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Journal of Computer Networks and Communications
Journal of Computer Networks and Communications / 2019 / Article
AbstractIntroductionEvaluationConclusionsData AvailabilityConflicts of InterestAcknowledgmentsReferencesCopyright
Advances in Machine Learning for Cybersecurity
Volume 2019 |Article ID 7198435 | https://doi.org/10.1155/2019/7198435
San Kyaw Zaw, Sangsuree Vasupongayya, "A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection", Journal of Computer Networks and Communications, vol. 2019, Article ID 7198435, 14 pages, 2019. https://doi.org/10.1155/2019/7198435
A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection
San Kyaw Zaw 1 and Sangsuree Vasupongayya 1
1Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Songkhla 90112, Thailand
Guest Editor: Arash H. Lashkari
Accepted28 May 2019
Published19 Jun 2019
Currently, the smartphone contains lots of sensitive information. The increasing number of smartphone usage makes it more interesting for phishers. Existing phishing detection techniques are performed on their specific features with selected classifiers to get their best accuracy. An effective phishing detection approach is required to adapt the concept drift of mobile phishing and prevent degradation in accuracy. In this work, an adaptive phishing detection approach based on case-based reasoning technique is proposed to handle the concept drift challenge in phishing apps. Several experiments are conducted in order to demonstrate the design decision of our proposed model. The proposed model is evaluated with a large feature set containing 1,065 features from 10 different categories. These features are extracted from more than 10,000 android applications. Five combinations of features are created in order to mimic new real-world Android apps to evaluate our experiments. Moreover, a reduced feature set is also studied in this work in order to improve the efficiency of the proposed model. Both accuracy and efficiency of the proposed model are evaluated. The experimental results show that our proposed model achieves acceptable accuracy and efficiency for the phishing detection.
Mobile communication is becoming more and more important within the context of Industry 4.0 [1]. The topmost security concern for mobile services is the phishing attack which can violence all confidential information of the mobile user [2]. Phishing attacks are increasing and evolving from a variety of newer methods despite the use of a number of detection approaches to battle mobile phishing attacks. Wombat Security revealed that 83% of organizations experienced phishing attacks in 2018 [3]. Figures published by the UK cyber security firm Alert Logic cited that phishing attacks, ransomware, and data loss as the top concerns [4]. Moreover, cybercrimes such as advanced persistent threats (APTs) and ransomware often start from phishing [5]. Currently, the phishers certainly try to hide their malicious payloads from a detection system using methods such as emulator detections, applications icon hiding, and reflection. APWG Phishing Attack Trends Reports released in March 2019 said a detection of phishing sites has become harder because phishers were obfuscating phishing URLs with multiple redirections [6]. In the context of machine learning, this phenomenon is known as concept drift and it becomes the main challenge to mobile phishing detections. Thus, the machine learning classifiers, applied in phishing detection models, must adapt to this concept drift in order to prevent any degradation in their detection accuracy.
In earlier phishing detection works, the variation of individual machine learning classification algorithm was applied. Each earlier phishing detection approaches showed an acceptable detection accuracy while using specific feature patterns with selected detection algorithms in their specific application domain [7, 8]. Currently, the usage of individual classification algorithm in phishing detection is developing to a combination of multiple classifiers in the form of ensemble methods to produce a better accuracy with more efficiency [9–11]. Unfortunately, most existing ensemble classification techniques in phishing detection could not afford to adapt automatically on the variation of input feature patterns, and it remains as a challenging issue in the phishing detection works [12]. Therefore, finding a way to make automatic adaptation classifiers based on the variation of input features pattern will improve the key quality criteria of phishing detection, accuracy, and efficiency.
The main objective of this work is to create a mobile phishing detection system using a case-based reasoning approach for an automatic adaptation of classifiers according to the incoming feature patterns. By addressing the optimal selection of the suitable classifier to the incoming features using a case-based reasoning approach, the proposed mobile phishing detection system could provide the best performance by combining the good performance of all used methods appropriately. An adaptive phishing detection system based on a case-based reasoning (CBR) technique, which can handle the concept drift challenge in phishing apps, is proposed in this work. CBR is applied to construct a phishing detection model. A knowledge base or case base will control the detection algorithm by utilizing phishing features as cases. Moreover, an experimental analysis to verify that our proposed case-based phishing detection is suitable for handling concept drift of mobile phishing attacks than existing detection approaches will be conducted.
The rest of the paper is organized as follows. In Section 2, the background information of phishing attacks on smartphone is presented. The machine learning techniques on phishing detection and the background of case-based reasoning which models the ensemble of classifier approaches as cases in a knowledge base are also illustrated. Next, the overview architecture of the proposed adaptive phishing detection system and their detail processes are described in Section 3. The accuracy and performance analysis of the proposed system is presented in Section 4. The conclusions are described in Section 5.
2. Theoretical Background
The background technologies are described in this section. The nature of phishing attacks on smartphones and their attack techniques are presented in Section 2.1. Section 2.2 presents the literature review on existing phishing detection solutions based on machine learning techniques and their frequently used features. Lastly, case-based reasoning classification techniques are explained in Section 2.3.
2.1. Phishing on Smartphone
Nowadays, phishers are motivated to target smartphones due to several different reasons. A smartphone today is as powerful as a desktop or laptop computer. Smartphones usually contain lots of sensitive information of their owners. The increasing number of smartphone usage makes it more interesting for attackers. The phishing attack techniques are based on two categories including application-oriented phishing attacks and website-oriented phishing attacks.
