The histories of an iconoclastic institution aren't easy to trace. The New School has always been diffuse, porous and underfunded. Until recently, The New School didn't even have an archive. So the fifty-seven volumes of publicity scrapbooks maintained by a clipping service for the school's first several decades — and now available through the digital archives — are a precious trove. They provide real time reflections of what the always outward-facing New School was up to — and was seen to be up to. Every page reveals something unexpected.
Consider an example. "Exiled Knowledge Salvaged for World Use: Europe's Scholars Find Opportunity to Work and Serve" offers insight on many levels. The date and full name of the newspaper where this article appeared have been lost, but the article seems to be from the Wall Street Journal. It was published in 1943, ten years after the New School established the University in Exile — through which it helped rescue nearly two hundred scholars endangered by the rise of fascism in Europe and reshaped academic life in the United States.
The photograph shows Hans Staudinger, one of the original eleven refugee scholars brought to the New School in 1933. The caption emphasizes that Staudinger was not a scholar but "once an important Prussian utilities executive" who served in the Weimar government. "Before he escaped to America he was badly beaten by storm troopers," it concludes. With many of his colleagues, the article reports, Staudinger had assumed American citizenship; they "never expect to return to Europe, unless they are needed by their adopted country for special missions." Such reassurances were needed to quell suspicions about the loyalties of the New School, especially during the war. But Alvin Johnson, founder of the University in Exile, earnestly believed that world knowledge and scholarship had found a new home in America. Johnson saw the New School as a fulcrum of a new Renaissance, as when refugee scholars from the fall of Constantinople had found new lives in Italy.
The caption tells us further that Staudinger is in "a New School classroom," but this was not quite true. In the 1940s the Benton room was not, in fact, used much as a classroom but instead for prominent events and meetings. Newspaper readers may indeed have recognized the room, for this dramatically muralled space — excitingly unlike anyone's picture of what a school should look like — appeared in many stories about the New School. The mural itself is part of Thomas Hart Benton's "America Today," the more celebrated of two mural-wrapped rooms that could be found in the 66 West 12th Street building the New School opened in 1931. Benton's murals left the New School in 1982 for reasons of preservation — and money — and have slipped from New School memory. Today they are on display in a dedicated room at the Metropolitan Museum of Art.
The juxtaposition of the German Staudinger (who was not Jewish, although his wife was) and Benton's America isn't accidental. The very same image appeared in other publications, too, where it is clear that it is a composite of two photographs — in an image in the Los Angeles Times, you can see the stitching. It is a publicity image with a clear and exciting message. It tells of European scholars in a fully American scene, confronting specifically American legacies and challenges.
Realize that the photograph was composed and you can recognize other parts of the image as intentional, too. Between Staudinger and the camera sit three students at a table, a representation of the intimate discussion-based pedagogy which had, from the start, been a hallmark of the New School.
Two of the three students are women. If we don't remark on this today, readers in the 1940s certainly would have likely noted it. As an institution which generally wasn't awarding degrees, the New School had little reason to keep track of who its student were. But a self-study conducted in 1952 found that women had composed a steady two-thirds of the student body for the school's first three decades. (The proportion was inverted in the graduate programs.) New School promotional publications from 1943 show women predominating, too. The presence of women was part of the nature of the place and was shown as such in its own promotional material.
Like the Benton room, the New School's long-standing female identity has been largely forgotten. The shortlist of "founders" changes with time but is always only men. Some note the novelty of the proposal for an "Independent School of Social Science for Men and Women" in our earliest document, and that "women in the modern social order" is listed among the "pressing problems" that called for a new kind of school. Yet few notice that more than half of the founding board were women — women who were decisive in shaping the distinctive structure and ethos of the school. That the school was effectively run for four decades by another woman, Clara Mayer, is even less known.
The image attending "Exiled Knowledge Salvaged for World Use" invites us to a school we've forgotten we are. A key node in an international community of knowledge, the New School was rooted in American traditions as it addressed American and global problems. Energized by the power of modern art to illuminate social change, it was a cosmopolitan community of engaged teachers and adult students, most of them women. What would it mean to recover this memory?
This Public Seminar vertical is a place within which a fuller portrait of the histories of the New School can unfold. It will defamiliarize the school, as the best history always does. It will also make the New School's complicated and often conflicting legacies available in a new way for thinking about the construction of knowledge, the aims of education, and institutionalization of higher education.
Issues in the representation and self-understanding of the history of institutions — like the persistent effacement of women from New School narratives — have broader resonance, too, especially for communities that risk making an idol of the new. The new, to put it a little broadly, is always a proposed future remedy for a perceived past infelicity. As successive contributions to this vertical will show, every moment of novelty, whether visionary or trivial, has something like the stitched-together structure of this image of "exiled knowledge salvaged for world use." The Benton mural in the background and the women and men in the foreground establish the precarious space where the new can be truly transformative.
Can There Be Dignity In A Vast Majority?


Bradford Academy has a large and growing comprehensive Sixth Form which is committed to ensuring all students fulfil their academic potential, embrace the opportunities offered beyond the curriculum and are thoroughly prepared for their future.
We offer a wide range of courses from Level 1 to Level 3 to meet students' individual requirements.
We have a wide range of enrichment activities and leadership opportunities available including Extended Project Qualification, Sign Language, Duke of Edinburgh, Climbing Wall and a variety of sports.


When considering the Internet providers in Tallahassee, there are multiple of ways to determine the cheapest Internet bundle options. First, it is best to determine what bundle might be the most functional for you and your family. Next, it is important to decide upon your Internet speed and connection type. Then, you can also research bundling options in your area to magnify your savings.
A very common question that members of the Tallahassee community will want the answer to is "how much broadband speed do I require"? To understand this answer it is most important to know how much Internet you will need. Once you've understood this, you can evaluate different Internet service providers in Tallahassee. If your household consists of those that cruise the web and check email, then you will assuredly be fine with 25 Mbps. However, if you are an avid gamer or enjoy streaming videos on a regular basis, then you may want more than 50 Mbps.
If you are shopping for an Internet plan in the city of Tallahassee, Florida, an important factor when choosing your Internet service provider is the amount of megabits per second your plan will provide. Megabits per second is the speed at which your Internet will download or upload a file, video, movie, or game. Depending upon the Megabyte size of the file, it can take longer to download. This is why plans with at least 25 Mbps of download speed are desirable as they will ensure that you can download videos, movies, and play online games without slow downs in your Internet speed.
Sometimes, your Internet connection may experience a slowdown that doesn't make any sense because you and your Tallahassee family have taken precautions to prevent it from happening. You're preventing too many devices from being connected at once and even believe you have an Internet plan with enough Mbps to provide fast connections for your entire family. However, you might not be aware that your Internet service provider may be throttling or slowing down your WiFi signal because it's a peak time of day or night. During throttling, your connection will not perform the way it typically does and there might be nothing you can do about it other than choosing another service provider, that is.
When you're in the bustling city of Tallahassee, you want the quality and reliable service you need to stay connected. With the nation's best Internet service providers, you can stream video, upload files, download music, and more all at the super fast speeds you need to stay connected.
Did you know you can get kicked from some online games for having too high of a ping rate? If your ping is above 100 m/s, you're probably seeing some non-optimized games. With our Internet service providers, you ensure you're always ready to game when you want to be!
Depending on where you live in comparison to Tallahassee, you may find that your Internet speeds aren't sufficient enough for you to accomplish everything you need. It might be that you don't have a quick-enough Internet connection for your family and it's time to switch to a new provider. It is best to calculate how much Internet speed you need by determining how many devices you will have connected to the Internet at once, how many people will be using the Internet at once, and how many expansive items will be downloaded or operated at the same time.


An Ottawa lawyer has made an emotional apology to the Law Society of Upper Canada for "foolishly" posting Crown disclosure from a client's criminal case on the Internet.
"This has been a terrible mistake," David Ian Anber told a three-person disciplinary panel at a law society hearing in Toronto on June 25.
"It was wrong because the Crown attorney trusted me to keep my undertaking," he said, his voice breaking.
Anber, a 33-year-old lawyer who focuses on vehicular offences, admitted to committing professional misconduct.
The disciplinary panel, chaired by Lyle Kanee, found Anber had breached his client's confidentiality and his undertaking to the Crown to keep the disclosure "strictly confidential" and had failed to maintain the integrity of the profession.
Anber admitted that on Nov. 25, 2012, he "mistakenly and foolishly" posted Crown disclosure in PDF format for his client's fraud case on freelancer.com, an Internet site where people can bid on jobs offered by individuals.
According to an agreed statement of facts, Anber asked on his posting if anyone could provide a program to remove "black boxes redactions" from documents.
"As a lawyer, I frequently get pdf documents that have been redacted by the other party which should not be redacted," he wrote.
He posted disclosure in his client Kimberley Billings' fraud case as a sample of the kind of material he wanted unredacted.
It included her address as well as photocopies of her birth certificate and bank card; the Crown synopsis of her charges; witness statements; loan application forms filled out by Billings identifying her phone number and income; a preauthorized debit form that included her bank account number; payroll verification from the Ottawa Hospital; and a copy of her online bank account statement.
An Australian freelancer saw Anber's online notice and, in a confidential message to the poster, warned that the PDF contained private information. He also contacted the Law Society of Upper Canada and Ottawa police.
An Ottawa police officer involved in the Billings case, Mitch Proteau, devised a sting operation. Posing as a Florida freelancer, Proteau called Anber and offered to sell him a program for $500 that would remove redactions from such files. Anber expressed interest.
Proteau also notified the Ottawa Crown's office. It made an urgent application in the Superior Court to have Anber remove any electronic criminal disclosure in his possession from any web site and return it to the Crown.
Anber retained counsel and immediately reported himself to the law society.
In a hearing before Superior Court Justice Robert Maranger, Anber agreed to remove the Billings material from the web and return all electronic disclosure he had for any case to the Crown. He offered and arranged a mentoring program for himself with three senior defence lawyers that became part of the judge's subsequent order. The judge declined, despite the Crown's urging, to bar Anber from uncontrolled access to all future disclosure. Nevertheless, he chastened Anber.
"What took place here are egregious violations of several rules of professional conduct," Maranger stated in his ruling.
Law society prosecutor Suzanne Jarvie asked the panel to penalize Anber with full costs of $7,205 and a "short but sharp" two-month licence suspension. "We are looking at a very serious breach of client confidentiality," she said. "There is certainly not another case . . . that is anything like this." The law society needs to send a message that it views this breach as egregious, she added.
Among the factors tempering Anber's misconduct was his admission of wrongdoing, his expression of remorse, and the agreement to get mentoring, she said. Otherwise, she said, she could have sought a six-month suspension.
Anber's lawyer, Michael Johnston, asked for a reprimand but no suspension. He didn't challenge costs, stating his client feels law society members shouldn't bear the financial burden of his case.
Called to the bar in 2009, Anber practises over a wide geographical area and is viewed as an outsider by lawyers at some of the courts in which he appears, said Johnston. He had few mentors until after this incident, he added.
"He now has been chastened," said Johnston.
He settled for $16,520 after his former client sued him.
Federal and provincial Crown offices in Ottawa now deny him access to electronic disclosure. As a result, he must view the material on their premises "under gazes of suspicion and, in some cases, hatred," said Johnston. He has been "publicly shamed," he noted.
The ordeal has taken a toll on his family. His wife, who was at the hearing with their two-month-old son, said Anber is under so much stress that he sleeps only two hours a night and has gained weight, according to Johnston.
"He has learned from his errors," Johnston added. A suspension, in a way, "would be non-responsive" because he needs an opportunity to learn, he said.
The panel reserved its penalty decision.


