which are not just useful for detecting general anomalies in the payload, but also efficient for detecting anomalous bytes that have long-term dependency relationships. First of all, the high-dimension information could be considered as a kind of semantic information, which is affected by the length of sliding block. Intuitively, the longer the sliding block is, the more high-dimension information the item contains. As is shown in Figure \ref{slidingwindowlabel}(a)\&(b), for the same part of the packet payload, when the block length equals to 2, the items $ka$, $ae$, $ef$ are extracted by the sliding block. They have more information than single character $k$, $a$, $e$, $f$ that are extracted when the block length equals to 1. However, when the length of the sliding block is too long, the extracted features would contain a mixture of normal information and abnormal information, which might confuse the learning process for anomaly detection. Thus, a suitable length of the sliding block should be chosen.

Moreover, as the length of sliding block increases, the block sequence could contain more abundant sequential relationships. To be specific, under the ASCII extended $256$ standard, there are about $256^{2 n}$ possibilities of the sequential relationships between items of length $n$. As is shown in Figure \ref{slidingwindowlabel}(c), when the block length equals to 2, the item $ka$ has 2 different sequential relationships, i.e., $ka$$\longrightarrow$$ae$ and $ka$$\longrightarrow$$ab$. When the block length equals to 1, the item $k$ only contains the sequential relationship $k$$\longrightarrow$$a$. Furthermore, the expression of both the short-term and long-term dependency relationships in the block sequence is enhanced as the block length increases. This will benefit practical payload anomaly detection, especially for those that have long-term dependency relationships, such as the Union Query Attack\cite{halfond2006classification}.

The high-frequency items in the block sequence would be selected by a dictionary for the reason that there are plenty of redundant information unrelated to the anomalous bytes. In the extraction process of the sliding block, a dictionary is constructed to record the frequency of occurrence for each item and a threshold is set to limit the number of high-frequency items in the dictionary. By using the dictionary, each high-frequency item in the block sequence is selected and rearranged in the original order. Finally, each sample would be reconstructed into a sequence of selected items, which represent the significant information of each sample.

\begin{figure}
\centering
\includegraphics[width=\textwidth]{slidingwindow.png}
\centering
\caption{An example of block sequence construction process and information variations using different sliding block. Using a sliding block with a fixed stride, the blocks extracted from payload could be regarded as a set of short strings. Comparing the process of sliding blocks with different lengths in the same field of the payload, the block with length 2 extracts more abundant sequential relationships than the block with length 1.}
\label{slidingwindowlabel}
\end{figure}

Furthermore, the high-frequency items in the block sequence are encoded by block embedding layer in order to make a better expression of the high-dimension information and underlying sequential information. One-hot encoding does not work for this task, because it could not represent the similarity between different items and with the increase of number of items, it is faced with the curse of dimensionality. Inspired from distributed representation\cite{hinton1986learning}, the items in each block sequence are encoded into low-dimension embedded vectors by a self-learning block embedding layer. The block-based features are constructed by concatenating all the vectors in order.

The proposed feature engineering method builds the block-based features, which do not rely on in-depth expert knowledge, as several low-dimension embedded vectors to form a valid expression of packet payload.

\begin{figure}
\centering
\includegraphics[width=\textwidth]{totalframework.pdf}
\centering
\caption{An illustration of our proposed anomaly detection model.}
\label{totalframeworklabel}
\end{figure}

\subsection{Model Construction and Anomaly Detection}
The structure of the proposed anomaly detection model is presented in Figure \ref{totalframeworklabel}. A neural network based on LSTM and CNN is designed to learn the high-dimension information and the underlying sequential information contained in the block-based features. LSTM is used to learn the sequential dependency relationships among the block-based features, which are indicated in its hidden states of each time step. In order to learn both the long-term and the short-term dependency relationships in block-based features, we make use of the chosen LSTM hidden states in different time steps instead of only using the last hidden state that is widely adopted in classification tasks. CNN based structure is adopted to extract the local spatial information in the chosen hidden states and an MLP connected with a softmax layer is used as a classifier to detect anomalies.

In recent years, LSTM has been applied to machine translation\cite{bahdanau2014neural}, speech recognition\cite{graves2013speech}, and so on, for its capability of processing persistent information. Benefiting from its special memory cell structure and gating mechanism, it solves the exploding and vanishing gradient problems, which enable the efficient learning for long sequences. Therefore, LSTM is adopted for purpose of learning the long-term dependency relationships in the block-based features. In the proposed detection model, we employ LSTM to learn the relationships in the block-based features, i.e., the constructed features of our feature engineering method. At each time step, an embedded vector $v$ of block-based features is fed into the LSTM. The LSTM updates its cell state $c_{t}$ and outputs the current hidden state $h_{t}$ according to the previous hidden state $h_{t-1}$ and the current input $v_{t}$ through its inner non-linear operations. For the output of the LSTM at each time step $t$, the hidden state $h_{t}$ is calculated as follows\cite{Zhou2015A}:

\begin{equation}
f_{t}=\sigma\left(W_{f} \cdot\left[h_{t-1}, v_{t}\right]+b_{f}\right)
\end{equation}
\begin{equation}
i_{t}=\sigma\left(W_{i} \cdot\left[h_{t-1}, v_{t}\right]+b_{i}\right)
\end{equation}
\begin{equation}
o_{t}=\sigma\left(W_{o} \cdot\left[h_{t-1}, v_{t}\right]+b_{o}\right)
\end{equation}
\begin{equation}
c_{t}=f_{t} * c_{t-1}+i_{t} * \tanh \left(W_{c} \cdot\left[h_{t-1}, v_{t}\right]+b_{c}\right)
\end{equation}
\begin{equation}
h_{t}=o_{t} * \tanh \left(c_{t}\right)
\end{equation}

Here, the $f_{t}$, $i_{t}$ and $o_{t}$ are the forget, input and output gates respectively. They control the process for updating the LSTM hidden state. $\sigma$ is the logistic sigmoid function\cite{yin2003flexible} and the tanh is the hyperbolic tangent function\cite{xiao2005simple}. $W$ is the weight matrix and $b$ is the bias. The notations $\cdot$ and $*$ represent the Matmul product and the Hadamard product\cite{manevitz2000document} respectively.

Assume that the length of block-based features is $n$, which varies with different samples, there will be $n$ hidden states. The last hidden state of LSTM is widely adopted in classification tasks, however, it could not adequately express the long-term relationships in the block-based features. To tackle this, we choose $m$ candidates from the $n$ hidden states and these candidates are equally spaced in the ascending hidden states. This process not only preserves the long-term relationships, but also reduces the complexity of feature expression.