The application-oriented phishing attacks can be categorized into two types based on their launching methods. First, the phishing application attempts to hijack (task interception) existing legitimate applications and continuously performs task polling. The phishing application will launch itself as long as it detects the launch of the target applications. These task interception attacks are specially based on the fake graphical user interface (GUI) techniques, which can easily impersonate and are hard to detect since a large touch screen is used as the primary user interface on most smartphones. As a result, the fake login interface is layered over the top of the real one, and the phishing app appears to be the target app. Second, the phishing application (repackaged applications) can directly present itself as the targeted legitimate app. This event may occur when the user downloads the fake applications from an unofficial app market. The website-oriented phishing attacks can also be categorized into two types based on their techniques. First, a phishing website hides (spoof) the URL bar of the targeted websites. Second, a phishing website attempts to overlay the genuine website with a crafted pop-up window. The spoofing URL is the process of creating a fake or a forged URL which impersonates a legitimate and secure website. This kind of URL spoofing attack is harmful and dangerous because the website looks exactly like the original one [13]. The fake website asks the user to enter his/her username, password, credit card number, or other information. For a legitimate mobile app that includes an embedded web page served over HTTP or a legitimate mobile app that allows the overlaying pop-up window, the network attacker can change the login button on the page or substitute a crafted pop-up window so that there is a link to a page owned by the attacker. When the user clicks the button, the user will be taken to the phishing page within the embedded web frame. This way, the attacker can steal the user credentials. The attacker can then relay the credentials to the valid website in order to mimic the normal work flow.
Existing solutions in phishing detection show an acceptable accuracy in their specific domain using their targeted features and their specified machine learning techniques. Thus, an effective phishing detection that is less dependent on the features pattern is still needed in this age. This work aims to propose an adaptive phishing detection by combining many existing techniques.
2.2. Current Phishing Detection Solutions
Existing phishing countermeasures use techniques such as content filtering, visual matching, and blacklist or whitelist matching [14]. Content filtering system examines the content of webpages for suspected URLs. Content filtering can be achieved by identifying statistical differences between legitimate and suspected phishing contents or constructing a set of rules [15, 16]. Visual matching computes a visual similarity between the phishing and the legitimate pages, based on the images, blocks, and layout [17, 18]. For a blacklisting system, the known phishing URLs are listed based on a human verification method. The very low false positive rates will be resulted in this approach. For a whitelisting system, users specify the links of trusted sites and frequently accessed websites. By contrary, other new websites will be suspected as phishing attacks [15].
Possible phishing attacks on mobiles which can launch during the control transfers are discussed in [19]. An indicator for the applications identity upon the navigation bar of the system to show the currently running application or the current web page was implemented in [20]. The personalized security indicators to mobile apps are proposed in [21]. However, the user-driven decision-making process is still needed.
The unified and trusted login user interface is used in another group of antiphishing techniques. A software keyboard which can be used safely for login input is provided in [22]. For the purpose of handling the credential, the hardware and software certificates that are used to confirm the login is proposed in [23]. However, these approaches require some modifications to the client application and the user effort. An antiphishing system for mobile platforms was presented in [24]. The work was continued in [4] to detect the persistent account registry phishing attacks. They used OCR technique, and their database needs to save every snapshot of the protected applications and webpages. Using the QR code in phishing attacks was demonstrated and analyzed in [25]. They combined the client-server architecture with a digital signature to perform an integrity checking and authentication. However, the work only focused on the QR code phishing attacks while the phishing malware was not considered. Phishing Detective [26] was created to identify whether or not a link in the user e-mail might send the user to a phishing page. However, the work was totally relied on the blacklist URL of Phish Tank database; it might not be able to satisfy other types of phishing attacks such as activity hijacking and repackaging attacks.
MP-Shield [27] is an Android application that aims to inspect the flow of IP packets between the origin and the destination of mobile user applications. Their work mainly emphasized on the monitoring URL for detection purposes. The types of phishing attack that can be mounted on mobile devices were identified in [19]. The authors conducted an analysis of ways in which the mobile applications and the web sites link to each other. The common control transfer on mobile and how phishing attacks can be mounted against the control transfer scenarios were discussed. The authors presented possible types of phishing attacks along with their legitimate behaviors as summarized in Table 1.
Legitimate behavior Respective attack techniques
Mobile sender Social sharing, upgrades, game credits, opening a target in the browser, send user to embedded http page in browser that links to https login Fake mobile login screen, task interception, scheme squatting, keylogging, URL bar hiding/spoofing, fake browser, using active network attack plus URL bar spoofing.
Web sender Link to mobile e-mail or Twitter, payment via PayPal or Google checkout and user follows link from http to https Website spoofs mobile app, task interception, scheme squatting, URL bar hiding/spoofing, active network attack plus URL bar spoofing.
Legitimate behaviors and their respective phishing attack techniques.
According to Table 1, the mobile sender means a mobile application that sends the user to a website or another mobile application, while the web sender means a website that sends the user to a mobile application or other web sites.
Our work will cover these attack models with ten groups of selected feature categories. Each phishing detection approach showed an acceptable detection accuracy while using different features. Unfortunately, majority of phishing detections may suffer the lack of features for efficient detection of phishing malwares. An optimized solution which used different kinds of features of Android applications to prevent the phishing and malware on Android smartphone is still needed. Our work will contribute to the finding of an optimal solution for mobile phishing detection in the sense of using the feature independently with various classifiers.