text:
Q: Odd things happening with Netbeans and WAMP appeared out of nowhere Before I used to press the 'run' button in netbeans and the website would pop up on firefox.
Now suddenly something changed in my WAMP server (might have been because I installed an older version of PHP to test PHPunit and then deinstalled it again once i figured out how it worked with 5.5( i changed some settings in WAMP but put em all back)).
Does anyone have the slightest idea how I might be able to send the impulse to the firefox web browser again to open the link when I press run in Netbeans?
I can reach the sites by typing in the URL on the firefox webbrowser so the problem isn't that big but it's still a tiny annoyance.

redpajama_set_name: RedPajamaStackExchange


text:
Arsene Wenger: Injury update on Ramsey, Arteta, Walcott and Koscielny
By Harry Robertson
Arsenal boss Arsene Wenger says defender Laurent Koscielny could be in the squad to face West Ham United this weekend.
With midfielder Tomas Rosicky and striker Yaya Sanogo already available for Friday's game against Queens Park Rangers, the Frenchman explained that all injured players will soon be back in the squad, including midfielder Aaron Ramsey.
"From Sunday I think everybody should be fit and available," Wenger said.
"We might have [Tomas] Rosicky back in the squad and [Yaya] Sanogo back in the squad. [Hector] Bellerin, I don't know yet, but certainly Rosicky and Sanogo will be available.
"David Ospina is available as well [but Aaron Ramsey, Mikel Arteta and Mesut Ozil] are all out. The closest at the moment is [Laurent] Koscielny. I believe he's a bit short for Friday, maybe has a chance for Sunday.
"He [Mikel Arteta] is progressing well. It should be very quick now. He will not play before January. [Aaron] Ramsey has a chance to be fit for Southampton."
Asked to give an update on England international Theo Walcott, Wenger said: "He [Theo Walcott] had only two days training with the team, I will see how he is this week. But fitness-wise he is not far.
"It is more that Theo has basically not played since January, so it has been a year. He has been missed because first of all he is a top-quality player, a team player and when he got injured he was in fantastic form. So of course he is a big miss for us.
"[But] I think [we will see the best of him]. Theo was born in 1989, 25 years old, and it is the best age for a player of his quality. He is as quick as he was, believe me. I see it in training."
Aaron Ramsey, Arsene Wenger, David Ospina, Laurent Koscielny, Mesut Ozil, Theo Walcott, Tomas Rosicky, Yaya Sanogo
Arsene Wenger gives positive news on Henrikh Mkhitaryan ahead of ...
Arsenal attacking midfielder Henrikh Mkhitaryan is close to making a return for the Gunners.
Manager Arsene Wenger confirms Henrikh Mkhitaryan will be out for ...
Arsenal attacking midfielder Henrikh Mkhitaryan is out for weeks, according to manager Arsene Wenger.
Henrikh Mkhitaryan delighted after winning Footballer of the Year ...
Arsenal attacking midfielder Henrikh Mkhitaryan says it is a great honour to be voted as Armenia's Footballer of the Year for the year 2017.
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Arsenal manager Arsene Wenger happy with players' attitude despite Cologne defeat
Arsenal Transfer Rumours
The very latest Arsenal transfer rumours from our transfer market experts.

redpajama_set_name: RedPajamaCommonCrawl


"When can fruit fly lay eggs in their host fruit?" was the simple question posed to the fruit fly expert of the then Department of Agriculture some years ago. The answer was "in the period between before the fruit ripens to the point of it being over-ripe or rotting, on the tree or on the ground under the tree, in the fruit supply-chain, in the shops, in the house, on a compost heap or a worm farm, in garbage bins and on landfill sites".
This explains why the fruit fly is so successful, prolific and so difficult to control.
The name "fruit fly" means different things to many people, and there are many types of fruit fly. The "Queensland fruit fly" is the one that we are concerned with.
Any serious attempt at controlling the incidence of Queensland fruit fly attack should include a full understanding of the life-cycle of the fly, and at each step when the adult fly has access to its host fruit.
This item looks at the period when we humans attempt to dispose of any fruit, or parts of fruit, that has been harvested for use and we no longer have a need of. We either send it to landfill or attempt to recycle it into new plant food. Recycling can include composting or placing in a worm powered unit (worm farm).
The basic principle suggested here is to place a paper barrier of some sort between the host fruit and the adult fly. Two methods have been proved successful in lengthy trials, both require a container of some sort to be located near the kitchen sink to collect food waste etc.
The first method uses newspaper to wrap the scraps to make a parcel in the kitchen, the parcel is then place on a compost heap, in a worm farm or in a garbage bin. A container of 2 to 3 litre capacity is required and the scraps need to be fairly dry.
The second method uses paper sacks previously used to hold 12.5 Kg of bakers flour, (no plastic lining). An empty sack is placed either on the compost heap or worm farm and the food waste container from the kitchen emptied into the sack. The waste can contain liquids or wet items such as coffee grounds. One or more additions of scraps can be added before another new sack is required.
Effective barrier between fruit fly and their host fruit.
Good way of introducing carbon to the normally high nitrogen materials.
Keeps the scraps moist for quicker breakdown.
Reduces annoyances such as vinegar flies and blowflies.
Better appearance, only paper visible instead of rotting foodstuffs.
Rein Worm Factory, before legs became available.
Trays in the worm farm should be filled to the top with parcels. The next empty tray sitting on the parcels.
The material shrinks and the higher tray settles down to sit on its stops; this also increases the capacity of the unit, which was a bit small for us.
Additional working trays could be purchased.
Adding wrapped wastes to prevent access to scraps by fruit fly.
This article by Graham Roberts.


Rain or shine... the event will go on!
from 100 different countries. Over 170 colleges and universities attended the event last spring!
SMC's College Fairs have become very popular and thanks to the schools' interest in our students our fair has become the largest of all other California Community Colleges.
Hanging out in beautiful Santa Monica after the event and want to know what to do or where to go?


text:
Rear services were those agencies in Warsaw Pact and Soviet style military forces concerned with military logistics and support. The Rear Services supplied the armed forces with ammunition, fuel, spare parts, food, clothing, and other material. They included the Central Military Medical Administration. Usually a deputy minister of defence served as chief of Rear Services for the armed forces. Rear Services encompasses mainly the Rear Services' Staff, several main and central departments and other services.

Structure
Major departments were:

Military Transportation which was the primary traffic management organization for the armed forces and was responsible for coordinating and planning supply movements by all means of transport.
Food Supply which both procured food from civilian agricultural enterprises and operated a military state farm system to supply troops, particularly those serving in remote areas.
Clothing Supply usually had its own clothing factories to manufacture uniforms and specialized gear.
The main and central directorates operated post exchange, health care, and recreational facilities for military personnel. The Rear Services also provided financial reports on armed forces activities to party and government organs.
The chief of the Rear Services commanded the Railway Troops, Road Troops (:ru:Дорожные войска), Pipeline Troops, and Automotive Troops. The mission of these supporting service was to construct and maintain the military transport infrastructure. Automotive Troops trained and provided the drivers and mechanics needed to maintain and drive cargo trucks loaded with supplies from railheads to operational units in the field. Railway Troops, Road Troops, and Pipeline Troops built permanent rail lines, roads, and pipelines between the supply points and units in the field.
Formerly divided among independent maintenance, medical, and motor transport companies, the provision of rear services in Soviet and some Warsaw Pact regiments had become the responsibility of unified matériel support units. As in most armies, these matériel support units were subordinate to operational commanders, although they worked with the next highest chief of rear services on technical matters.
Traffic Regulators, were generally non-military police military personnel who directed traffic through temporary traffic control zones using signs or flags. They were responsible for maintaining the safety and efficiency of traffic, as well as the safety of road workers, while allowing construction, accident recovery or other tasks to proceed. They used signal flags, hand signals, signalling batons, traffic cones, and warning signs to control traffic movement.

In 1984 main components included food supply, fuel supply, Central Military Medical Administration, Clothing Supply, Main Administration of Trade (Voyentorg, military stores & military post exchanges), Rear Cadres (personnel), Railway Troops, Pipeline Troops, Central Administration of Military Communications (VOSO), veterinarian, Motortransport Service (provides vehicles while Automotive Troops provides drivers), Highway Directorate, Chief, Tourism and Excursion Administration, and the Tyl Political Section. The last five departments were all headed by general-majors. Later the agricultural department was separated from food supply. The Rear Services also included the Topographic Troops (:ru:Трубопроводные войска).

On the dissolution of the Soviet Union much of the former Soviet component became the Rear Services of the Armed Forces of Russia.