{CNN is powerful for its capability to learn spatial features and reduce feature space. Benefiting from sparse connectivity, shared weights and pooling, CNN extracts the spatial correlation information via convolution without any complex processing\cite{krizhevsky2012imagenet}. The CNN based structure in our model is used to extract high-level spatial information in the chosen hidden states. The chosen hidden states are concatenated in order and reshaped into a two-dimensional matrix. In the convolution layer, multiple convolution filters slide over the matrix to do the convolution operations, which extract the local spatial features. The learning process for CNN based structure is progressive, where the first convolution layer extracts low-level features and the next convolution layer extracts high-level features. After each convolution layer, a max-pooling layer is adopted to obtain the largest value of a small region, which preserves the important parameters and enhances the generalization ability of the model. In addition, the rectified liner unit (ReLU)\cite{nair2010rectified} is used as the activation function to add nonlinear constraint in the process. After the convolution and pooling, the spatial features of each sample are extracted and flattened into a vector which is further transmitted to the classifier.}

By casting the payload-based anomaly detection as a classification problem, an MLP is stacked on the above neural network to detect anomalies. The MLP has two layers, which would convert the flattened vector into a two-dimension vector $z$, and the softmax function maps it into a two-dimension distribution $s=\left(s_{0}, s_{1}\right)$ by Eq. (\ref{softmax}), whose values are scaled between 0 and 1, and the sum of these two values is 1. The sample would be labeled by the Eq. (\ref{addlabel}). The label 0 means the classifier judges the sample is normal, while the label 1 means the classifier judges the sample is anomalous.

\begin{equation}
s=\operatorname{Softmax}(z)
\label{softmax}
\end{equation}

\begin{equation}
label=\left\{\begin{array}{ll}{0,} & {\text { if } s_{0}<s_{1}} \\ {1,} & {\text { else }}\end{array}\right.
\label{addlabel}
\end{equation}

\subsection{Implementation}
In the proposed framework, there are four hyper-parameters needed to be set up, which includes the length of sliding block, the stride of sliding block, the number of high-frequency items in the dictionary and the number of chosen LSTM hidden states. In the experiments, the length of sliding block, the stride of sliding block, the number of high-frequency items in the dictionary, and the number of chosen states are set up as 3, 1, 15000, and 50, respectively.

Regarding the parameters of the neural network we have designed, the hidden units of LSTM are set to 128 and the LSTM is fed with a embedded vector of 64 dimensions at each time step. Two convolution layers and two pooling layers are implemented in the CNN based structure. These two convolution layers have 32 and 64 filters, respectively. All the filters in the convolution layers are with size of $4\times4$. Each pooling layer uses max-pooling with a $2\times2$ filter.

An MLP with two layers is used as a classifier, which has 128 and 2 hidden units respectively. It finally converts the feature maps into a two-dimension vector for classification. During the training process, we set the learning rate to 0.0001 for a stable training. The dropout rate for the MLP is 0.1.

\section{Evaluation}
\label{sec:guidelines}
{In this section, we conduct various experiments to evaluate the performance and effectiveness of the proposed framework for the payload anomaly detection. We first describe the datasets and metrics used for the evaluation. Then, experiments are conducted to evaluate the performance of the proposed framework on different aspects.}
\subsection{Datasets}
We conduct experiments on three datasets to evaluate the performance of the proposed method. These three datasets contain various types of network traffic attacks. We randomly divided each dataset into three parts, the training set, the validation set and the testing set. These three sets account for 70\%, 10\% and 20\% of the total data in each dataset, respectively. The overview of three datasets are shown in Table \ref{dataset} and the detailed description of each dataset is introduced as follows.
\subsubsection{CSIC 2010}
{The CSIC 2010 dataset\cite{csic} is developed at the Information Security Institute of Spanish Research National Council and contains thousands of Web requests which are generated automatically. The dataset consists of 72,000 normal requests and more than 25,000 anomalous requests, and all HTTP requests are marked as normal or abnormal. The CSIC 2010 dataset contains various types of Web attacks such as SQL injection, buffer overflow, information collection and so on.}
\subsubsection{CICIDS 2017}
{The CICIDS 2017 dataset\cite{cicids} contains both normal traffic and up-to-date attacks which resemble the true data. The dataset is developed by the Canadian Institute for Cyber Security. Various types of attacks, include DoS, DDos, heartbleed, web attack, infiltration and botnet, are collected in this dataset. In the experiments, we only use the traffic data collected in July 6, which contains three types of Web attacks that are related with the packet payload including Brute Force, XSS and SQL injection. }
\subsubsection{ISCX 2012}
{ISCX 2012 dataset\cite{ssh} contains network traffic which aims to describe network behaviors and intrusion patterns. This dataset contains actual traffic types such as HTTP, SMTP, SSH, IMAP, POP3, and FTP. It records packet payloads of traffic traces in the form of PCAP and the relevant profiles are publicly available for researchers. In our experiments, we use the traffic data collected in June 17th, which contains Brute Force SSH anomalies related with the packet payload.}

\begin{table}[ht]
\caption{The detailed description of three experimental dataset.}
\centering
\begin{tabular}{ccccc}
\toprule
\textbf{Dataset}  &\textbf{Category}	&\textbf{Train}  &\textbf{Validation} &\textbf{Test}  \\
\midrule
\multirow{2}*{CSIC 2010}
&Anomaly &17,617 &2,439 &5,009 \\
&Normal  &50,328 &7,268 &14,404 \\
\cmidrule{1-5}
\multirow{2}*{CICIDS 2017}
&Anomaly &6,374 &951 &1,822 \\
&Normal  &14,188 &1,987 &4,053 \\
\cmidrule{1-5}
\multirow{2}*{ISCX 2012}
&Anomaly &1,618 &241 &482 \\
&Normal  &33,722 &4807 &9,616 \\

\bottomrule
\end{tabular}
\label{dataset}
\end{table}

\subsection{Performance Metric}
{In our experiments, we consider the abnormal packet payload to be a positive sample and the normal packet payload to be a negative sample. The methods performed in the experiments are evaluated on five metrics, i.e., Precision, Detection Rate (DR), False Positive Rate (FPR), Accuracy and F1-Score. These metrics are defined based on four related parameters, i.e., TP, TN, FP, FN, where TP represents the number of true positive samples, FN represents the number of false negative samples, FP represents the number of false positive samples and the TN represents the number of true negative samples. }

The definitions of the five metrics are listed as follows:

\begin{equation} { Precision }=\frac{TP}{TP
+F P}\label{eq1}\end{equation}
\begin{equation}DR=\frac{TP}{TP+FN}\label{eq2}\end{equation}
\begin{equation} FPR=\frac{FP}{FP+TN}\label{eq3}\end{equation}
\begin{equation}{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN}\label{eq4}\end{equation}
\begin{equation} F_{1} \text{-score }=2\times\frac{\text{Precision}\times DR}{\text{Precision}+DR}\end{equation}

\subsection{Results and Discussion}

{In this section, we have implemented five experiments to evaluate the performance of the proposed framework in the following five aspects: }

\begin{itemize}
\item Experiment A: How is the performance of the proposed framework compared with the traditional machine learning methods and other state-of-the-art methods?
\item Experiment B: Whether the block-based features are a well expression of the packet payload characteristics?
\item Experiment C: Whether the proposed detection model can extract the long-term dependency relationships in payload anomalies?
\item Experiment D: What is the influence of the hyper-parameters in the proposed framework?
\item Experiment E: How does the proposed model perform on other public datasets?
\end{itemize}