2.3. Case-Based Reasoning
Case-based reasoning (CBR) is a problem-solving approach that solves new problems by adapting or reusing old solutions that were used to solve similar problems [28]. The past experience or previous problems are saved as cases, and each case contains representative features, characteristics of the problem, and its solution. The case base is a collection of these cases. The knowledge base of the problem-solving experience is used for the new problem solving [29]. The solutions in the retrieved cases are reused as a proposed solution to the new problem. Thus, the solution to the new problem can be found from similar known solution in the past.
If the new problem situation is exactly the same as the previous cases, then the reuse is simple. CBR systems start their reasoning from the knowledge unit, called cases, while the data-mining systems most often start from the raw data. CBR systems also belong to the instance-based learning systems in the field of machine learning, that are defined as systems that are capable of automatically improving their performance over time. As long as the CBR systems learn new cases in the retain step, they are qualified as the learning systems, thus belonging to the machine learning system [30]. The learning process of a case-based reasoning approach is shown in Figure 1.
Case-based reasoning approach.
Case-based reasoning system performs the learning process as follows:(1)Retrieving the most similar case or cases from the case base to the new problem(2)Reusing the previous solutions of the similar cases to solve the new problem(3)Revising the proposed solution (if necessary)(4)Retaining the solution of the new case for future problem solving
A new problem to the system is represented as a case and is compared with existing cases in the case base. The most similar case or cases are retrieved based on the similarity comparison of case representations. These retrieved cases are adapted (i.e., combined and reused) to propose a solution for the new problem. The suggested solution may need to be evaluated and corrected (i.e., revised) in some cases if it is not the best solution. This verified solution can be added back as a new case to the case base (i.e., retained) or as amendments to existing cases in the case base to be used in future problem solving [28].
3. Architecture Overview
A case-based reasoning model is proposed as an automatic adaptation of classifiers for mobile phishing detection. The information on how to design the case-based adaptive classification system is presented in this section. The proposed system consists of two main parts, including the application on Android smartphones and the detection system on the cloud environment. Figure 2 shows the overall system design.
Overall system design.
As shown in Figure 2, the feature will be extracted from the Android application for the phishing detection process. The detailed information of features will be discussed in Section 3.1. Then, the extracted features will be sent to the cloud environment for phishing detection processes. As the main objective of this work is to enhance the phishing detection processes, the detection will be performed on the static and dynamic feature from Android malware dataset (described in Section 4.1). The detailed process of feature extraction is out of this paper scope.
The contribution of our work starts on the receiving of the extracted features by the detection system. The first process is to retrieve the most similar case from the case base (which stored previous Android phishing detection approach along with the corresponding features). The case-retrieving process will be described in Section 3.3. The case base must be set up before the case-retrieving process. The case base setting up process is shown in Figure 3. The details of the case base setting up process are presented in the following section.
Setting up case base.
According to the retrieved case, the most suitable classification techniques will be used for the adaptive classification. If the feature set extracted from the Android application does not match the sets of features stored in the case base, the adaptive classification will select the suitable methods to process the extracted feature set according to the similarity ratio score. The selection of suitable methods means choosing the multiple classifiers for the extracted feature set. Finally, the final result of the active Android application will be sent to the application on Android smartphone to be displayed to the user.
3.1. Feature for Mobile Phishing
Existing antiphishing solutions on mobile environments were collected, and their features were extracted to identify a phishing attack. Under an Android environment, the features can be extracted from miscellaneous sources such as program entities and program outputs of the runtime monitoring. The list of frequently used features by existing antiphishing solutions can be classified into ten classes including Android components, Android API counts, API usage action, security-sensitive data flow, hardware components, intent actions, permissions, shell command and strings, contents, and visual, and URLs. The details of each feature are given below.(1)Android components: a variety of component types with specific functionalities (e.g., components for providing GUIs, and others for running background services) are declared within an Android app's manifest, and these features are collected in [31–33].(2)API count: the number of invocations of a specific Android API method (e.g., the malicious apps access the location APIs twice and the telephony package 8 times) are collected in [4, 24, 27, 32].(3)API usage actions: APIs can be used to develop applications in Android platform and also misused by malicious purposes. There are many approaches to submit the web requests and to ex-filtrate the captured data via the API without the Internet permission. Some existing phishing detection works [27, 31, 32, 34] collect the API calls (e.g., API calls to access the sensitive data; API calls to access the network communications; API calls to send and receive the SMS messages; API calls to execute the external commands, and API calls frequently used for obfuscation).(4)Security-sensitive data flows: a few approaches for Android malware detection [31, 34, 35] use data flows between security-sensitive Android interfaces to determine if an app is malicious. Tracking this form of information is particularly useful for identifying privacy leaks.(5)Hardware components: the hardware components are listed in AndroidManifest.xml that is used in the app (e.g., to access the camera, an app needs to include android.hardware.camera feature) and these features are collected in [4, 36].(6)Intent actions: Android malwares are known to rely upon tracking of an Intent (e.g., whether a package is installed, or if a device has recently completed booting) to determine when to perform a malicious behavior. These features are used in [32, 36].(7)Permission: specific permissions, provided by Android to execute some risky operations, are acquired by Android malwares. These features are collected in [34, 37, 38].(8)Shell command and strings: the features of interested strings associated with malicious behaviors and potential risky shell commands are collected in [36, 39]. Some of the structural attributes of APK file, such as size of code, presence of zip file, binary file, and related information, are also included in this feature group.(9)Contents and visual: the main display channel for the deception of phishing is the web content which expresses the intention of the website. These features consist of the page elements, such as the page title, the submitted form, and the contained links. Some researchers also extract the logo icon and the contained pictures from the web page and use an image recognition algorithm to identify the phishing website [16–18].(10)URLs: web link features for phishing fraud is collected based on five criteria, including URL and Domain Identity, Security and Encryption, Source Code and Java script, Page Style and Contents, and Web Address Bar. These features are collected in [4, 13, 40].