Chiefs of the Rear Services

References

  For sale by the Supt. of Docs., U.S. G.P.O.
 Большая советская энциклопедия (БСЭ), Третье издание, выпущенной издательством «Советская энциклопедия» в 1969—1978 годах в 30 томах;
 Военный энциклопедический словарь (ВЭС), Москва (М.), Военное издательство (ВИ), 1984 г., 863 стр. с иллюстрациями (ил.), 30 листов (ил.);
 Тыл Вооружённых Сил: 300 лет. / Под ред. генерал-полковника В. И. Исакова. — Moscow: Защитники Отечества, 2000. — 336 с.
 Бочков Е. А. Локальные войны и конфликты с участием Красной Армии (конец 1930-х — начало 1940-х годов): тыловое обеспечение. — СПб., 2007.
 Булгаков Д. В., Турков А. Г. Тыл Красной Армии в советско-финляндской войне 1939—1940 гг. — М., 2008.  — 269 pages

Warsaw Pact
Military units and formations of the Soviet Union
Military logistics
Road transport
Road traffic management
Road safety

redpajama_set_name: RedPajamaWikipedia


Senior Vice President, Acquisitions and Dispositions of Core Pacific Advisors and CRHMI. Mr. Billsten has over 24 years of commercial real estate experience. From 2008 through 2015, he was responsible for the leasing of CRHMI's commercial property portfolio consisting of office, industrial and retail properties. Over the past seven years, the CRHMI team successfully negotiated over 500 leases totaling over 5.5 million square feet with an aggregative gross lease value of over $320 million. He is also responsible for the disposition coordination of CRHMI assets totaling over $200 million in the past 12 months. His background includes the acquisition, disposition, due diligence and asset management of office, industrial, retail and multi-family assets at PM Realty Advisors, a pension fund advisor, and in brokerage at CBRE. Mr. Billsten began his career in the Real Estate Services Group at Arthur Andersen & Co. Mr. Billsten holds a MBA from the University of Southern California, and a BA from Wheaton College (IL).


text:
\section{Introduction}
\label{sec:Intro}

Today's Cyber-Physical Systems (CPSs) such as smart buildings, factories, power plants, and data centres are large, complex, and affixed with networked sensors and actuators that generate substantial amounts of multivariate time series data that can be used to continuously monitor the CPS' working conditions to detect anomalies in time \cite{Jonathan2017} so that the operators can take  actions to investigate and resolve the underlying issues. The ubiquitous use of networked sensors and actuators in CPSs and other systems (e.g. autonomous vehicles) will become even more prevalent with the emergence of the Internet of Things (IoT), leading to multiple systems and devices communicating and possibly operating a large variety of tasks autonomously over networks.  As many of the CPSs are engineered for mission-critical tasks, they are prime targets for cyber-attacks.  It is thus of particular importance to closely monitor the behaviors of these systems for intrusion events through anomaly detection using the multivariate time series data generated by the systems.

An anomaly is usually defined as points in certain time steps where the system's behaviour is significantly different from the previous normal status \cite{Chandola:2008}.  The basic task of anomaly detection is thus to identify the time steps in which an anomaly may have occurred.
Traditionally, Statistical Process Control (SPC) methods such as CUSUM, EWMA and Shewhart charts were popular solutions for monitoring quality of industrial processes to find out working states that are out of range \cite{Sun:2014}. These conventional detection techniques are unable to deal with the multivariate data streams generated by the increasingly dynamic and complex nature of modern CPSs.  As such, researchers have moved beyond specification or signature-based techniques and begun to exploit machine learning techniques to exploit the large amounts of data generated by the systems\cite{Donghwoon2017}.  Due to the inherent lack of labeled data, anomaly detection is typically treated as an unsupervised machine learning task.  However, most  existing unsupervised methods are built through linear projection and transformation that is unable to handle non-linearity in the hidden inherent correlations of the multivariate time series. Also, most of the current techniques employ simple comparisons between the present states and the predicted normal ranges to detect anomalies, which can be inadequate given the highly dynamic nature of the systems.

Recently, the Generative Adversarial Networks (GAN) framework has been proposed to build generative deep learning models via adversarial training \cite{Goodfellow2014}.  While GAN has been shown to be wildly successful in image processing tasks such as generating realistic-looking images, there has been limited work in adopting the GAN framework for time-series data todate. To the best of our knowledge, there are only few preliminary works that used GAN to generate continuous valued sequences in the literature.  Yet in these early works, the GAN framework has been proven to be effective in generating time series sequences, either to produce polyphonic music with recurrent neural networks as generator and discriminator \cite{Mogren2016}, or to generate real-valued medical time series using an conditional version of recurrent GAN \cite{Esteban2017}. These early successes of GAN in generating realistic complex datasets, as well as the simultaneous training of both a generator and a discriminator in an adversarial fashion, are highly suggestive of the use of the GAN framework for anomaly detection.

In this work, we propose a novel Multivariate Anomaly Detection strategy with GAN (MAD-GAN) to model the complex multivariate correlations among the multiple data streams to detect anomalies using both the GAN-trained generator and discriminator.  Unlike traditional classification methods, the GAN-trained discriminator learns to detect fake data from real data in an unsupervised fashion, making it an attractive unsupervised machine learning technique for anomaly detection \cite{XueY2017}. Inspired by \cite{Raymond2016} and \cite{Salimans2016} that updates a mapping from the real-time space to a certain latent space to enhance the training of generator and discriminator, researchers have recently proposed to train a latent space understandable GAN and apply it for unsupervised learning of rich feature representations for arbitrary data distributions. \cite{Schlegl2017} and \cite{Zenati2018} showed the possibility of recognizing anomalies with reconstructed testing samples from latent space, and successfully applied the proposed GAN-based detection strategy to discover unexpected markers for images. In this work, we will leverage on these previous works to make use of both the GAN-trained generator and discriminator to detect anomalies based on both reconstruction and discrimination losses.

The rest of this paper is organized as follows. Section \ref{sec:RW} presents an overview of the related works. Section \ref{sec:Model} introduces our proposed MAD-GAN framework and the anomaly score function. In Section \ref{sec:ConcandFW}, we introduce the tested CPSs and datasets. In Section \ref{sec:ExpandRes}, we show the experimental results of our proposed MAD-GAN on two real-world CPS datasets. Finally, Section \ref{sec:ConcandFW} summarizes the whole paper and suggests possible future work.

\section{Related Works}
\label{sec:RW}

Given the inherent lack of labeled anomaly data for training supervised algorithms,  anomaly detection methods are mostly based on unsupervised methods. We can divide the unsupervised detection methods into four categories: i) linear model-based method, ii) distance-based methods, iii) probabilistic and density estimation-based methods, and vi) the deep learning-based methods that are recently highly popular.

For linear model-based unsupervised anomaly detection methods, a popular approach is the Principal Component Analysis (PCA) \cite{Li:2014}. PCA is basically a multivariate data analysis method that preserves the significant variability information extracted from the process measurements and reduces the dimension for huge amount of correlated data \cite{Wold1987}. PLS is another multivariate data analysis method that has been extensively utilized for model building and anomaly detection \cite{Herman1985}. However, they are only effective for highly correlated data, and require the data to follow multivariate Gaussian distribution \cite{XuewuD2013}.

For the distance-based methods, a popular approach is the K-Nearest Neighbor (KNN) algorithm which computes the average distance to its k nearest neighbours and obtains anomaly scores based on this distane\cite{Fabrizio:2002}.  The Clustering-Based Local Outlier Factor (CBLOF) method is another example of the distance-based methods.  It uses a predefined anomaly score function to identify anomalies based on clustering, which is an enhanced version of the Local Outlier Factor (LOF) method \cite{Breunig:2002}. Although effective in some cases, these distance-based methods perform better with priori knowledge about anomaly durations and the number of anomalies.

The probabilistic model-based and density estimation-based methods were proposed as improvements of distance-based methods by paying more attention to the data distributions. For example, the Angle-Based Outlier Detection (ABOD) method \cite{Kriegel:2008} and Feature Bagging (FB) method \cite{Lazarevic:2005} deal with data by taking variable correlations into consideration. However, these methods are unable to take into consideration the temporal correlation along time steps, and thus do not work well for multivariate time series data.

The deep learning-based unsupervised anomaly detection methods have gained much popularity recently with their promising performance. For instance, the Auto-Encoder (AE) \cite{Jiawei2011} is a popular deep learning model for anomaly detection by inspecting its reconstruction errors. Others like Deep Autoencoding Gaussian Mixture Model (DAGMM) \cite{ZongBo:2018} and LSTM Encoder-Decoder \cite{Habler:2018} have also reported good performance for multivariate anomaly detection. In this work, we follow the  promising success of deep learning-based unsupervised anomaly detection methods, and propose a novel deep learning-based unsupervised anomaly detection strategy  built on the basis of the Generative Adversarial Networks (GAN).

Our contributions of this paper are summarized as follows: (i) we proposed MAD-GAN, a GAN-based unsupervised anomaly detection method  to detect anomalies for multivariate time series; (ii) the MAD-GAN architecture adapts the GAN framework previously developed for  image-related applications to analyze multivariate time series data by adopting the Long Short Term-Recurrent Neural Networks (LSTM-RNN) as the base models learned by the GAN to capture the temporal dependency; (iii) we used both GAN's discriminator and generator to detect anomalies using a novel anomaly score that combines the discrimination results and reconstruction residuals for each testing sample.  The proposed MAD-GAN is shown to outperform existing methods in detecting anomalies caused by cyber-attacks for two CPS datasets.