\subsubsection{Experiment A: Performance compared with other methods }
{In this experiment, we test the proposed framework on the CSIC 2010 dataset and compare the results with those of other methods. Five compared methods are involved in this experiment. Specifically, we use two classical machine learning methods and three methods recently released as the compared methods.  }
We use the scikit-learn library\cite{pedregosa2011scikit} to implement two traditional machine learning methods, support vector machine(SVM)\cite{chang2011libsvm} and random forest(RF)\cite{breiman2001random}, and test them on CSIC 2010 dataset. The other three methods include a RNN based method (Qin'18\cite{qin2018attentional}), a CNN based method (Zhang'17\cite{zhang2017a}) and a LSTM based method (Bochem'17\cite{bochem2017streamlined}). In the experiments, we simplify the http payload by ignoring the request header fields, which removes redundant information and reduces calculation complexity. The detection results of each method are listed in Figure \ref{ExperimentA}(a)\&(b), respectively.

\begin{figure}[htbp]
\centering
\subfigure[Detection Rates]{
\begin{minipage}[t]{0.85\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig1.png}
\end{minipage}
}
\subfigure[False Positive Rates]{
\begin{minipage}[t]{0.85\linewidth}
\centering
\includegraphics[width=\textwidth]{fig2.png}
\end{minipage}
}
\caption{The Detection Rates and False Positive Rates of the related works and our experiment.}
\label{ExperimentA}
\end{figure}

{As shown in Figure \ref{ExperimentA}(a)\&(b), the proposed framework and three deep learning based methods outperform the two classical machine learning based methods in DR, and the proposed framework achieves the highest DR of 99.12\%. The FPR of our proposed framework is 0.22, which is lower than those of all the compared methods. To the best of our knowledge, our model achieves state-of-the-art performance on the CSIC 2010 dataset.}

\subsubsection{Experiment B: Performance analysis of the block-based features }
{In this experiment, we compare the performance of three models with or without the adoption of the block-based features. We aim to investigate whether the block-based feature engineering method can improve the anomaly detection performance in the investigated models. The first model, LSTM-CNN based model, uses the proposed network structure described in Section 3.3. The other two models are constructed by only using the LSTM network or the CNN network described in Section 3.3. We call them as the LSTM based model and the CNN based model, respectively. The "BL" prefix of a model's name indicates the model uses the block-based features, otherwise the method uses the payload bytes stream. The CSIC 2010 dataset is used to test each model in this experiment.}
\begin{figure}[htbp]
\centering
\subfigure[Detection Rates]{
\begin{minipage}[t]{0.85\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig3.png}
\end{minipage}
}
\subfigure[False Positive Rates]{
\begin{minipage}[t]{0.85\linewidth}
\centering
\includegraphics[width=\textwidth]{fig4.png}
\end{minipage}
}
\caption{Detection Rates and False Positive Rates of three models with or without the adoption of the block-based features.}
\label{ExperimentB}
\end{figure}

{Our experiment results are shown in Figure \ref{ExperimentB}(a)\&(b). When the block-based features are not used, the CNN based model achieves DR of 84.35\% and FPR of 3.56\%, the LSTM based model achieves DR of 91.5\% and FPR of 4.76\%, and the LSTM-CNN based model achieves DR of 96.57\% and FPR of 0.72\%. When the block-based features are used, the BL-CNN based model achieves DR of 98.82\% and FPR of 0.15\%, the BL-LSTM based model achieves DR of 99.08\% and FPR of 0.44\%, and the proposed model achieves DR of 99.12\% and FPR of 0.22\%. These results show that when the block-based features extraction method is applied, the DR of each model increases 14.47\%, 7.58\%, 2.55\% respectively, while the FPR of each model decreases 3.42\%, 4.31\%, 0.5\%, respectively. }

{The above results demonstrate that the block-based features help these three models improve their detection performance on CSIC 2010 dataset. Moreover, the block-based feature extraction method could be easily combined with other anomaly detection methods and has the potential to improve their performance.}

\subsubsection{Experiment C: Performance analysis for the long-term dependency relationships in payload anomalies}
{In this experiment, we design a more challenging anomaly detection task, compared to the basic task in experiment A, to evaluate the proposed framework. The purpose of this task is to investigate whether the proposed framework has the capability of learning the long-term dependency relationships for packet payload anomalies.}

{In order to conduct the above task, we use a random-insertion method to change the sequential dependency relationships of anomalous bytes in samples of the CSIC 2010 dataset. In this method, each sample is inserted with a segment of noise, and the length of noise is 20\% of the sample length. The insertion index is random and the noise is all composed of character `0'. On one hand, after the random-insertion preprocessing, the noise is added into each sample which disrupts the original short-term dependency relationships of anomalous bytes. On the other hand, the same redundant information increases the similarity between each sample, which makes it more difficult to extract effective features and detect anomalies.}

{We calculate the performance metrics for the BL-CNN based model, the BL-LSTM model and the proposed framework. All the packet payloads are preprocessed by the random-insertion method introduced above. The experiment results are shown in Table \ref{tableExperimentC}, which indicate that the proposed framework still achieves an excellent detection performance. Compared with the results on the original CSIC 2010 dataset, the DR of the proposed framework only decreases 0.08\%, while the DR of the BL-CNN based model decreases 3.1\% and the BL-LSTM based model decreases 1.6\%. The proposed framework shows the ability to extract the long-term dependency relationships in the packet payload anomalies and still performs well on the task in this experiment. }
\begin{table}[ht]
\caption{Comparison for the results of three models in Experiment C.}
\centering
\scalebox{0.85}{
\begin{tabular}{cccccc}
\toprule
\textbf{Models} &\textbf{DR}& \textbf{FPR}& \textbf{Precision}& \textbf{$F_{1}$-score}  &\textbf{Accuracy}   \\
\midrule
BL-CNN &  95.72\% & 0.22\% & 99.34\% & 97.50\%&98.73\% \\
BL-LSTM &  97.47\% & 0.45\% & 98.68\% & 98.07\%&99.02\% \\
BL-LSTM-CNN &  \textbf{98.67\%} & \textbf{0.17\%} &\textbf{99.52}\% &\textbf{99.29\%} &\textbf{99.53\%}\\
\bottomrule
\end{tabular}}
\label{tableExperimentC}
\end{table}

\subsubsection{Experiment D: Influence of the hyper-parameters on the proposed framework}
{In this experiment, we attempt to evaluate the influence of four different hyper-parameters on the proposed framework. We evaluate one hyper-parameter each time and the other hyper-parameters are the same as those set in Experiment A. The experiment is also performed on the CSIC 2010 dataset.}
{The four parameters that we evaluate include the length of sliding block, the stride of sliding block, the number of high-frequency items in dictionary and the number of chosen LSTM hidden state. The length of sliding block is set to 1, 2, 3, 4 and 5, respectively. The block sliding length is set to 1, 2 and 3, respectively. For the evaluation of the number of high frequency items in dictionary, it is set to 5000, 10000, 15000 and 20000 each time. The number of chosen LSTM hidden state we tested includes 5, 20, 50 and 100.}