3.2. Case Representation
A case represents an experience at an operational level. Typically, a case includes the problem specification, the solution, and sometimes the outcome. This is the most common representation used. However, more elaborate case representations can be employed. Depending on the information included in a case, different types of results can be achieved from the system. Cases that describe a problem and its solution can be used to derive solutions to new problems.
In general, a case specification is described as a set of features. The features are those aspects of the domain and the problem that are considered to be most significant in determining the solution and/or outcome. A case represents an experience. In this situation, a case should represent the features of the application that is used to determine a phishing attack.
In our model, a case includes the combination of feature sets, ensemble method of classifiers, or individual classification algorithm with their specific parameters, the accuracy and performance of the solution, and potential facilitations. A case description stored in the phishing detection system is shown in Table 2.
No. Name Value
1 Case ID Case identification number
2 Feature pattern Combination of feature sets
3 Ensemble methods of classifiers (or) classification algorithm Boosting/bagging/bayesian (or) algorithm name and their specific parameters
4 Accuracy Percentage of correct classification
5 Performance Runtime (seconds)
Case description for mobile phishing detection system.
To define a new case in case base, the input features have to pass through different machine learning classifiers, and the results from each classifiers are calculated to produce the final result. Then, the input features, the classifiers with parameters, the activation function, and the final result are stored in the case base (knowledge base) as a new case. The process of defining a new case to be stored in the case base is shown in Figure 4.
Case defining process (define a new case and store in case base).
3.3. Case Retrieval
Case-based reasoning (CBR) solves a new problem by retrieving the previously solved problems and their solutions from a knowledge source of cases, called the case base. There are challenges related to retrieving process that still need to be addressed. One issue is the computation of similarity, which is particularly important during the retrieving process. The effectiveness of a similarity measurement is determined by the usefulness of a retrieved case in solving a new problem.
The aim of using the CBR approach is the selection of the most similar past phishing detection cases to the new problem. A set of similar cases is selected from the case base according to a similarity criterion that requires the specification of weights corresponding to attributes. The assessment of case similarity involves the comparison of attribute values of the new case and that of the past cases, stored in the case base. The retrieved old cases are ranked according to their similarity scores to the attributes of the new case. In this work, the nearest neighbor method is applied to calculate the similarity score and the total similarity score of a potentially useful case.
3.4. Adaptive Classification System Design
The main objective of case-based adaptive classification is to assign a suitable classification technique to the target case (a feature set extracted from Android application) by identifying and analysing the training case (sets of features that are stored in the case base) that is similar. The proposed case-based adaptive classification is shown in Figure 5. If the feature set extracted from the active Android application do not match with any set of features, stored in the case base (that means the extracted feature set is not complete for the case-retrieving process), the adaptive classification will select suitable methods to process the extracted feature set. The selection of suitable methods has two options. First, the possible features are added to the extracted feature set in order to perform the case-retrieving process and to choose a suitable classifier. Second, multiple classifiers are selected to process the extracted incomplete feature set. Under the second option, multiple answers, resulted from multiple classifiers, are collected in order to produce a final answer by the way of weighted sum of all answers.
Adaptive classification.
4. Detection Model and Evaluation
This section explains how our detection model performs adaptively on the combination of individual classifiers and ensemble classifier. To verify that our proposed model can improve the accuracy of the mobile phishing detection, an experiment is conducted using the feature sets (which has been described in Section 3.1). The experiment was conducted by running Weka 3.8 on a Laptop computer with core i7 processor, 8 GB RAM, and Windows8.1 64 bit operating system. The cross-validation method is used as an evaluation technique to estimate the error rate efficiently and in an unbiased way by running repeated percentage splits. Firstly, the dataset is divided into 10 pieces. Each piece is used as a testing dataset in turn while the remaining 9 pieces together are used as a training dataset. We preformed 10 simulations (i.e., experiments are repeated 10 times). Then, all these results are averaged as a single estimation result. Six of the existing machine learning algorithms are chosen from different categories and used with 10-fold cross-validation methods to evaluate the variation of accuracy and efficiency.
4.1. Dataset
The features are extracted from more than 10,000 Android malware samples which are collected from Android malware repositories including VirusShare [41], AndroZoo [42], Droid screening [43] and Reveal droid [44]. There are 76 extracted features of Android components, including 31 features of API counts, 82 features of API usage actions, 421 features of security-sensitive flows, 6 features of hardware components, 109 features of intents, 82 features of permissions, 190 features of malicious shell command and strings, 19 features of content visual, and 49 features of URLs. Thus, there are 1,065 features in total. The information of the feature sets used in this experiment is shown in Table 3.