\section{Anomaly Detection with Generative Adversarial Training}
\label{sec:Model}
The basic task of anomaly detection for time series is to identify whether the testing data conform to the normal data distributions; the non-conforming points are called anomalies, outliers, intrusions, failures or contaminants in various application domains \cite{Donghwoon2017}. Fig. \ref{fig:MAD-GAN} depicts the overall architecture of the proposed MAD-GAN.

\begin{figure*}[!t]
\centering
\includegraphics[width=1.0\textwidth]{MAD-GAN-long.png}
\vspace{-0.5cm}
\caption{MAD-GAN: Unsupervised GAN-based anomaly detection. On the left is a GAN framework in which the generator and discriminator are obtained with iterative adversarial training. On the right is the anomaly detection process where both the GAN-trained discriminator and generator are applied to compute a combined anomaly score based on discrimination and reconstruction.}
\label{fig:MAD-GAN}
\end{figure*}

\subsection{MAD-GAN Architecture}
\label{subsec:MAD-GAN}
 First, to handle the time-series data, we construct the GAN's generator and discriminator as two Long-Short-Term Recurrent Neural Networks (LSTM-RNN), as shown in the left middle part of Fig. \ref{fig:MAD-GAN}. Following a typical GAN framework, the generator ($G$) generates fake time series with sequences from a random latent space as its inputs, and passes the generated sequence samples to the discriminator ($D$), which will try to distinguish the generated (i.e. ``fake'') data sequences from the actual (i.e. ``real'') normal training data sequences.

Instead of treating each data stream independently, the MAD-GAN framework considers the entire variable set concurrently in order to capture the latent interactions amongst the variables into the models.  We divide the multivariate time series into sub-sequences with a sliding window before discrimination. To empirically determine an optimal window length for sub-sequences representation, we used different window sizes to capture the system status at different resolutions, namely $s_w=30\times i, i=1,2,...,10$.

As in standard GAN framework, the parameters of $D$ and $G$ are updated based on the outputs of $D$, so that the discriminator can be trained to be as sensitive as possible to assign correct labels to both real and fake sequences, while the generator will be trained to be as smart as possible to fool the discriminator (i.e. to mislead $D$ to assign real labels to fake sequences) after sufficient rounds of iterations. By being able to generate realistic samples, the generator $G$ will have captured the hidden multivariate distributions of the training sequences and can be viewed as an implicit model of the system at normal status. At the same time, the resulting discriminator $D$ has also been trained to be able to distinguish fake (i.e. abnormal) data from real (i.e. normal) data with high sensitivity. In this work, we propose to exploit both $G$ and $D$ for the anomaly detection task by (i) reconstruction: exploiting the residuals between real-time testing samples and reconstructed samples by $G$ based on the mapping from real-time space to the GAN latent space; and (ii) discrimination: using the discriminator $D$ to classify the time series. This is depicted in the right middle part of Fig. \ref{fig:MAD-GAN}. As shown, the testing samples are mapped back into the latent space to calculate the corresponding reconstruction loss  based on the difference between the reconstructed testing samples (by the generator $G$) and the actual testing samples. At the same time, the testing samples are also fed to the trained discriminator $D$ to compute the discrimination loss.  Note that the testing multivariate time series are similarly divided into a set of sub-sequences by sliding window and before being fed into the detection model. We use a novel Discrimination and Reconstruction Anomaly Score (DR-Score) to combine the two losses to detect potential anomalies in the data (more details are described in Section \ref{subsec:DRS}).

\subsection{GAN-based Anomaly Detection}
\label{subsubsec:Prob}
Let us now formulate the anomaly detection problem using GAN. Given a training dataset $\mathcal{X}\subseteq \mathcal{R}^{M\times T}$ with $T$ streams and $M$ measurements for each stream, and a testing dataset $\mathcal{X}^{test}\subseteq \mathcal{R}^{N\times T}$ with $T$ streams and $N$ measurements for each stream, the task is to assign binary ($0$ for normal and $1$ for anomalous) labels to the measurements of testing dataset. Note that we assume here that all the points in the training dataset are normal.

To effectively learn from $X$,  we apply a sliding window with window size $s_w$ and step size $s_s$ to divide the multivariate time series into a set of multivariate sub-sequences $X=\lbrace x_i, i=1,2,...,m \rbrace \subseteq \mathcal{R}^{s_w\times T}$, where $m=\frac{(M-s_w)}{s_s}$ is the number of sub-sequences. Similarly, $Z=\lbrace z_i, i=1,2,...,m\rbrace$ is a set of multivariate sub-sequences taken from a random space. By feeding $X$ and $Z$ to the GAN model, we train the generator and discriminator  with the following two-player minimax game:
\begin{equation}
\begin{array}{lcl}
\label{eq:gan_minmax}
\min \limits_G \max \limits_D V(D,G) = \mathcal{E}_{x\sim p_{data}(X)}\left[ \log D(x)\right]\\
\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; + \mathcal{E}_{z\sim p_z(Z)}\left[\log (1-D(G(z)))\right]
\end{array}
\end{equation}

In this work, both the generator ($G$) and discriminator ($D$) of GAN are Long Short Term-Recurrent Neural Networks (LSTM-RNN).  After sufficient rounds of training iterations, the trained discriminator $D_{rnn}$ and the generator $G_{rnn}$ can then be employed to detect anomalies in $\mathcal{X}^{test}$ using a combined Discrimination and Reconstruction Anomaly Score (DR-Score), which will be introduced in Section \ref{subsec:DRS}.

For detection, the testing dataset $\mathcal{X}^{test}\subseteq \mathcal{R}^{N\times T}$ is similarly divided into multivariate sub-sequences $X^{tes}={x^{tes}_j, j=1,2,...,n}$ with a sliding window, where $n=\frac{(N-s_w)}{s_s}$. Using the computed DR-Score (DRS) of the testing dataset, we label each of the sub-sequences in the testing dataset as follows:
\begin{equation}
\label{eq:mark_vec}
\begin{array}{lll}
A_t^{tes}=\left\{\begin{matrix}
1, & if\; H\left(DRS_t,1\right)>\tau\\
0, & else
\end{matrix}\right.
\end{array}
\end{equation}
where $A_t^{tes}\subseteq \mathcal{R}^{N\times 1}$ is a label vector for the testing dataset, where non-zero values indicate an anomaly is detected, i.e. the cross entropy error $H\left(.,.\right)$ for the anomaly score is higher than a predefined value $\tau$.

\begin{algorithm}[t!]
\begin{algorithmic}
  \LOOP
   \IF {epoch within number of training iterations}
    \FOR {the $k^{th}$ epoch}
     \STATE Generate samples from the random space:
     \STATE $Z=\lbrace z_i, i=1,...,m\rbrace \Rightarrow G_{rnn}(Z)$
     \STATE Conduct discrimination:
     \STATE $X=\lbrace x_i, i=1,...,m\rbrace \Rightarrow D_{rnn}(X) $
     \STATE $G_{rnn}(Z) \Rightarrow D_{rnn}(G_{rnn}(Z))$
     \STATE Update discriminator parameters by minimizing(descending) $D_{loss}$:
        \STATE $\min \frac{1}{m}\sum_{i=1}^{m}\left[-\log D_{rnn}(x_i)-\log (1-D_{rnn}(G_{rnn}(z_i)))\right]$
     \STATE Update discriminator parameters by minimizing(descending) $G_loss$ :
        \STATE $\min \sum_{i=1}^{m}\log (-D_{rnn}(G_{rnn}(z_i)))$
     \STATE Record parameters of the discriminator and generator in the current iteration.
    \ENDFOR
   \ENDIF
   \FOR {the lth iteration}
    \STATE Mapping testing data back to latent space:
       \STATE $Z^k=\min\limits_{Z} Er(X^{tes}, G_{rnn}(Z^i))$
   \ENDFOR
   \STATE Calculate the residuals:
       \STATE $Res = \mid X^{tes}- G_{rnn}(Z^k)\mid$
   \STATE Calculate the discrimination results:
       \STATE $Dis=D_{rnn}(X^{tes})$
   \STATE Obtain the combined anomaly score:
       \FOR {k,j and s in ranges}
           \IF{j+s=k}
             \STATE $R=\lambda Res +(1-\lambda)Dis$
             \STATE $DRS_k=\frac{\sum R_{j,s}}{L_k}$
            \ENDIF
       \ENDFOR
  \ENDLOOP
\end{algorithmic}
\caption{LSTM-RNN-GAN-based Anomaly Detection Strategy}
\label{algo:lstm-rnn-gan}
\end{algorithm}

\subsection{DR-Score: Anomaly Detection using both Discrimination and Reconstruction}
\label{subsec:DRS}

An advantage of using GAN is that we will have a discriminator and a generator trained simultaneously.  We propose to exploit both the discriminator and generator that has been jointly trained to represent the normal anatomical variability for identifying anomalies. Following the formulation in \cite{Schlegl2017}, the GAN-based anomaly detection consists of the following two parts:
\begin{enumerate}
\item \textbf{Discrimination-based Anomaly Detection }\\
Given that the trained discriminator $D$ can distinguish fake data (i.e. anomalies) from real data with high sensitivity, it serves as a direct tool for anomaly detection.
\item \textbf{Reconstruction-based Anomaly Detection }\\
The trained generator $G$, which is capable of generating realistic samples, is actually a mapping from the latent space to real data space: $G(Z):Z\rightarrow X$, and can be viewed as an inexplicit system model that reflects the normal data's distribution. Due to the smooth transitions of latent space mentioned in \cite{Radford2015}, the generator outputs similar samples if the inputs in the latent space are close. Thus, if it is possible to find the corresponding $Z^k$ in the latent space for the testing data $X^{tes}$, the similarity between $X^{tes}$ and $G(Z^k)$ (which is the reconstructed testing samples) could explain to which extent is $X^{tes}$ follows the distribution reflected by $G$. In other words, we can also use the residuals between $X^{tes}$ and $G(Z^k)$  for identifying anomalies in testing data.\\
\end{enumerate}

To find the optimal $Z^k$ that corresponds to the testing samples, we first sample a random set $Z^1$ from the latent space and obtain reconstructed raw samples $G(Z^1)$ by feeding it to the generator (as shown in the right part of Fig. \ref{fig:MAD-GAN}). Then, we update the samples from the latent space   with the gradients obtained from the error function defined with $X^{tes}$ and $G(Z)$.
\begin{equation}
\label{eq:invLoss}
\min\limits_{Z^k} Er(X^{tes}, G_{rnn}(Z^k))=1-Simi(X^{tes},G_{rnn}(Z^k))
\end{equation}
where the similarity between sequences could be defined as covariance for simplicity.