{The influence of the hyper-parameters on the proposed framework is shown in Figures \ref{ExperimentD1}\&\ref{ExperimentD2}. When the length of sliding block is too small, the information contained in the block-based features is limited. If it is too large, both normal and abnormal information might be mixed in the block-based features, which will cause poor performance. The stride of sliding block affects the amount of information extracted from the packet payload, which should be set to 1. The number of high-frequency items in dictionary affects the amount of information of block-based features, if the number is to large, the dictionary will involve too much redundant information. However, if the number is too small, the dictionary may not contain enough valuable information. The number of chosen LSTM hidden state affects the amount of sequential feature information used for the proposed framework. The experiment results indicate the model achieves its best detection performance when the number of chosen states is 50.}

\begin{figure}[b]
\centering
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig91.png}
\end{minipage}
}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig92.png}
\end{minipage}
}
\end{figure}
\addtocounter{figure}{-1}
\begin{figure}
\centering
\addtocounter{figure}{1}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig93.png}
\end{minipage}
}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth]{fig94.png}
\end{minipage}
}
\caption{The influence of model parameters on DR.}
\label{ExperimentD1}
\end{figure}
\begin{figure}
\centering
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig101.png}
\end{minipage}
}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig102.png}
\end{minipage}
}
\end{figure}
\addtocounter{figure}{-1}
\begin{figure}
\centering
\addtocounter{figure}{1}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth ]{fig103.png}
\end{minipage}
}
\subfigure[]{
\begin{minipage}{0.45\linewidth}
\centering
\includegraphics[width=\textwidth]{fig104.png}
\end{minipage}
}
\caption{The influence of model parameters on FPR.}
\label{ExperimentD2}
\end{figure}

\subsubsection{Experiment E: Performance evaluation on other public datasets}
{In this experiment, we evaluate the performance of the proposed framework on other public datasets. We use the subset of the public dataset for evaluation, which contains attacks related with the packet payload. For the CICIDS 2017 dataset, we use the Web attack data includes Brute Force, XSS, SQL injection in one day's record to set up the attack dataset, and use the normal HTTP traffic data in that day to conduct the normal dataset. For the ISCX 2012 dataset, we only use the Brute Force SSH attack data to form the attack dataset. The normal packet payload of SSH data in that day is used to form the normal dataset. }

{The detection results on the two datasets are shown in Table \ref{tableExperimentE}. Our proposed method achieves excellent performance on both two datasets. On the CICIDS 2017 dataset, our proposed model achieves a DR of 99.78\% and an FPR of 0.0165\%. On the other dataset, our proposed model achieves a DR of 99.17\% and an FPR of 0.332\%. }

\begin{table}[htbp]
\caption{Experimental results of proposed model on other datasets.}
\centering
\scalebox{0.85}{
\begin{tabular}{cccccc}
\toprule
\textbf{Datasets} &\textbf{DR}& \textbf{FPR}& \textbf{Precision}& \textbf{$F_{1}$-score}  &\textbf{Accuracy}   \\
\midrule
CICIDS 2017 &  99.78\% & 0.0165\% & 99.34\% & 99.56\%&99.92\% \\
ISCX 2012 &  99.17\% & 0.332\% & 98.68\% & 98.92\%&99.64\% \\
\bottomrule
\end{tabular}}
\label{tableExperimentE}
\end{table}

\section{Conclusion}
This paper proposed a payload-based anomaly detection framework to construct block-based features for efficient feature extraction and to adaptively detect anomalies. The block-based features are constructed by the former part of the proposed framework, which is a feature engineering method implemented via block sequence extraction and block embedding. The latter part of the proposed framework, i.e., the anomaly detection model, is designed to learn both the long-term and short-term dependency relationships in the block-based features and to discover potential attacks in the packet payload. Experiment results with three public datasets showed that the proposed framework could achieve a high detection rate and a low false positive rate compared with existing methods in the literature. In future work, we will consider other kinds of anomalies, e.g., anomalies in video surveillance\cite{luo2019video}, and try to explore a unified framework to detect them.

redpajama_set_name: RedPajamaArXiv


Be Sustainable, implement means of achieving a continuous improvement in energy management.
Supporting our energy awareness campaign.
Given the diverse operating context and stage of mine life at each operation, each site is advancing their energy efficiency programs at different stages. We are also working closely with our suppliers to find additional opportunities to reduce our energy intensity in both fuel and electricity.
Employee awareness is essential to improving energy efficiency. As part of the Kinross Way Forward, we launched an Energy Management campaign across our operations in 2013.


text:
caboose1
The Restoration and Preservation of
Norfolk and Western Railway Caboose 518554
Located in Warrenton at the 4th st. trailhead
Photo by Bill Lee
Caboose Dimensions
Overall Length 41' 8-3/4"
Overall Width 9' 10-1/2"
Overall Height 15' 6-1/4"
Weight 50,500lbs
Norfolk and Western Railway caboose #518554 was built in 1969 by the International Railway Car Company at Kenton, Ohio as one of 200 C-31 class cars built between the years 1968 and 1971. This caboose was delivered to the N & W Rwy. in the traditional blue body color with yellow lettering and was repainted in safety red when the railroad dropped the blue livery. The Caboose was refitted for pool service in the 1970's and the 3 bunks were removed at that time. The Pool service was designated by the white stripe around the cupola and the change of designation from C-31 to C-31P. This car was retired from active service in 1994 by the Norfolk Southern Rwy. (formed in the merger of the N & W and the Southern Rwys.) and stored in Roanoke, Va. awaiting disposition.
Caboose #518554 was donated to the Town of Warrenton, Va. in 1995 and moved to the quarry at the current end of track on the NS Warrenton Branch until a site could be prepared in Warrenton. The head of the Warrenton Branch Greenway hiking and biking trail, located behind the former Southern Rwy. Passenger Depot in Warrenton was chosen as the site to display the caboose. Site preparation included clearing of dense brush overgrowth, rebuilding the ballast roadbed and relaying of over 400 feet of track including a track switch using donated materials. The caboose was then moved into town over the highway on a truck trailer and placed on the newly laid rails with a crane. The interior and exterior of the car was completely restored to an in service appearance and completed in October 1998.
The caboose is opened for public tours on select weekends during the warmer months. For more information, visit The Fauquier County Parks & Recreation website. The Piedmont Railroaders are proud of their role in the site preparation, restoration, and on-going support of Caboose #518554 and the certificate received as shown below.
Return to Piedmont Railroaders home page
last updated October 2012
Copyright © 2002 Piedmont Railroaders
Site Maintained by Ken McIlvoy