No Feature sets Number of features Example features
1 Android components 76 android.media, android.media.effect, android.media.audiofx, android.service.textservice, android.service.notification
2 API counts 31 account_information, account_settings, audio, bluetooth, bluetooth_information
3 API usage actions 82 android.util, android.widget, android.renderscript, android.webkit, android.os, android.os.storage, android.content
4 Security-sensitive flows 421 system_settings____audio, system_settings____phone_connection, system_settings____voip, system_settings____database_information
5 Hardware components 6 android.hardware.display, android.hardware, android.hardware.usb, android.hardware.location, android.hardware.input
6 Intent_action 109 action_main, action_view, action_default, action_attach_data, action_edit, action_insert_or_edit
7 Permission 82 android.permission.access_cache_filesystem, android.permission.access_checkin_properties, android.permission.access_coarse_location, android.permission.access_gps
8 Shell_command_strings 190 runtime.exec, createSubprocess, cipher-classes, longstring, SecretKey, method.invoke, small_code_size
9 Content_visual 19 HostnameLength, PathLength, QueryLength, DoubleSlashInPath, NumSensitiveWords, EmbeddedBrandName, PctExtHyperlinks,
10 URLs 49 having_ip_address, url_length, shortining_service, having_at_symbol, double_slash_redirecting, prefix_suffix,
Feature sets.
4.2. Machine Learning Classifiers
To detect and classify the phishing applications, different machine learning classification techniques are used with an adaptive method. An adaptive classification system is proposed to automatically choose a combination of suitable classifiers for the extracted features of an active Android application. Various machine learning techniques were used as the classifier in existing works [31, 32, 34, 35]. Among them, six algorithms were selected from different categories for the coverage usage of all classification nature. The six algorithms include C4.5 (J48), decision table (DT), k-nearest neighbors (IBK), logistic regression (LR), naive Bayes (NB), and support vector machine (SVM). According to the pretesting on the effectiveness of parameter on these classifiers [45], naive Bayes (NB) classifier with supervised discretization function, the default maximum number of iterations in logistic regression (LR), the confidence factor of 0.5 for pruning tree for J48 classifier, and a 1-nearest neighbors (IBK) classifier are chosen for our experiment. SVM and decision table classifiers are used with their default parameters.
4.3. Experimental Results and Analysis
The accuracy comparison of six classifiers on the 10 feature sets is shown in Table 4. The italicized values shown in Table 4 represent the maximum detection accuracy among six classifiers for each feature set. It can be seen that the accuracy of each classification algorithm depends on the features. IBK can provide a better accuracy in 6 features, and J48 can provide a better accuracy in other 4 features. Our work aims to detect mobile phishing in the nature of feature independent with various classifiers. To create a real-world application, a random feature combination is created because a new Android application can consist of any combination of features. In this experiment, 5 random combinations of features are created, as shown in Table 5.
Feature sets J48 (%) DT (%) IBK (%) LR (%) NB (%) SVM (%)
1 Android components 93.23 89.02 93.40 90.16 84.67 87.95
2 API count 95.85 93.02 95.66 91.90 89.20 85.25
3 APIusage_actions 95.20 91.86 95.32 91.97 89.02 91.24
4 Flow 93.05 91.03 93.32 87.18 87.45 83.17
5 Hardware components 89.00 89.06 89.12 89.06 89.02 89.06
6 Intent_action 86.89 85.73 87.13 84.64 83.75 85.53
7 Permission 94.30 91.92 94.65 93.95 88.54 94.14
8 Shell_command_strings 75.40 71.18 74.08 70.28 68.74 70.22
9 Content_visual 97.20 95.79 95.53 94.49 95.77 93.87
10 URLs 96.03 93.24 97.18 93.99 92.98 93.80
Accuracy comparison of classifiers on 10 features.
Case ID Feature pattern Combination of feature sets Number of features
01 Pattern 1 API count + API usage + hardware 112
02 Pattern 2 API count + intent 139
03 Pattern 3 API count + API usage + intent + hardware 220
04 Pattern 4 Flow + intent 529
05 Pattern 5 Flow + intent + API usage + hardware 610
Scenarios for random combinations of features.
These 5 feature combination patterns are tested with individual six classifiers and three models of ensemble classifiers to develop a case for our adaptive model. Each model is an ensemble of six classifiers with different methods in providing the final answer. The final answer finding methods of ensemble classifiers include the average of probabilities, majority voting, and maximum probabilities. The detection results for 5 scenarios of random feature combination sets with the six base classifiers and three ensemble classifiers are described in Table 6. The italicized values shown in Table 6 represent the maximum detection accuracy of 5 cases among nine classifiers.
Case ID J48 (%) DT (%) IBK (%) LR (%) NB (%) SVM (%) AVG (%) MAJ (%) MAX (%)
Detection accuracy of 5 scenarios on randomly combined feature patterns.
According to the results shown in Table 6, some feature patterns are more suitable with ensemble techniques while some are better used with individual classification techniques. It can conclude that the accuracy variation of classification techniques in mobile phishing detection heavily relies on the input features.
The adaptive method used in our model will choose the most suitable classification approach for a set of input features. Based on the results presented in Table 6, we can develop a case to be stored in case base for an adaptive choice of suitable classifiers. The tentative cases for building our case-based phishing detection model is shown in Table 7.
Case ID Feature pattern Adaptive method Accuracy (%) Run time (seconds)
1 Pattern 1 J48 95.93 4.43
3 Pattern 3 AVG 96.43 95.18
4 Pattern 4 AVG, MAJ 90.64 174.4, & 174.6
5 Pattern 5 MAJ 95.69 205.50
Tentative cases for mobile phishing detection system.