After enough iteration rounds such that the error  is small enough, the samples $Z^k$ is recorded as the corresponding mapping in the latent space for the testing samples. The residual at time $t$ for testing samples is calculated as
\begin{equation}
\label{eq:residual}
Res(X^{tes}_t)=\sum_{i=1}^n \mid x_t^{tes,i}-G_{rnn}(Z^{k,i}_t) \mid
\end{equation}
where $X^{tes}_t\subseteq \mathcal{R}^n$ is the measurements at time step $t$ for $n$ variables. In other words, the the anomaly detection loss is
\begin{equation}
\label{eq:score}
L_t^{tes}=\lambda Res(X^{tes}_t)+(1-\lambda)D_{rnn}(X^{tes}_t)
\end{equation}

Based on the above descriptions, the GAN-trained discriminator and generator will output a set of anomaly detection losses $\{L=L_{j,s},j=1,2,...,n;s=1,2,...,s_w\}\subseteq \mathcal{R}^{n\times s_w}$ for each test data sub-sequence. We compute a combined discrimination-cum-reconstruction anomaly score called the DR-Score (DRS) by mapping the anomaly detection loss of sub-sequences back to the original time series:
\begin{equation}
\label{eq:DRS}
\begin{array}{lll}
DRS_t = \frac{\sum \limits_{j,s\in \{j+s=t\}} L_{j,s}}{lc_t} \\
lc_t = count(j,s\in \{j+s=t\})
\end{array}
\end{equation}
where $t\in \{1,2,...,N\}$, $j\in \{1,2,...,n\}$, and $s\in \{1,2,..., s_w\}$.

The proposed MAD-GAN is summarized in Algo. \ref{algo:lstm-rnn-gan}. We used mini-batch stochastic optimization based on Adam Optimizer and Gradient Descent Optimizer for updating the model parameters in this work.

\section{CPSs and Cyber-attacks}
\label{sec:CPSandAtt}

\subsection{Water Treatment and Distribution System}
\label{subsec:CPS}

\subsubsection{SWaT}
The Secure Water Treatment (SWaT) system is an operational test-bed for water treatment that represents a small-scale version of a large modern water treatment plant found in large cities \cite{Mathur2016}. The overall testbed design was coordinated with Singapore's Public Utility Board, the nation-wide water utility company, to ensure that the overall physical process and control system closely resemble real systems in the field. The SWaT dataset collection process lasted for a $11$ days with the system operated 24 hours per day. A total of $36$ attacks were launched during the last $4$ days of 2016 SWaT data collection process \cite{Jonathan2016}. Generally, the attacked points include sensors (e.g., water level sensors, flow-rate meter, etc.) and actuators (e.g., valve, pump, etc.). These attacks were launched on the test-bed with different intents and diverse lasting durations (from a few minutes to an hour) in the final four days. The system was either allowed to reach its normal operating state before another attack was launched or the attacks were launched consecutively. Please refer to the SWat website\footnote{https://itrust.sutd.edu.sg/testbeds/secure-water-treatment-swat/} for more details about the SWaT dataset.

The water purification process in SWaT is composed of six sub-processes referred to as $P1$ through $P6$ \cite{Jonathan2016}. The first process is for raw water supply and storage, and $P2$ is for pre-treatment where the water quality is assessed. Undesired materials are them removed by ultra-filtration (UF) backwash in $P3$. The remaining chorine is destroyed in the Dechlorination process ($P4$). Subsequently, the water from $P4$ is pumped into the Reverse Osmosis (RO) system ($P5$) to reduce inorganic impurities. Finally, $P6$ stores the water ready for distribution.

\subsubsection{WADI}
Unlike a water treatment system plant which is typically contained in a secured location, a distribution system comprises numerous pipelines spanning across a large area. This highly increases the risk of physical attacks on a distribution network. The Water Distribution (WADI) testbed is  an extension of the SWaT system, by taking in a portion of SWaT's reverse osmosis permeate and raw water to form a complete and realistic water treatment, storage and distribution network. There are three control processes in the water distribution system. The first process is to intake the raw water from SWaT, Public Utility Board (PUB) inlet or the return water in WADI, and store the raw water in two tanks. $P2$ distributes water from two elevated reservoir tanks and six consumer tanks based on a pre-set demand pattern. Water is recycled and sent back to $P1$ at the third process.

The WADI testbed is similarly equipped with chemical dosing systems, booster pumps and valves, instrumentation and analysers \cite{AhmedCM2017}.  In addition to simulating attacks and defences being carried out on the PLCs via networks, WADI has the capabilities to simulate the effects of physical attacks such as water leakage and malicious chemical injections. The WADI data collection process consists of 16 days' continuous operations, of which $14$ days' data was collected under normal operation and $2$ days' with attack scenarios. During the data collection, all network traffic, sensor and actuator data were collected. Please refer to the WADI website\footnote{https://itrust.sutd.edu.sg/testbeds/water-distribution-wadi/} for more details about the WADI dataset.

\subsection{Cyber-Attacks}
\label{subsec:Atta}

The goal of an attacker is to manipulate the normal operations of the plant. It is assumed that the attacker has remote access to the SCADA system of SWaT and WADI and has the general knowledge about how the systems work. Various experiments have been conducted on the SWaT and WADI systems to investigate cyber-attacks and respective system responses. In total, 36 attacks and 15 attacks have been inserted to SWaT and WADI, respectively \cite{Mathur2016,MathurCM2017}. For illustration, let us explain one exemplary attack for each system.
\begin{itemize}
\item \textbf{SWaT}
One attacking goal is to degrade the performance of SWaT from the nominal level (for example, 5 gallons/minute) to a lower value. This attack could be launched by compromising sensor LIT401, which measures the water level of the Reverse Osmosis (RO) feed tank in $P4$. By attacking LIT401, the attacker reduced the level of the RO feed tank from 800mm to 200mm, which would lead the PLC-4 to stop the pump P401 and less water was pumped to $P5$. Finally he negative impact of attacking sensor LIT401 was reflected on the output water flow rate of RO unit (values measured by FIT501 in $P5$). According to system specifications, this flow rate must remain at about 1.2cm/hr which leads to nearly 5 gallons/minute of treated water. Thus, the amount of treated water was reduced during the observation period.
\item \textbf{WADI}
One attacking goal is to tamper the readings of water level sensor in $P1$. The attacker altered the sensor reading from $76\%$ to $10\%$ of the tank capacity which indicated a ``low state''. As a result, the PLC-1 (controller of $P1$) sent a command to turn on the intake water pump to intake more water from WADI return, SWaT output or PUB inlet. At the same time, due to the false low water level state in the raw water tank, the water supply from $P1$ to $P2$ was cut off while $P2$ continued to supply water to the consumer tanks. Thus, water levels of tanks in $P2$ decreased. Once the water level in the elevated tanks ($P2$) reached a low level, the supply to consumer tanks ($P2$) was cut off. Consequently, by tampering the readings of water level sensor in $P1$ to a low level, there would be an overflow in the tanks of $P1$ and no water flow in $P2$.
\end{itemize}

\begingroup
\setlength{\tabcolsep}{10pt}
\begin{table}[h]
  \caption{General Information about Datasets}
  \label{tab:datasets}
  \centering
  \newcommand{\cc}[1]{\multicolumn{1}{c}{#1}}
  \begin{tabular}{llll}
  \hline\hline
    \toprule
          Item& SWaT& WADI& KDDCUP99 \\
    \midrule
          Variables& 51& 103& 34\\
          Attacks& 36& 15& 2\\
          Attack durations(mins)& 2$\sim$ 25& 1.5$\sim$ 30 &NA\\
          Training size (normal data)& 496800& 1048571& 562387 \\
          Testing size (data with attacks)& 449919& 172801& 494021\\
          N\_rate(\%)& 88.02& 94.01& 19.69\\
  \hline\hline
  \multicolumn{3}{l}{*N\_rate is the rate of normal data points versus all in testing data sets. }\\
  \hline
  \bottomrule
  \end{tabular}
\end{table}
\endgroup

\subsection{SWaT and WADI Datasets}
\label{subsec:Dataset}
The SWaT/WADI data collection process lasted for a 11/16 days with the systems operated 24 hours per day. Various cyber-attacks were executed on the test-beds with different intents and divergent lasting durations (from a few minutes to an hour) in the final 4/2 days. The systems were either allowed to reach its normal operating state before another attack was launched or the attacks were launched consecutively. Some general information about these two datasets are summarized in Table \ref{tab:datasets}. To better understand the complexity of the detection tasks, it is worthwhile to note that the two datasets and their associated normal conditions and attacks have the following characteristics:
\begin{itemize}
\item Different attacks may last for different time durations due to different scenarios. Some attacks did not even take effect immediately. The system stabilization durations also vary across attacks. Simpler attacks, such as those aiming at changing flow rates, require less time for the system to stabilize while the attacks that caused stronger effects on the dynamics of system  will require more time for stabilization.
\item Attacks on one sensor/actuator may affect the performance on other sensors/actuators or the whole system (this point can be seen from the two examples in Section \ref{subsec:Atta}), usually after a certain time delay.
\item Furthermore, similar types of sensors/actuators tend to respond to attacks in a similar fashion. For example, attacks on the LIT101 sensor (a water level sensor in $P1$ of SWaT) caused obvious abnormal spikes in both LIT101 and LIT301 (another water level sensor in $P3$ of SWaT) but the affects on readings of other sensors and actuators (such as flow rate sensors and power meters) were relatively smaller.
\end{itemize}

The aforementioned observations suggest that it is of importance to adopt a multivariate approach in the modeling of the systems for anomaly detection, as the overall change in performance by different sub-process could collectively help to better recognize the attacks. In other words, the underlying correlations between the sensors and actuators could be useful to detect the anomalies in the behaviors of the system caused by the attack.

\section{Experiments}
\label{sec:ExpandRes}

\subsection{Data Preparation and System Architecture}
\label{subsec:DataandSys}

In the SWaT dataset, $51$ variables (sensor readings and actuator states) were measured for 11-days. Within the raw data, $496,800$ samples were collected under normal working conditions (data collected in the first 7-days), and $449,919$ samples were collected when various cyber-attacks were inserted to the system subsequently. Similarly, for the WADI dataset, $789371$ samples for $103$ variables were collected under normal working conditions in the first 14-days, and $172801$ samples were collected when various cyber-attacks were inserted to the system in the last 2 days. For both of these datasets, we eliminated the first $21,600$ samples from the training data (normal data) since it took 5-6 hours to reach stabilization when the system was first turned on according to \cite{Jonathan2016}.