text:
Magic Landing Amusement Park
64.6 miles from Dell City, TX
Magic Landing Amusement Park stood abandoned since 1989, and was demolished in 2012. The area was believed to be haunted by the ghost of an 18-year-old employee, Frank Guzman Jr., who was killed in 1985 while trying to recover a park guest's cap from a roller coaster track. As he ...
Alameda Avenue
Clint, Texas
At the intersection of Alameda Avenue and Leisure Lane, local legend warns, is the favorite haunting spot of a terrifying ghost. So his story goes, he was a pastor possessed by evil who brutally murdered his wife and two children by decapitating them, then killed himself. His family's heads were ...
Six Poles
Socorro, Texas
Rumors abound as to the purpose and the past of Six Poles. The most oft cited urban legend is that it was a slaughterhouse and that a woman accidentally killed herself there and now her ghosts haunts the spot.
Fabens Old Port of Entry
Tornillo, Texas
The Fabens Port of Entry border station at the Fabens-Caseta International Bridge between Texas and Mexico was built in 1938 and operated until the mid-1990s. It is scheduled to be replaced; the new Tornillo-Guadalupe Port of Entry station was begun in January of 2014. In the interim, operations are being ...
Fox Street Book Store
Carlsbad, New Mexico
This historic house was formerly a private residence, and is now occupied by a local book store. Visitors have reported disembodied footsteps, doors opening and closing of their own accord and the apparition of man in old-fashioned clothing standing on the main staircase. (Submitted by Callum Swift)
Carlsbad Police Department
This former hospital is rumoured to be haunted by a nurse who committed suicide, a choking infant and a nun dressed in grey. (Submitted by Callum Swift)
Carlsbad Public Library
Staff and patrons at this branch library complain of an entity that throws books and sets off alarms. (Submitted by Callum Swift)
Trinity Hotel and Restaurant
This 1892 hotel is allegedly haunted by a ghostly presence and a female spirit known as Ruby. (Submitted by Callum Swift)
This steak house is supposed to be haunted. Staff and diners have reported smoke that materialises from nowhere in the second floor dining room, while a construction worker complained of a pair of ghostly hands that would rest upon his shoulders while he worked in the basement. (Submitted by Callum ...
Ghosts of North Texas
Mitchel Whitington
Spirits of Dallas: The Haunting of the Big D
Brian Righi
Haunted Fort Worth (TX)
Rita Cook
Cavern Theatre
66 miles from Dell City, TX
This historic theatre is no longer in operation, but visitors claim it is haunted. Reports indicate that strange noises have been heard in the main room, and that a shadowy apparition has been seen standing in the projectionist booth. (Submitted by Callum Swift)
Cristo Rey Catholic Church
Cristo Rey Catholic Church is only open to the public during Holy Week, as it is part of a cloistered convent. Its ghost in residence is said to be a kindly nun in traditional habit, seen praying at the altar communion rail. If people approach her, she gradually fades away.
Kennedy Brothers Memorial Apartments
Kennedy Brothers Memorial Apartments are rumored to be built on top of an old cemetery. Now, folks say they are haunted. Witnesses have reported disembodied footsteps, the sound of crying, and a ghost girl. The sound of someone playing basketball has been heard in the gym when no one was ...
El Paso Community College
At the photo lab at El Paso Community College, strange things may be afoot. Students have seen the revolving door open by itself and felt the touch of an unseen hand while inside. Also, some say there is an overwhelming feeling of a presence watching you, and sometimes a feeling ...
La Hacienda Airport Travelodge
The Texas Hauntings society lists this as a haunted hotel, primarily due to the bar inside called the "Las Trancas Lounge." It is said there is a female spirit who is often heard laughing.
Hideaway Mobile Food
Fort Bliss, Texas
Things began to happen after our boss passed away in early 2010. The ice scoop that sits on the ice machine would fall off the machine at different times. We have a paper towel machine that you have to wave your hand to get paper towels. ...
Fort Bliss is rumored to be a ghost-hunter's delight with apparitions that appear in photographs, footsteps, unexplained noises, lights that go on and off, toilets that flush by themselves, and voices that come from nowhere. Ghostly soldiers have been seen by many, notably in or around Building 4, once used ...
Loretto Academy
Rumor has it that the bell tower is haunted by the ghost of a nun who lost her life in the bell tower. While there are many variations of the story - accidental death, suicide over pregnancy, murdered through being locked away and starved - the particulars of the ...
The ghost boy at Evergreen Cemetery is said to stand on the sidewalk and ask passersby for a ride in the wee hours of the morning. Also, a mist and a strong smell are said to materialize near the train tracks behind the graveyard.
Concordia Cemetery
At Concordia Cemetery, more than 60,000 folks have been laid to rest, and many lie in UN-rest, according to reports. It's said that the cemetery is so active with apparitions and other strange occurrences that the L&J Café across the street even gets some "business" from the cemetery's ghosts.
Haunted Texas Highways
Haunted History of Old San Antonio
Lauren M. Swartz
Ghosts Along the Texas Coast
Docia Schultz Williams
Monteleone's Ristorante
Featured on SyFy Channel's Paranormal Witness, Monteleone's Ristorante is reported to have lost customers because of the strange things that happened there. Just to name a few: Pans of lasagna flew off tables, an apparition of a charred arm appeared coming through a door, and unexplained pounding was heard at ...
Transmountain Road
The ghost of an old monk and his donkey are said to walk along this road.
Fire Station No. 9, say the firefighters who work here, has a ghost: Woodard Bloxom, a former captain who died battling a warehouse fire across the street in 1934. The ghost is said to be friendly, and may forewarn the firefighters about impending calls. The men have witnessed cold spots, ...
Old Southwestern General Hospital
At Old Southwestern General Hospital, there is said to be a ghost nurse who changes bedsheets and walks the halls in the hospital's empty fourth floor. Eerie activity has been reported here since 1936, when a psychic claimed to have sensed the spirits of a woman and of a man ...
Albert Fall Mansion
The 1907 mansion is haunted by a phantom figure seen pacing the upstairs balcony. The spectre has also been standing at the windows, staring down into the street. (Submitted by Callum Swift)
Magoffin Homestead
The historic Magoffin Home was originally built by El Paso mayor Joseph Magoffin in 1875. His family lived there until 1976, when the City of El Paso and the State of Texas bought it--and apparently also bought the ghosts that came with it. One ghost is believed to be Charles ...
Toltec Building
The landmark Toltec Building, completed in 1910, is rumored to be haunted. Over the years, witnesses have described such things as stage lights exploding or changing color, props falling, and apparitions, including one of Pancho Villa.
Chase Tower
Chase Tower, formerly the El Paso National Bank Tower and the Texas Commerce Bank Tower, is the site of many law and bank offices. Red lights and a ghostly woman in a red dress have been seen on the upper floor.
At the Sacred Heart Catholic Church, a ghostly woman in a turn-of-the-century bridal gown is said to weep in the side chapel. According to local lore, the young Mexican woman was left at the altar and shortly thereafter passed away. Her ghost is said to return to the church, hoping ...
Plaza Theatre
The 1930 Plaza Theatre, originally a movie house, has several ghosts, according to witnesses. Lights turn on and off, and an eerie red-orange light has been seen traveling through the mezzanine. Some of the apparitions here include a shadowy man in black and an elderly man smoking a cigarette. Also ...
Best Tales of Texas Ghosts
Haunted Texas Vacations: The Complete Ghostly Guide
Lisa Farwell
Volume 3: Ghost Stories from Texas: Joe Kwon's True Ghost Stories from Around the World
Joe Kwon Inc
Rumor has it that the Camino Real is a haunted hotel. A ghostly woman is said to wander the halls wearing a white gown and a melancholy expression.
El Paso Museum of Art
El Paso Museum of Art is believed to be haunted by the spirit of an older woman, whose apparition has been seen looking at folks from an upper window. Witnesses also claim that the lights go on and off and doors open and close by themselves. Moaning sounds have been ...
Fire Station No. 11 is said to have a haunted bed that the firefighters refuse to sleep in. Men who have used that bed in the past say the bed shakes, the blankets are tugged at, and their sleep is ruined by unexplained coughing fits.
Insights El Paso Science Museum
Insights El Paso Science Museum is no more; according to reports, it was demolished in 2013. It was said to be haunted by the Native American spirits, including a chief, and a ghostly woman dressed in white.
The Lodge Resort & Spa
Cloudcroft, New Mexico
The ghost of a red-haired woman is said to haunt the dining room as well as the ghost of a former maid who was allegedly murdered there.
Union Depot
With more than 100 years of service, it's small wonder that this historic train station would have some occurances. Since opening as the Texas Tech School of Architecture, El Paso in 2012, fellow students have reported several strange occurances on the upper floor, including moving chairs, doors creaking, ...
University of Texas El Paso
University of Texas El Paso, aka UTEP, has more than its share of haunted buildings. Folks say that at Old Main, Cotton Memorial, the Geology Building, Hudspeth Hall and Worrell Hall, there are sounds of footsteps, trash cans that open by themselves and flickering lights. And on the library's fifth ...
Gravity Hill - Thunderbird Drive
Gravity hills are interesting natural phenomena that are nature's practical jokes on the gullible. Gravity hills are what happen when a common optical illusion that causes a slight downward slope to appear to be an uphill slope tricks people into thinking that their car is magically going uphill,when in fact, ...
Seamon Hall
One ghost story at Seamon Hall on the University of Texas at El Paso campus tells of a ghostly janitor who passed away after stashing his money somewhere in the building. Another tells of a female student who was murdered by her boyfriend at a tea party held by the ...
Monte Vista Cemetery
A woman in white is rumored to haunt the cemetery grounds.
Ghosts of Fort Worth: Investigating Cowtown's Most Haunted Locations
Haunted Texas: A Travel Guide
When Darkness Falls: Tales of San Antonio Ghosts and Hauntings
Docia Shultz Williams
Otero County Court House
The office location previously served as a sheriff's office and a cellblock before being converted to offices. There are reported hauntings in the corridor's of the building, where the ghost of a young man dressed in an early 20th century suit has been seen. Doors slam and footsteps are heard ...
La Luz Water Treatment Plant
La Luz, New Mexico
An unknown entity haunts the treatment plant. Eerie feelings and hot and cold spots make up the majority of the claims.
La Luz Canyon
Reportedly haunted by the ghosts of children who died when the car they were riding in crashed.
Hanger on Hangerlake Road
Las Cruces, New Mexico
Teenagers attempt to scare the gullible with rumors of "demonic activity" and "satanic rituals" that took place at the hanger. However, there is no evidence of this, and no one over the age of 13 actually believes it. People also claim that someone was murdered in the hanger and their ...
Hale Lake
Ruidoso Downs, New Mexico
This is reported to be one of those places where simply driving there at night will cause all sorts of mechanical havoc with your car. While these reports probably came about through young men trying to con gullible girls - "no honey, I swear my car just died, it ...
Southeast New Mexico Historical Society
107.9 miles from Dell City, TX
The historical museum features war paraphernalia and other such exhibits from the past, and is rumoured to be haunted. The apparition of a young soldier who was taken as a prisoner of war has been seen waving and smiling to guests in a room on the second floor. A ...
Pueblo Auditorium
108 miles from Dell City, TX
There are rumors of a boy who haunts the upper balcony.
Chaves County Courthouse
The sounds of young children playing are often heard in the basement.
Pecan Fields of Roswell
Reports of a young, crying boy who runs across the street at night.
If you go to most ghost websites out there, you will find some variation of this hogwash: The NMMI does not use the word "juliet" for the letter "J" in their phonetic alphabet because they believe it is badluck. The NATO phonetic alphabet is fairly standard for any military ...
Haunted Austin (TX): History and Hauntings in the Capital City
Jeanine Marie Plumer
Ghosthunting Texas
April Slaughter
Transparent Tales: An Attic Full of Texas Ghosts
Allen Turner
» Cemeteries near Dell City, TX
» Find museums in Dell City, TX