Performing the classification process on these large numbers of features takes a long runtime. The comparison of runtime to build the detection model on 6 base classifiers and 3 ensemble approaches before selecting the feature is shown in Table 8.
Case ID J48 DT IBK LR NB SVM AVG MAJ MAX
01 4.43 18.63 0.01 1.93 0.64 3.01 29.87 29.52 26.16
02 4.54 31.80 0.0001 2.57 0.66 39.24 76.20 76.35 75.90
03 9.44 58.80 0.01 7.22 1.14 18.94 95.18 96.09 97.41
04 12.09 148.32 0.0001 5.28 1.39 6.25 174.4 174.6 174.61
05 17.09 167.14 0.01 7.86 1.93 3.61 203.62 205.50 203.51
Runtime comparison of 5 scenarios on 9 classification approaches (in seconds).
To reduce the detection time, some features may be omitted because the features may not provide a high impact on the result. Therefore, some experiments are conducted to select a set of effective features in order to reduce the number of required features.
4.4. Selecting the Features
Feature selection is necessary to reduce the dimension of the feature space. With the aim of getting the benefits of performing a feature selection technique on a large data set such as reducing an overfitting issue, improving accuracy, and reducing a processing time, two feature selection techniques are performed in this experiment by comparing their results to get the optimized results. The process of selecting the features can be described by the following steps.
Let U be the universe of feature sets . And, the dataset , with number of attributes A, be . Then, the attributes can be grouped into feature group as . Some attribute evaluation is performed and selected on the worth of each attribute, which becomes a selected feature set , where .
Two methods of feature selection techniques are used in this experiment to confirm the advantages of selecting the features in phishing detections. The first method is a correlation-based feature selection with a ranker search method that evaluates each attribute and lists the results in a ranked order. The worth of each attribute is evaluated by measuring the correlation (Pearson's) between it and the class [46].
Pearson's correlation coefficient is described in equation (1), where all variables have been standardized. The correlation between a composite and a class label is a function of the number of component variables (attributes) in the composite and the magnitude of the intercorrelations among them, together with the magnitude of the correlations between the attributes and the class label.
If the correlation between each of the attributes in a test and the class label is known, and the intercorrelation between each pair of attributes is given, then the correlation between a composite test consisting of the summed attributes and the class label can be predicted from the following equation:where is the correlation between the summed attributes and the class label; k is the number of attributes; is the average of the correlations between the attributes and the class label; and is the average intercorrelation between attributes.
We get the ranked attributes listed with their corresponding class correlation. Some attributes, which owned no or less values on the class correlation measures, are eliminated. The resulting reduced feature sets are shown in Table 9.
Case ID Feature combination pattern Features before feature selection Features selected by Pearson's correlation Features selected by information gain
01 Pattern 1 112 96 100
02 Pattern 2 139 114 120
Information of selected feature sets for 5 cases.
The second method is an information gain attribute evaluation-based feature selection with a ranker search method. Information gain ratio evaluation is calculated by using the following equations. In the attribute evaluation processes, I index measures the impurity of D; a data partition or a set of training tuples is calculated usingwhere is the probability that a tuple in D belongs to class and is estimated by . The sum is computed over m classes when I index considers a binary split for each attribute. First, the case where A is a discrete-valued attribute having distinct values, occurring in D is considered. The expected information provided by that split is calculated by
In this equation, represents the observations that contain the attribute. The information gain of a binary split on attribute A is calculated by
Information gain ratio attempts to correct the information gain calculation by introducing a split information value. The mathematical formulation for split information is provided in
This value represents the potential information generated by splitting the training dataset, D, into partitions, corresponding to the outcomes of a test on attribute A. The gain ratio is defined in
The attribute with the maximum gain ratio is selected as the highest ranked attribute. The low-ranked attributes that provide a gain ratio less than 0.0003 are eliminated. After performing the two feature selection techniques on the dataset, the reduced feature sets are generated as shown in Table 9.
The same detection experiments are conducted with 9 classifications on each selected feature set. The detection results of 5 cases on selected feature sets are described in Tables 10 and 11. In this experiment, 9 classification approaches with their related parameters are set up as the same as that of previous experiments (described in Section 4.2).
Detection accuracy of 5 cases after correlation attribute evaluation feature selection.
Detection accuracy of 5 cases after information gain attribute evaluation feature selection.
According to the results of the reduced datasets with a correlation attribute evaluation method shown in Table 10, the classification approaches with the best detection accuracy are slightly changed in 2 cases (feature patterns 3 and 4). Feature pattern 3 is a combination of API count, API usage, Intent, and Hardware. The italicized values shown in Table 10 represent the maximum detection accuracy of 5 cases among nine classifiers. The highest detection accuracy is now provided by ensembles with AVG and MAJ final answer methods, while the highest detection accuracy is provided by ensembles with the AVG final answer method when full feature set is used. The detection accuracy is slightly increased for most classifiers in feature pattern 4 which is a combination of flows and Intents features.
According to the results shown in Table 11 of the reduced datasets with an information gain attribute evaluation method, the detection accuracy is increased in 4 cases (feature patterns 1, 3, 4, and 5). The italicized values shown in Table 11 represent the maximum detection accuracy of 5 cases among nine classifiers. Moreover, the classification approaches which produced the best detection accuracy are changed in 3 cases (feature patterns 3, 4, and 5). That is, an ensemble with AVG final answer finding method provides the best accuracy for feature patterns 3, 4, and 5.