In the anomaly detection process, we subdivide the original long multiple sequences into smaller time series by taking a sliding window across raw streams. Since it is an important topic in time series study to decide the optimal window length for sub-sequences representation, we tried a set of different window sizes to capture the system status at different resolutions, namely $s_w=30\times i, i=1,2,...,10$. To capture the relevant dynamics of SWaT data, the window is applied to the normal and testing datasets with shift length $s_s$=$10$.

For this study,  we used an LSTM network with depth 3 and 100 hidden (internal) units for the generator. The LSTM network for the discriminator is relatively simpler with 100 hidden units and depth 1. Inspired by the discussion about latent space dimension in \cite{Esteban2017}, we also tried different dimensions and found that  higher latent space dimension generally generates better samples especially when generating multivariate sequences. We set the dimension of latent space as $15$ in this study.

\subsection{Evaluation Metrics}
\label{subsec:EvMe}

We use the standard metrics, namely Precision (Pre), Recall (Rec), and F\_1 scores, to evaluate the anomaly detection performance of MAD-GAN:
\begin{equation}
\label{eq:Pre}
Pre=\frac{TP}{TP+FP}
\end{equation}
\begin{equation}
\label{eq:Rec}
Rec=\frac{TP}{TP+FN}
\end{equation}
\begin{equation}
\label{eq:F1}
F_{1}=2\times \frac{Pre \times Rec}{Pre+Rec}
\end{equation}
where $TP$ is the correctly detected anomaly (True Positives: $A_t=1$ while real label $L_t=1$), $FP$ is the falsely detected anomaly (False Positives: $A_t=1$ while real label $L_t=0$), $TN$ is the correctly assigned normal (True Negatives: $A_t=0$ while real label $L_t=0$), and $FN$ is the falsely assigned normal (False Negatives: $A_t=0$ while real label $L_t=1$).

Given that our application in this work is to detect intrusions (cyber-attack), it is important for the system to detect all the attacks even if it requires tolerating a few false alarms.  As such, the focus is on correctly detecting  actual positives,  while the false positives are not so important as long as they are not excessive. Therefore, we use recall as the main metric to measure the performance of anomaly detection for this study.

\subsection{Results}
\label{subsec:ADRes}
We evaluate the anomaly detection performance of MAD-GAN on the aforementioned two datasets SWaT and WADI. As described earlier, the sub-sequences are fed into the MAD-GAN model.  Note that to reduce the computational load, we reduce the original dimension by PCA, choosing the PC dimension in based on the PC variance rate. For comparison on the anomaly detection performance, we also applied PCA, K-Nearest Neighbour (KNN), Feature Bagging (FB), and Auto-Encoder (AE) that are popular unsupervised anomaly detection methods on the datasets. To compare with a GA-based method, we  compared  MAD-GAN with the Efficient GAN-based (EGAN) method of \cite{Zenati2018} whose discriminator and generator were implemented as fully connected neural networks.

\subsubsection{Multivariate Generation}
\label{subsubsec:MultiGen}
\begin{figure}[!h]
\centering
\begin{minipage}{1.0\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{swat_rs_gs.png}
\end{minipage}\\
\begin{minipage}{1.0\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{wadi_rs_gs.png}
\end{minipage}%

\caption{Comparison between generated samples at different traning stages: GAN-generated samples at early stage are quite random while those generated at later stages almost perfectly took the distribution of original samples. Note that we only plot four variables for each dataset
as visualization examples.}
\label{fig:rs_gs}
\end{figure}

First, we visualize the multivariate data samples generated by  MAD-GAN versus the actual samples from the CPS. As can be observed in Fig. \ref{fig:rs_gs}, our GAN-generated samples that were clearly different from the training data in the early learning stage (iteration=10). However, after sufficient number of iterations, the generator was able to output realistic multivariate samples for the various sensors and actuators of the systems. Note that no PCA projection was applied to the trainings samples (real samples) in this visualization example.

In addition, we use Maximum Mean Discrepancy (MMD) \cite{Gretton2007} to evaluate whether the GAN model has learned the distributions of the training data.  MMD  is one of the training objectives for moment matching networks \cite{Yujia2015,Chun-Liang2017}.
\begin{equation}
\label{eq:mmd}
\begin{array}{lll}
MMD(Z_j,X_{tes})=\frac{1}{n(n-1)}\sum_{i=1}^n \sum_{j\neq i}^n K(Z_i^k, Z_j^k)\\
\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;-\frac{2}{mn}\sum_{i=1}^n \sum_{j=1}^m K(Z_i^k, X_j^{tes})\\
\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+\frac{1}{m(m-1)}\sum_{i=1}^m \sum_{j\neq i}^m K(X_i^{tes}, X_j^{tes})\\
\end{array}
\end{equation}

We plot the MMD values across GAN training iterations for generating  multivariate samples for both datasets in Fig. \ref{fig:mmd}. We can observe that the MMD values tend to converge to small values after 30-50 iterations for both datasets. We also compared the MMD values for univaraite sample generation. Interestingly,  the early MMD values of multivariate samples were lower than that of univariate samples, and the MMD for multivariate samples also converged faster than the univariate case. This suggests that using multiple data streams can help with the training of GAN model.
\begin{figure}[!h]
\centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{MMD_swat_wadi.png}
\caption{MMD: generation for multiple time series v.s. generation single time series.}
\label{fig:mmd}
\end{figure}

\subsubsection{Anomaly Detection Performance}
\label{subsubsec:Comp}
In Table \ref{tab:detectionRes}, we show the best performance by the popular unsupervised methods (PCA, KNN, FB and AE) with underlines, and the overall  best performance in bold. As mentioned in Section \ref{subsec:DataandSys},  MAD-GAN was tested with multiple sub-sequence length resolutions.

\begingroup
\setlength{\tabcolsep}{15pt}
\begin{table}[!t]
	\caption{Anomaly Detection Metrics for Different Datasets}

	\label{tab:detectionRes}
	\centering
	\newcommand{\cc}[1]{\multicolumn{1}{c}{#1}}
	\begin{tabular}{lllll}
		\hline\hline
		\toprule
		Datasets& Methods& Pre& Rec& F\_1\\
		\hline
		\midrule
		\multirow{8}{*}{SWaT}&PCA&    24.92& 21.63& 0.23\\
		                     &KNN&    7.83& 7.83& 0.08\\
		                     &FB&    10.17& 10.17& 0.10\\
		                     &AE&    \underline{72.63}& \underline{52.63}& \underline{0.61}\\ \cline{2-5}
		                     &EGAN&  40.57& 67.73& 0.51\\

		                     &MAD-GAN*& \textbf{99.99}& 54.80& 0.70\\
		                     &MAD-GAN**& 12.20& \textbf{99.98}& 0.22\\
		                     &MAD-GAN***& 98.97& 63.74& \textbf{0.77}\\
		\hline
		\multirow{8}{*}{WADI}&PCA&     \underline{39.53}& 5.63& 0.10\\
		                     &KNN&     7.76& 7.75& 0.08\\
		                     &FB&      8.60& 8.60& 0.09\\
		                     &AE&      34.35& \underline{34.35}& \underline{0.34}\\ \cline{2-5}
		                     &EGAN&    11.33& 37.84& 0.17\\
		                     &MAD-GAN*& \textbf{46.98}& 24.58& 0.32\\
		                     &MAD-GAN**& 6.46& \textbf{99.99} &0.12\\
		                     &MAD-GAN***& 41.44& 33.92& \textbf{0.37}\\
		\hline
        \multirow{8}{*}{KDDCUP99}&PCA&  60.66& 37.69& 0.47\\
                                 &KNN&   45.51& 18.98& 0.53\\
                                 &FB&    48.98& 19.36& 0.28\\
                                 &AE&    \underline{80.59}& \underline{42.36}& \underline{0.55}\\ \cline{2-5}
                                 &EGAN&    92.00& 95.82& \textbf{0.94}\\
                                 &MAD-GAN*& \textbf{94.92}& 19.14& 0.32\\
                                 &MAD-GAN**& 81.58& \textbf{96.33}& 0.88\\
                                 &MAD-GAN***& 86.91& 94.79& 0.90\\
		\hline
		\multicolumn{5}{l}{Rows GAN-AD* list results chosen by best Precision.}\\
        \multicolumn{5}{l}{Rows GAN-AD** list results chosen by best Recall. }\\
        \multicolumn{5}{l}{Rows GAN-AD*** list results chosen by best F\_1. }\\
		\hline
		\bottomrule
	\end{tabular}
\end{table}
\endgroup

From Table \ref{tab:detectionRes}, we  observe the following:
\begin{itemize}
\item For the SWaT dataset, focused on the results chosen by best F\_1 since F\_1 balance precision and recall, MAD-GAN outperformed the best performance by four popular methods was given by AE by $26.34\%$ and $11.11\%$ for precision and recall, respectively. In fact, MAD-GAN achieved nearly $100\%$ precision and recall here, detecting all the anonymous points correctly for SWaT without false alarms.
\item For the WADI dataset, also focused on the results chosen by best F\_1, recall by MAD-GAN is slightly poorer ($3.02\%$ lower) than that by AE. However, for the best recall case, MAD-GAN outperformed others by $65.64~94.36\%$ based on the recall values. Although the MAD-GAN's precision seems poor, it can achieve a near $100\%$ recall value. This is acceptable in the cyber-attack setting as the cost for false positive is tolerable for detecting all the intrusions (as mentioned in Section \ref{subsec:EvMe}). In comparison, none of the popular detection methods can achieve a satisfactory recall.
\item Between the two  datasets,  MAD-GAN performed markedly better for SWaT. As shown by the ``N\_ rate'' of Table \ref{tab:datasets}, the WADI dataset is more unbalanced than SWaT (i.e. more actual negatives), which leads to more falsely reported positives. In addition, we also note that as shown in Table \ref{tab:datasets}, the WADI dataset has a larger feature dimension than SWaT (WADI has $103$ variables while SWaT only has $51$ variables.).
\item Here, we also applied MAD-GAN to a more balanced dataset, the KDDCUP99 dataset. On this dataset, MAD-GAN can reach $0.90$ F\_1 score with precision larger than $85\%$ and recall higher than $94\%$. Although The EGAN results on KDDCUP99 dataset (which were reported by \cite{Zenati2018}) are better (but not overwhelmingly better) than that by MAD-GAN, MAD-GAN performed better than EGAN for both SWaT and WADI datasets (unbalanced datasets). This is because LSTM-RNN, which is used in MAD-GAN, is capable of learning complex time series better than CNNs used in EGAN. In fact, looking at the relative performance of EGAN with other non-GAN methods, we can see that GAN-based anomaly detection is unable compete other traditional methods if we do not model temporal correlation appropriately.
\end{itemize}

Overall, MAD-GAN consistently outperformed the popular unsupervised detection methods. The only drawback is that it takes LSTM-RNN more time to deal with longer sub-sequences (to be specific, the model becomes slow when the sub-sequence length $s_w$ is larger than $200$). It will be worthwhile to explore using other Neural Networks to incorporate the temporal correlation and consider the choice of sub-sequence length for future work.