text:
AboutKOHC2022-09-27T15:35:23-04:00
Improve the oral health of all people of Kentucky
Kentucky's Vision of Impact is to engage partners across the state to create a collaborative oral health network designed to:
Educate parents
Activate policymakers
Inspire and engage professionals to create optimal oral health for all.
In 1990, a collaborative effort formed between the University of Louisville School of Dentistry and the University of Kentucky College of Dentistry known as the Kentucky Oral Health Consortium. The Consortium, eventually renamed the Kentucky Dental Health Coalition, was involved in numerous oral health promotion projects such as the development of the Kentucky Smile Curriculum which is still used by Kentucky school teachers, active legislative advocacy, the National Governor's Association Policy Academy on Children's Oral Health (2001), the Kentucky Oral Health Surveys (for children, adults, elders 2001-2003) and many more.
While the Coalition became inactive in 2006, a steering committee of diverse stakeholders came together in 2010 to plan for the reinvigoration of the Coalition. Now called the Kentucky Oral Health Coalition (KOHC), the newly revitalized group is staffed by Kentucky Youth Advocates. The first coalition meeting held in March 2012 had over 70 in attendance. The group is moving forward to tackle key priorities and make a true impact in Kentucky.
Coalition Priorities Through 2025
Improve oral health awareness
Increase access to oral health care
Chair of KOHC: Dr. Stephanie Poynter
Dr. Stephanie Poynter is a general dentist who graduated from the University of Louisville School of Dentistry in 1998. Dr. Poynter completed a one-year Advanced Education in General Dentistry residency at the University of Louisville in 1999. She has worked in public health for almost 18 years. She is the dental director at Family Health Centers, Inc, a federally qualified community health center with 3 dental offices in Louisville serving homeless, immigrant, and urban populations. Dr. Poynter is committed to improving access to dental services for all patients. She is from Louisville but enjoys water skiing in the summers in Taylor County with her husband and son.
Jennifer Hasch, BS, RDH
Jennifer Hasch is a graduate of the University of Louisville School of Dentistry, Dental Hygiene Program. As a student, she became passionate about access to care and public health dentistry within Kentucky. She has participated in foreign dental missions and recognizes the global similarities to Kentucky's poorest populations. Jennifer is currently working as the Dental Director for Shawnee Christian Healthcare Center where she leads their fixed location and a school based dental program in 10 JCPS schools. She is a graduate student studying Public Health and Non-Profit Management at Western Kentucky University. She believes oral health outcomes cannot be improved without an equity and social justice framework. Born and raised in Louisville, she is a second-generation University of Louisville graduate and a Cardinal fan through and through.
Dr. Julie Watts McKee, State Dental Director
Dr. Julie Watts McKee received a degree in biology from the University of Kentucky, and a dental degree from the University of Louisville in 1983. She practiced general dentistry for 10 years in Mount Sterling, Kentucky, worked in state government, focusing on health care policy and regulation, and managed a district health department north of Lexington for 12 years. She has held the position of State Dental Director since 2007.
Featured projects Dr. McKee has worked on include establishing Fluoride Varnish Programs in health departments, overseeing the General Dentists Training in Pediatric technique, working with funded local oral health coalitions.
She worked to develop a new licensure category, Public Health Dental Hygienist, and the seven programs implemented because of this practice change.
Dr. McKee facilitated the development of the Strategic Plan for Oral Health and led a multidisciplinary group to develop and distribute Kentucky's Plan for Dentists to Return to Work during the COVID-19 pandemic.
She was awarded the Presidential Award of the Kentucky Dental Association in Fall 2020.
Being a graduate of both basketball powerhouses, she cheers for both UK and UofL in sports, but when they play each other, she wears purple!
Leah Smothers, RDH
Leah has been a Registered Dental Hygienist for over 15 years. She is currently employed as a Public Health Registered Dental Hygienist at the Purchase District Health Department. For the past two years, she has managed their Floss and Gloss portable Dental Program. The Floss and Gloss program provides preventative dental services in the school setting for grades K-12.
Leah establishes, integrates, and promotes a mutual vision for the Floss and Gloss Program through collaboration with her Floss and Gloss teammates, Purchase District Health Department staff, FRYSC coordinators, and school nurses. Leah is striving to incorporate teledentistry into the program with the help of Mouthwatch Teledent Software and equipment.
Leah has also been a CE provider to dental professionals, nurses, and FRYSC's, including speaking at the ADHA 2020 National Convention. She has served in her local component as the CE Coordinator, Trustee, and President. Leah also served as the voting delegate for her component at KYDHA.
Dr. Pam Stein VanArsdall, DMD
Dr. Pam VanArsdall is a professor at the University of Kentucky College of Dentistry and the Education Director of the grant Eradicate Oral Cancer in Eastern Kentucky. Following graduation from the University of Kentucky College of Dentistry in 1990, she practiced dentistry in Carlisle and Mt. Olivet, Kentucky. In 2003, she became a full-time faculty member at the University of Kentucky College of Medicine teaching and conducting research. After earning her master's in public health, Dr. VanArsdall joined the faculty of the University of Kentucky College of Dentistry in the Division of Public Health. She is the past Academic Dean of the University of Kentucky College of Dentistry. Dr. VanArsdall's teaching, research and service have focused on health behaviors and prevention. She has published funded research in the Journal of the American Dental Association, the Journal of Dental Research, and the Journal of the Alzheimer's Association. She has a holistic approach to health. She is a certified health and wellness coach and trained in professional plant based culinary arts.
Dr. Justin Whitney, DMD
Dr. Justin Whitney is Assistant Professor of Comprehensive Dentistry at the University of Louisville School of Dentistry. Following graduation from the University of Kentucky College of Dentistry in 2011, he served as a dentist for a Federally Qualified Health Center in San Antonio, TX. During his five years in Texas, he held positions of staff dentist, interim dental director, and Chief Dental Officer for the second largest health center in Texas. Having a deep love for his home state, Dr. Whitney returned home to live in his wife's hometown of Louisville while practicing in southern Indiana for nearly three years. He was then offered the opportunity to transition into academics, having an intriguing background of public service and private practice. A native of Greenville, KY, he now resides in Crestwood, KY with his wife and three children. Dr. Whitney considers it his mission to help those in need and to educate the next generation of dentists.
Our Networks : National Oral Health Resources