The detection accuracy percentages of 5 cases by using different algorithms are comparatively described in Figure 6. This figure represented the detection results from Tables 6, 10, and 11. Each case is represented in 3 situations such as no features selection, after correlation attribute evaluation feature selection, and after information gain attributes evaluation feature selection. There are 15 points in the figure, representing the 5 cases with 3 conditions. The best classifier for case 01 and case 02 is J48 classifier while ensemble classifier AVG is the best one for case 03, case 04, and case 05. The cases with the best algorithm are used in the case-based reasoning detection method.
Accuracy comparison of 9 classifiers on 5 cases before and after feature selection.
With the aim of highlighting the performance of feature selection techniques, the runtime results of reduced feature sets are collected as described in Tables 12 and 13. The information gain attribute evaluation method results in a large number of features than the correlation attribute evaluation method. The runtime of the information gain attribute evolution method is also slightly larger than that of the correlation attribute evaluation method.
04 5.60 44.75 0.0001 5.15 0.56 6.27 61.95 61.99 62.00
Runtime comparison after correlation attribute evaluation feature selection (seconds).
02 3.86 29.43 0.001 2.35 0.56 31.96 67.63 66.52 66.40
04 6.83 87.36 0.01 2.25 0.93 6.76 102.86 93.21 93.15
05 8.42 104.52 0.001 5.37 1.21 3.95 111.80 107.53 108.07
Runtime comparison after information gain attribute evaluation feature selection (seconds).
The runtime on 5 cases by selecting the features are showed in Figure 7. This figure compared the runtime from Tables 8, 12, and 13. There are 15 points in the figure representing the 5 cases with 3 conditions.
Runtime comparison of 9 classifiers on 5 cases before and after feature selection.
Selecting the features with the information gain attribute evaluation approach is applied on our feature sets to improve our model for better accuracy and efficiency. The percentages of detection accuracy on 4 feature patterns are improved as shown in Table 11 while the performances of the detection on all feature patterns are improved as shown in Table 13. Table 14 shows the comparison of accuracy and efficiency of full feature sets and reduced feature sets of our proposed adaptive model. The italicized values shown in Table 14 represent the accuracy values when a reduced feature set is used, and the accuracy values are improved over their counterpart when a full feature set is used.
Case ID Adaptive (before) Adaptive (after) Accuracy (before) Accuracy (after) Runtime (before) Runtime (after)
01 J48 J48 95.93 95.96 4.43 4.05
03 AVG AVG, MAJ 96.43 96.45 95.18 87.31, & 90.63
04 AVG, MAJ AVG 90.64 90.77 174.4, & 174.6 102.86
05 MAJ AVG 95.69 95.80 205.50 111.80
Accuracy and efficiency of proposed adaptive model.
The phishing malware detection task is an imbalanced classification problem. That is, there are two classes to be identified, including phishing and benign, with one category representing the overwhelming majority of the data points. In these cases, the positive class "phishing" is greatly outnumbered by the negative class. These types of problems are examples of the fairly common case in the data science when the accuracy is not a good measure for assessing the model performance. Intuitively, proclaiming all data points as negative in the phishing detection problem is not helpful and, instead, we should focus on identifying the positive cases.
In order to assess the effectiveness of our proposed model, the confusion matrix evaluation is applied: accuracy, precision, and sensitivity. While sensitivity expresses the ability of a model to find all relevant instances in the dataset, precision expresses the proportion of the instances that our model predicts as positive and they are actually positive. The following formulas represent their definitions:
True positive (TP) is the amount of correct positive prediction; false positive (FP) is the incorrect positive prediction; true negative (TN) is the amount of correct negative prediction; and false negative (FN) is the amount of incorrect negative prediction. These four outcomes form the confusion matrix as shown in Figure 8.
Confusion matrix.
The evaluation of effectiveness on our proposed model by means of accuracy, precision, and sensitivity is described in Table 15. According to the results shown in Table 15, our adaptive model achieves a good detection accuracy for the phishing features. Meanwhile, the performance of all the classifiers gets an acceptable precision and sensitivity ratio. According to the previous experiments, our adaptive phishing detection model using case-based reasoning can perform well on the diversely distributed features.
Case Classifier Accuracy (%) Precision (%) Sensitivity (%)
01 J48 95.96 83 79
03 AVG 96.45 92 75
Detection results achieved by the proposed model.
An adaptive mobile phishing detection model based on a variation of input feature patterns using a case-based reasoning (CBR) technique is proposed in this work. An experimental analysis is conducted to demonstrate the design decision of our model and to verify the performance of our proposed model in handling the concept drift of mobile phishing attacks. The proposed model is evaluated with a large feature set that contains 1,065 features from 10 feature groups which are frequently collected from Android apps. Moreover, 5 cases of randomly combined patterns of features are created in order to provide a diversity of unknown patterns to mimic new real-world mobile apps. Six classification algorithms are chosen from different categories for the coverage usage of all classification nature on the diversion of feature sets. Three ensembles of six base classifiers are used, each of which uses different final answer-finding methods including average, majority voting, and maximum. In total, there are 9 classifiers. Due to the involvement of efficient features in the dataset and the uses of multiple classifiers, the efficiency degradation happened. To overcome this hurdle, 2 feature selection techniques are applied on the dataset in order to reduce the size of the features which is the size of the input to the classifiers. The two feature selection techniques used are information gain attribute evaluation method and Pearson's correlation coefficient attribute evaluation method. By addressing the optimal selection of the suitable classifier to the incoming features using a case-based reasoning approach, the proposed mobile phishing detection model could provide an accuracy improvement with an acceptable runtime increment.