\subsubsection{Dimension Reduction}

As mentioned, to minimize the computation load of the LSTM-RNNs, we used PCA to project the raw data into a lower dimensional principal space instead of directly feeding the high dimensional data to the MAD-GAN model. We plot the variance rates of the first 10 Principal Components (PC) for both datasets  in Fig. \ref{fig:PCA} below.

\begin{figure}[h]
\centering
\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth]{PC_rate_swat.png}
\label{fig:swat_Acc_bp}
\end{minipage}%
\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth]{PC_rate_wadi.png}
\label{fig:swat_Pre_bp}
\end{minipage}%

\caption{Variance Ratio of Principal Component for the SWaT and WADI data.}
\label{fig:PCA}
\end{figure}

The figure showed that one main PC explained more than $50\%$ ($30\%$) of the variance for the SWaT (WADI) data.  Also, the PCs after the $5^{th}$ ($8^{th}$) one contributed little to the overall variance (near to $0$). As such, we projected the SWaT (WADI) data to the first $5$ ($8$), and then applied the MAD-GAN to detect anomalies for the projected data.

While the computation load was reduced largely by dimension reduction (reducing more than $\frac{1}{3}$ of the overall training-testing time) information could be lost,  which may affect the generation of realistic time series data.  To show that  the anomaly detection performance was not influenced by removing the less important variables,  in Table \ref{tab:PC_reso}, we list the anomaly detection evaluation metrics of MAD-GAN at different PC resolution (from PC=1 to PC=10) as well as all the original variable space for the SWaT dataset with sub-sequence length $s_w=30$.

\begingroup
\setlength{\tabcolsep}{5pt}
\begin{table}[!t]
  \caption{Anomaly Detection Metrics of MAD-GAN at Different PC Resolutions}

  \label{tab:PC_reso}
  \centering
  \newcommand{\cc}[1]{\multicolumn{1}{c}{#1}}
  \begin{tabular}{llllllllllll}
  \hline\hline
    \toprule
     EM& PC=1& PC=2& PC=3& PC=4& PC=5& PC=6& PC=7& PC=8& PC=9& PC=10& ALL\\
    \hline
    \midrule

    Pre&  16.90& 17.76& 18.57& 14.53& 26.56& 24.39& 13.37& 14.78& 13.60& 13.83& 13.95\\
    Rec&  72.03& 95.34& 92.76& 91.16& 95.27& 81.25& 91.87& 92.02& 95.06& 96.03& 92.96\\
    F\_1& 0.24&  0.23&  0.23&  0.23&  0.37& 0.22&  0.22&  0.22&  0.23&  0.23&  0.23\\
  \hline
  \multicolumn{12}{l}{EM: evaluation metrics. Sub-sequence length equals to 30.}\\

  \bottomrule
\end{tabular}
\end{table}
\endgroup

We observe from Table  \ref{tab:PC_reso} the following:

\begin{itemize}
\item Although the first principal component contributed more than $50\%$ of the variance of the SWaT dataset (as shown in Fig. \ref{fig:PCA}), the MAD-GAN performance was not satisfactory (recall is less than $75\%$) with only this component. By adding the second principal component (the first two principal components together explained for more than $80\%$ of the variance of SWaT), the performance of MAD-GAN was largely improved and the performance (mainly evaluated by recall value) generally converged at a high level (larger than $90\%$) by adding more variable dimensions.
\item By using the first five principal components (which explained for more than $99.5\%$ of the variance), we can achieve a recall higher than $95\%$. Although it was poorer than the recall reported by PC=2,10, the precision obtained by PC=5 was higher than others. This indicates that suitable feature subset/sub-combination selection is important  to reduce the false positive.
\item It is interesting to see that the F\_1 scores under different principal spaces were at similar low levels (about $0.22~0.24$). This was caused by the low precision, as large amount of false positives are reported by MAD-GAN for the unbalanced SWaT dataset. We will discuss more about the precision issues in the next subsection.
\end{itemize}

\subsubsection{Subsequence Resolution}
\label{subsubsec:AD_mul_reso}

Selecting a suitable subsequence resolution (ie. sub-sequence length) is critical for RNN and time series related studies. In this work, we empirically determined the optimal subsequence resolution by trying a series of different sub-sequence lengths (i.e. window sizes) $s_w=30\times i,\; i=1,2,...,10$. For each sub-sequence length, the GAN model was trained recursively for $100$ iterations (i.e. epochs). We depict the box-plots of the recall and F\_1 values of MAD-GAN at each of the training iterations over different sub-sequence lengths in Figs. \ref{fig:GAN-mul-reso_swat_wadi}.

\begin{figure}[!h]
\centering

\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{swat_Precision_boxplot.png}
\label{fig:swat_rec_bp}
\end{minipage}%
\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{wadi_Precision_boxplot.png}
\label{fig:wadi_rec_bp}
\end{minipage

\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{swat_Recall_boxplot.png}
\label{fig:swat_rec_bp}
\end{minipage}%
\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{wadi_Recall_boxplot.png}
\label{fig:wadi_rec_bp}
\end{minipage

\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{swat_F1score_boxplot.png}
\label{fig:swat_f1_bp}
\end{minipage}%
\begin{minipage}{0.49\textwidth}
  \centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{wadi_F1score_boxplot.png}
\label{fig:wadi_f1_bp}
\end{minipage}%

\caption{Values of evaluation metrics (precision, recall and F\_1) as a function of sub-sequence lengths for SWaT and WADI. The boxes show the MAD-GAN performance at fixed 100 training iterations. The green lines in all the boxes are the median values. The mean values of each box are shown as green triangles and they are linked together with a red line.}

\label{fig:GAN-mul-reso_swat_wadi}
\end{figure}

From  Figs. \ref{fig:GAN-mul-reso_swat_wadi}, we can observe the following differences:
\begin{itemize}
\item SWat dataset: Generally, MAD-GAN achieved better metric values (precision, recall and F\_1) with SWaT dataset when the sub-sequence length used was only $3$. The precision values when using sub-sequence length $300$ were markedly poorer than those using smaller sub-sequence lengths, while the recall values were better than the average level across different sub-sequence lengths for the SWaT dataset. This indicates that there are more false positives (bad precision) with larger sub-sequence lengths for SWaT dataset.
\item WADI dataset: The overall F\_1 level for the WADI dataset was poor (less than 0.2) while the average recall values were satisfactory. This is in accordance with what we have mentioned in Section \ref{subsec:ADRes}. The poor F\_1 (as well as precision) was caused by the large amount of false positives reported for the highly unbalanced data. Nevertheless, this is acceptable given that false alarms are relatively tolerable for intrusion detection applications.
\end{itemize}

While there were fluctuations in performance  (in particular recall) and no convergence along the range of tested sub-sequence lengths, we can see that the recall values were roughly oscillating within relatively acceptable bounds ($50~100\%$) for both datasets. Thus, a relatively small sub-sequence length (such as $s_w=30$) could be a safe choice for CPS datasets like SWaT and WADI, where the data were recorded every second, and the system has relatively quick responses\footnote{It only takes several hours for SWaT and WADI to achieve steady states, and the system can recover very quickly after attacks (usually within several minutes).}. This choice of small sub-sequence length can save lots of time while the performance can be guaranteed (As mention in Section \ref{subsubsec:Comp} it took more time to train test the GAN model with sub-sequences longer than $200$). It would be a worthy  further work  for us to investigate and develop a  principled approach to determine the optimal subsequence resolution for time-series analysis using GAN.

\subsubsection{Stability across Multiple Iteration Epochs}

As alluded above, the stability of MAD-GAN is another interesting issue to explore further. To observe the stability during the training epochs, we plot the recall values as a function of the iteration epochs for the SWaT and WADI datasets with sub-sequence length $s_w=180$ in in Fig. \ref{fig:swat_wadi_APR180_100epoch}. Note that the performance for 100 iterations was shown since we have fixed the number of training epochs to be $100$ for our experiments in this work.

\begin{figure}[h]
\centering
\includegraphics[width=1.0\textwidth, height=4.5cm]{swat_wadi_APR180_100epoch.png}
\caption{Values of evaluation metrics as a function of the iteration epochs for the SWaT and WADI datasets when the sub-sequence length $s_w=180$.}
\label{fig:swat_wadi_APR180_100epoch}
\end{figure}

From the figures, we can observe  large variances of the performance (the recall values)  amongst the iteration epochs  for both datasets. On closer scrutiny, it can be seen that the performance was rapidly improved during the first few epochs and then started to oscillate at relatively high level (larger than $60\%$). We have also observed similar fluctuation when testing EGAN (its performance was also not improved by adding more training iterations).  While this  may suggest that a small number of training epochs could be sufficient for MAD-GAN to rapidly achieve satisfactory results,  the subsequent oscillations  indicate the instability of GAN-based anomaly detection method. As further work, it will be worthwhile to consider the stability of GAN-trained generators and discriminators for anomaly detection.