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redpajama_set_name: RedPajamaGithub


text:
Q: Add FTP user with all directories access (like root) with Virtualmin / Webmin It has been 2 hours that I' trying to allow a FTP user to have grant access to all directories.
I've add a FTP user with Webmin. I'm able to connect with a FTP client but when I'm trying to change directory to a virtual site (in /home/*), I have
    550 ****: No such file or directory

I just want this new user to have the same right as root user. (for ftp directories access).
Does someone can help me ?
Thanks you in advance !

A: webmin has restrictions on creating ftp users with root permissions, there is a setting somewhere i think to disable this though, reply if you are still interested.

redpajama_set_name: RedPajamaStackExchange


Housing is recovering. Now what about the rest of the economy?
Last September, Lehman Brothers filed the biggest bankruptcy case in US history. Now, an upcoming decision by a US bankruptcy judge could set a precedent for how entities are paid when one of the parties in a collateralized debt obligation (CDO) becomes insolvent. The case pits Lehman against Bank of New York Mellon [stock BK][/stock] UK subsidiary BNY Corporate Trustee Services, acting on behalf of Australian asset manager Perpetual Investments, who entered into a credit default swap with Lehman.
Hey, buddy, can you spare a billion? I know, I know -- bad joke. But last week, Freddie Mac asked the bankruptcy judge handling Lehman's bankruptcy to investigate $1.2 billion in claims it says Lehman hasn't yet paid. That's not a small chunk of change, for one thing. From Reuters, who reported on the story: Freddie Mac, which was bailed out by the U.S. government last month, said it is missing $1.2 billion from two fund transactions conducted in August, according to court documents.
Richard S. Fuld Jr., in his first public appearance since Lehman Brothers Holdings Inc. collapsed in September, said in a sworn testimony before a Congressional panel on Monday that while he took full responsibility for the failure of the company he led, he still believed all his decisions "were both prudent and appropriate" at the time.
Henry Waxman (D-CA), chairman of the House Committee on Oversight and Government Reform, announced late last week that his committee will hold five hearings in October on the financial meltdown, signaling the start of a Congressional witch hunt to determine who is most to blame for the mortgage and financial ills that now threaten the broader U.S. economy.
So Lehman is down for the count. The stock market is tanking to the tune of a 500 point drop in the Dow Jones Industrials, when this story was published. But those in the mortgage industry are wondering about the fate of a different company: Littleton, Colo.-based Aurora Loan Services, a Lehman subsidiary and lender/servicer that faces an increasingly uncertain future as its corporate parent enters restructuring.
Looking to cut off speculation that any sale of floundering Lehman Brothers Holdings Corp. [stock LEH][/stock] might need to involve federal dollars, Treasury secretary Henry Paulson on Friday was reportedly adamant that any effort to sell the Wall Street investment bank not use government funds.
Lehman Brothers [stock LEH][/stock] said Wednesday morning that it expects to lose $3.9 billion in the third quarter, a loss of $5.92 per share, the largest quarterly loss in company history. Driving the loss are $5.6 billion in net write-downs on residential mortgage and commercial real estate positions, the company said in a press statement. That's the bad news.