\section{Conclusions}
\label{sec:ConcandFW}

Today's cyber-physical systems, affixed with networked sensors and actuators, generate large amounts of data streams that can be used for monitoring the system behaviors to detect anomalies such as those caused by cyber-attacks.  In this paper, we have explored the use of GAN for multivariate anomaly detection on the time series data generated by the CPSs.  We  proposed a novel MAD-GAN (\underline{M}ultivariate \underline{A}nomaly \underline{D}etection with GAN) framework to train LSTM-RNNs on the multivariate time-series data and then utilize both the discriminator and the generator to detect anomalies using a novel Discrimination and Reconstruction Anomaly Score (DR-Score). We tested MAD-GAN on two complex cyber-attack CPS datasets from the Secure Water Treatment Testbed (SWaT) and Water Distribution System (WADI), and showed superior performance over existing unsupervised detection methods, including a GAN-based approach.

Given that this is an early attempt on multivariate anomaly detection on time series data using GAN, there are interesting issues that await further investigations.  For example, we have noted the issues of determining the optimal subsequence length as well as the potential model instability of the GAN approaches. For future work, we plan to conduct further research on feature selection for multivariate anomaly detection, and investigate principled methods for choosing the latent dimension and PC dimension with theoretical guarantees.  We also hope to perform a detailed study on the stability of the detection model.  In terms of applications, we plan to explore the use of MAD-GAN for other anomaly detection applications such as predictive maintenance and fault diagnosis for smart buildings and machineries.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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This tribute to rapper Mac Miller was created by Greensboro artist JEKS. During the short time we visited, three groups of people came by the mural to admire it and pay their respects to Miller. Someone had even made a makeshift shrine at the mural's base.
If you or someone you love is struggling with suicidal thoughts, please contact the National Suicide Prevention Hotline at 800.273.8255.


text:
Info Starships Fleet Yards Treknology
Episodes Database Fandom Community
Treknology Encyclopedia - V
Varon-T disruptor Type of disruptor that is illegal in the Federation in the 24th century (TNG: "The Most Toys").
It is well possible that there is a general ban on all disruptors, of course including the military types of the Romulans and Klingons.
Vasostimulator 22nd century Vulcan medical device, used to stimulate the blood flow (ENT: "Fusion").
Vehicle replicator in PRO: "Starstruck"
Vehicle replicator Manufacturing facility for shuttles on late 24th century Starfleet ships (PRO: "Starstruck", "Time Amok").
Verteron array Beam emitter based on the planet Mars in the Sol system, designed to deflect comets in orders to terraform the planet (ENT: "Demons", "Terra Prime").
The xenophobic Paxton and his organization "Terra Prime" misused the array to threaten aliens living on the planet Earth.
Verteron mine Device programmed to emit a massive verteron pulse, thereby disabling the subspace systems of any approaching vessel (TNG: "Force of Nature").
The Hekaran scientists Serova and Rabal built these devices, not primarily to damage passing vessels but to keep them from damaging subspace.
Enterprise-D viewscreen in TNG: "Heart of Glory"
Viewscreen Screen used for navigation, communication and various other purposes, most notably as the main viewscreen in the bridge designs of most races. Also called viewer (generic).
Viewscreens (and image sensors) of the future may have on the order of billions of pixels, but for mere display purposes they won't need more than the human eye can resolve. The data rate necessary for a real-time transmission (bandwidths of several gigabits per second) should be no problem. It has been suggested that all main viewscreens are holographic already since the time of TOS, thereby allowing to view a scene from different angles for better orientation. A visual proof for a three-dimensional projection is missing, however.
In the Abramsverse, all starships have a large window on the bridge, facing forward, that doubles as a viewscreen. This even applies to designs from the 22nd century such as the USS Franklin ("Star Trek Beyond").
In the Discoverse, all starships have a large window on the bridge, facing forward, that doubles as a viewscreen, just like in the Abramsverse (DIS, SHO, SNW).
Vinculum Part of a Borg ship roughly equivalent to a computer core (VOY: "Infinite Regress").
Viridium patch Small device that can be attached to clothing. Allows to track a person across a distance of as much as two sectors ("Star Trek: The Undiscovered Country"). Viridium is still used in the late 24th century, but now in an ingestible form (PIC: "Broken Pieces"). See also emergency transponder.
Virtual display Portable display devices that are required to operate the controls on Jem'Hadar vessels. There are at least two models: a smaller headset-like device and a larger one that is shoulder-mounted (DS9: "The Ship", "Call to Arms", "A Time to Stand", "Tacking into the Wind").
As Captain Sisko got a headache from wearing the display device, he handed it over to Garak who successfully mastered the task.
Geordi's VISOR
(Star Trek Fact Files)
VISOR Acronym for Visual Instrument and Sensory Organ Replacement, a device Lt. Cmdr. Geordi La Forge used until 2373 to overcome his blindness. The VISOR was not only sensitive to the visible spectrum, but to a wide range of electromagnetic radiation. By 2373, the VISOR has been replaced by bionic eye implants (TNG).
In the real world the basic functionality of Geordi's VISOR will possibly be achieved within the next decades, provided that our understanding of seeing (which takes place in the brain as well as within the eyes) is improved. Suited sensors will be available very soon. However, subspace physics will most probably not be involved. The subspace technobabble obviously serves to emphasize that the VISOR is a 24th century high-tech device, and allows stories in which Geordi is abducted and his VISOR misused for espionage (TNG: "The Mind's Eye", "Star Trek: Generations"). Still, the question arises why virtually any gadget must be subspace-based as if there were no other technologies in the 24th century. Geordi is blind since his birth, so he got used to see the wide spectrum provided by the VISOR, while his view was confusing for the other crew members, as was shown in TNG: "Heart of Glory". In TNG: "Hide and Q", Q fulfills Geordi's wish to perceive his environment the same way as other people. However, the same could be achieved with a simple set of filters available in any photography shop or with an electronic bandwidth reduction of the sensor element. See also Geordi's VISOR and Eyes.
Visual acuity transmitter Device that allows to transmit visual data from Geordi's VISOR to conventional display devices like the ship's main viewscreen (TNG: "Heart of Glory").
Voice transit conductor Communication system on the planet Kataan developed some time before the planet's sun went nova (TNG: "The Inner Light").
In other words, some sort of telephone, radio or cell phone.
Vulcan death grip Bogus practice invented by Spock to fool the Romulan Commander in 2268, when Spock pretended that he had killed Captain Kirk using this technique (TOS: "The Enterprise Incident").
Vulcan mind meld Ancient Vulcan technique of telepathically linking two minds. Mind melds are performed for different purposes. These include: information retrieval, mutual transfer of knowledge and skills, contact with lifeforms that are not capable of verbal communication, healing of mental traumas, alleviating the symptoms of the Bendi Syndrome, mind control, transfer of the Vulcan katra. Not all of these usages are deemed ethically correct. In the 22nd century mind melds and the people who conducted them were still generally scorned. They gained acceptance again after V'Lar's doctrine of melders being an anti-social minority had been disposed of. In fact, almost every Vulcan is capable of melding minds, among Vulcans as well as with many other species. Vulcan mind melds are a common practice again at latest since the 23rd century (generic).
The history of this technique is exhaustively described in my article on Vulcan Mind Melds.
Vulcan nerve pinch Vulcan technique that allows to render an individual unconscious by pressing two fingers against certain nerves at the base of the neck (generic).
The nerve pinch seems to work on all humanoids and many mammals, even the horses on Nimbus III ("Star Trek: The Final Frontier"). Although it looks very simple, there must be something about the nerve pinch that makes it nearly impossible to learn for non-Vulcans. Only Data masters the technique (TNG: "Unification II"). Purportedly Leonard Nimoy conceived the nerve pinch, because in his opinion Vulcans would not use violence to disable someone. The technique was often referred to as FSNP ("Famous Spock Nerve Pinch") by the TOS staff.
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TV Guide app personalizes viewing choices
Natasha Baker
TORONTO (Reuters) - Television viewers can watch hundreds of shows via broadcast, cable, satellite TV and the Internet, as well as on-demand, but how does one manage all that content? TV Guide has an app to help.
TV Guide late last week released a free companion app for Apple's iOS devices called TV Guide Mobile. The highlight is its "Watchlist," a personalized guide based on user habits that consolidates the many viewing choices available.
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Fans of George Clooney, for example, can see upcoming TV shows or movies starring the actor and being aired by a TV network or other provider. Or, they can watch instantly through direct links to streaming services such as Hulu or iTunes.
"Think of it as a 3D TV Guide for the future in which all your favorites are there in one easy-to-manage interface," said Christy Tanner, general manager of TV Guide Digital.
Users also can share what they are watching with their friends via social networks. This functionality, previously only available on TV Guide's website, has seen over 9 million user check-ins on the Internet.
In the app, check-ins also are used to create a "Trending Tonight" list - a social hot list of the most popular TV shows.
Tanner views the user data as a game-changer for networks and ad agencies looking to predict the next big hit.
"We look at this as a potential early indicator of success," she said. Currently, the top show being added to watch lists is "Elementary" starring Jonny Lee Miller, airing this fall on CBS.
The app supports nearly every cable and satellite provider in the United States, although the streaming partners content is a bit more narrow and includes ABC, ABC Family, CW, Hulu Plus, HBO GO, MAX GO, Crackle and iTunes. Some popular streaming options - such as Netflix - are not yet supported.
"We have a couple dozen streaming partners in the queue so it's just a matter of time before we add more steaming partners in the app," Tanner said.
In addition, TV Guide plans to add more smart and social features such as remote record and recommendations.
The app is available in the United States, and select countries abroad in the Caribbean, South America and Europe.
(Editing by Bob Tourtellotte, Steve Orlofsky and Bob Burgdorfer)
(This story was refiled to delete magazine reference in lead and to correct check-ins to 9 million)
'The Chi' recap: Season 3 returns, minus Jason Mitchell
National Geographic series explores 2 historic Lake Michigan shipwrecks on new Chicago-set episode