text:
Vito Mannone
A Safe Pair of Hands
Vito Mannone Biography
Vito Mannone Photos
Vito Mannone Videos
Vito Mannone Expressed Happiness after Joining Monaco
Posted on October 27, 2020 by index
It is quite natural for professional players to change club teams. There are some players who are demanded by most of the clubs and the clubs also make different types of contracts to those players so that they can play for the clubs for a long time. Vito Mannone is a professional footballer of Italy and plays as the last guard of the goal. He played for many famous clubs like Arsenal, Sunderland Reading, and so on. For his national team also he played in the U21 stage.
This 32 years old goalkeeper has now joined Monaco which is one of the finest clubs in Italy. Vito Mannone joined this club from Minnesota United (Major League Soccer) and Esbjerg FB (Danish D1), and he looks very happy to join there. Since he formerly played for Sunderland and Hull City, he is confident to cope with the new conditions and the players of the club. This promising goalkeeper was spotted by the legend Arsen Wanger and since then he has stood tall in all the expectations from him. He also played in the Champions League as well as the British Premier League. Monaco is in great hopes to utilize his talent and experience for their team.
The club officials also expressed their excitement in the press release and bid him a warm welcome to make his arrival a grand one. Monaco has already signed Kevin Volland and after that, the signing of Vito will make their team stronger. The inclusion of these players will balance the team with experience and youthful energy. The sporting Director of Monaco has said that he witnessed the high-level performance of Vito and is expecting him to perform for the club as well. He is very confident about the performance of Vito.
← MANNONE DIDN'T SHARE GOOD RELATIONSHIP WITH WENGER
MANNONE DETERMINED TO MAXIMISE MONACO CAREER →
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text:
Qassem Soleimani assassination: Trump is playing with fire to save his presidency
January 4, 2020	World News
Middle East Eye
By: Nader Entessar, Kaveh Afrasiabi
The US assassination of Qassem Soleimani, Iran's top commander, and a number of Iraqi militia leaders, on Friday, represents a significant escalation tantamount to an act of war with grave long-term consequences.
Iran's volcanic anger will inevitably explode into retaliatory action, as indicated by the statement of the country's supreme leader, and will undoubtedly target US forces in Iraq.
Viewed from Tehran, this is American rogue behaviour run amok, a blatant act of war with the full blown arrogance of a declining superpower willing to cross any red line, transgress any rule of law, to achieve its hegemonic ends.
When I voted against the war in Iraq in 2002, I feared it would lead to greater destabilization of the region. That fear unfortunately turned out to be true.
The U.S. has lost approximately 4,500 brave troops, tens of thousands have been wounded, and we've spent trillions.
— Bernie Sanders (@BernieSanders) January 3, 2020
The timing of the operation is of essence.
For the Trump administration, 2019 ended on a sour note, with the double whammy of an impeachment-induced domestic political crisis and a combustible, dangerous escalation in Iraq that could, in light of this latest development, easily turn 2020 into another year of US war in the Middle East.
With the seeds of such a war firmly planted by unbounded Iranophobia, which is reflected in the unilateral US warfare waged under the rubric of a "maximum-pressure strategy", its slow but steady manifestation in the Iraqi theatre of conflict has become an inescapable reality.
Locked in a zero-sum competition for influence in Iraq, Washington and Tehran are now on a collision course and, unless prudent diplomacy prevails, a highly unlikely possibility in light of Soleimani's assassination, the current situation will likely trigger a new military dimension in Trump's anti-Iran strategy.
Flagrant violation of sovereignty
Some context to this week's events is useful here.
Coinciding with a joint Iran-Russia-China naval exercise in the Gulf of Oman as a clear indication of Beijing's and Moscow's antipathy towards Trump's anti-Iran policy, last week's US air strikes on pro-Iran militias in Iraq and Syria have been portrayed as "precision defensive" strikes by the US military in response to the growing threat of pro-Iran forces in the region.
Yet, it is abundantly obvious that these strikes also have geostrategic connotations, in light of Secretary of State Mike Pompeo's outreach to leaders in Israel, the UAE and Saudi Arabia immediately after launching the strikes, which were denounced by Iraq and Syria as a flagrant violation of their sovereignty.
With Saudi and UAE officials warming to the notion of a thaw in relations with Iran, Pompeo's intention is clearly to neutralise that prospect, which runs contrary to US hegemonic interests in the region. The dependency of Gulf Arab states on the US means they are mortgaged to a sustained rivalry with Iran, which is pushing its own peace card in the region while simultaneously upping the ante against US dominance.
Media propaganda
Thus, the entire mainstream US media has lined up behind Trump, crediting him with prudent use of force and patting him on the shoulder for putting aside his earlier reservations over use of force. But little of that US media propaganda for a warmongering US policy – which stands in direct violation of international law – washes with the local population in the region, as even the New York Times has admitted that Iraqis across the political spectrum were united in condemnation of the US attacks, which killed and wounded dozens of Iraqis.
As a result, the tables have turned on the US, and no matter how many tweets Trump sends to the Iraqi people exhorting them to direct their anger against Iran, the die has been cast – and the new level of anti-Americanism seen in Iraq today is unlikely to recede anytime soon.
Already, the powerful Sadr faction has called on other Shia political factions to unite to expel the US from Iraqi soil.
As the situation escalates with the killing of both Soleimani and the deputy head of the Popular Mobilisation Units (PMU), it is fair to bet that the US has miscalculated the ramifications of its military action, which has elicited the promise of revenge and retaliation from the Iraqi PMU. The pro-Iran militia will likely freeze or reverse their integration process with the Iraqi armed forces, particularly if they feel Baghdad's reaction to Trump's transgression of Iraq's sovereignty is insufficient.
Right now you have a lot of Americans across the region (as well as US allies) who are feeling pretty vulnerable. Iran's retaliation will probably be carefully calculated rather than all out, but waiting for it (and any wider fallout) will be nerve-wracking.
— arabist (@arabist) January 3, 2020
The new blow to Washington-Baghdad ties caused by this latest dramatic developments also goes against US national security interests, even though it may serve Trump's personal interests vis-a-vis impeachment.
In essence, this reflects a bifurcation of interests, whereby Trump may end up inflicting severe damage to US interests abroad by creating an artificial crisis to save his own presidency, wrapping himself in the flag and tapping into the hidden reservoir of American jingoism to neutralise the attrition facing his presidency.
Another Middle East war?
It is difficult to overestimate the grave American error in perpetrating this state-sponsored act of terror that will most likely trigger a deadly cycle of violence exacting many lives.
Nonetheless, it is in the US's interests to prevent the deepening of the Iraq crisis that it has willfully generated, and yet lacks full control to contain.
Essentially, an outgrowth of the US-Iran crisis has played out inside Iraq, which could escalate well beyond "shadow wars" and the use of proxies, with unpredictable twists that present a clear risk to the thousands of US military personnel in the region – not to mention the health of the global economy, which is tied to the free flow of oil through the Strait of Hormuz.
Playing with fire to shore up his flagging presidency, undermined by the sting of impeachment, Trump is now showing all the symptoms of a trigger-happy president apt to cause another calamitous Middle East war, thus betraying his pledge to avoid getting the US entangled in another conflict.
Sadly, as his misguided policies with respect to both Iran and Iraq clearly show, Trump is not only repeating the impeachment ploy of his predecessor, but he is also on the verge of recycling the errors of the Bush-era wars in the Middle East.
The views expressed in this article belong to the author and do not necessarily reflect our editorial policy.
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