The dataset of the features used in this research is available from the authors upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was supported by the Higher Education Research Promotion and the Thailand's Education Hub for Southern Region of ASEAN Countries Project Office of the Higher Education Commission.
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redpajama_set_name: RedPajamaCommonCrawl


text:
How Seth Brinkerhoff's consistency helped Cedar edge Desert Hills for the first time since 2011
Megan Webb
For The Spectrum
For the first time in his career at Cedar High School, Reds running back Seth Brinkerhoff has seen his team get a win over the Desert Hills Thunder.
The games became nail biters in the previous two seasons, with the 2019 matchup between the Thunder and Reds going to two overtimes before Cedar ultimately lost.
On Friday night, though, Brinkerhoff and the Reds finally pulled it off.
He personally helped usher a 17-14 victory over the Thunder, the first Cedar win over Desert Hills since 2011, with a rushing touchdown and leadership.
On the Thunder's senior night, the Reds gutted out a tough win.
The history between these two teams caused high tensions through the majority of the game. Both teams grew increasingly frustrated, and Cedar even saw an unsportsmanlike conduct call at the end of the first half.
Despite all the drama on the field, it was nothing Brinkerhoff hadn't experienced before. He and his team did their best to just brush it off.
"It got a little chippy," Brinkerhoff said. "But we found a way to calm down and now we just have to look at Hurricane and beat Hurricane next week."
Brinkerhoff had a great night against the Reds, as a 26-yard rushing touchdown gave the Reds the lead in the second quarter. Afterward, Brinkerhoff credited the other side of the ball for the win.
"I give it to our defense," Brinkerhoff said. "That big stop at the end and throughout the whole game. The offense had a couple big plays. Other than that, it's all defense."
Brinkerhoff, who's in his senior season, has been a consistent member of the Reds offense. He scored his fourth rushing touchdown of the season against the Thunder and has the most carries for the Reds' rushing attack.
It felt like every time Cedar quarterback Jaron Garrett passed the ball, he was finding Brinkerhoff. The two of them found their rhythm against Desert Hills and were able to frustrate the Thunder's defense.
Cedar head coach Josh Bennett attributed Brinkerhoff's success to his ability to see the game behind his team's offensive line.
"It really starts with our offensive line," Bennett said. "Our offensive line opens up holes for him to run the ball and get through there. He did a good job of seeing the holes and what he did tonight was taking care of the football."
Brinkerhoff was able to use his experience to help close out a tense game against the Thunder. Although it wasn't the same rivalry as the Reds saw last week against Canyon View, this game had many frustrations from two teams that have down gone to the wire the last two seasons.
Ultimately, the win was all that mattered.
"The last two years we lost by a total of three points so it felt good to come out here and win on their home field on their senior night," Brinkerhoff said.
The Reds have two more Region 9 games this season before getting a senior night of their own; the games are against Hurricane and Snow Canyon.
Despite the win over the Thunder that was "great for our program," Bennet said, the Reds are still looking to improve against Hurricane next week.
"We've got to get some things cleaned up and watch some film and get better," Bennett said.
Contact The Spectrum's Sports Desk at sports@thespectrum.com, or (435) 414-3261.


According to the survey: In the tread rubber all steel radial truck tire, generally equipped with 12 to 15 parts of silicon dioxide; tread rubber semi-steel radial tires or light truck tires, in addition to some foreign tire companies in high production, when using the mid-range cars Original tires, in general, not silica; tread rubber portion of bias truck tire, start with 5 to 12 parts of white carbon black. Studies have shown that, in truck tire tread and carcass equipped with 6 to 12 parts of white carbon, to make the best overall performance of the tire; some enterprises in the engineering tire tread equipped with 7 parts white carbon black. In the cut-resistant tire tread is equipped with 35 parts of silica.
The main application is currently the silicon dioxide in the tread compound to improve the resistance to cut and puncture performance tread, yet to reduce rolling resistance in a dominant position. But with the rapid development of automobile production and ownership, as well as the rise in oil consumption and an increase in oil prices, in particular, the country has begun to vigorously promote energy and resource conservation, to reduce tire rolling resistance, reducing fuel consumption demand will also increases. On the other hand, the tire export volume of China reached 30% of tire production, where nearly 30 percent of exports to Europe and North America, the market for "green" tires have a greater demand. Of silica to be relatively low price, so the tire silica demand will also increase rapidly.
Domestic tire companies of silicon dioxide quality requirements, mainly reinforcing better performance, product quality should be stable, also requires good dispersion. However, due to the formulation proportion of the amount is not large, so the demand for highly dispersible silica is not yet urgent. In developing the "green" tire trend, the demand for highly dispersible silica will rapidly increase.


Established in 1955 as a sole trader in paintings by Franz Schmied, the company has developed to one of Austria's biggest corporate groups in the paintings sector. With more than 350 employees we are flexible to carry out orders in a range from the one-room apartment to the million euro construction site.
We offer interior and exterior painting and decorating, wood and metal coatings, facade refurbishment, external thermal insulation, fire protection coatings, floor coverings and coatings.
In addition to that we enlarge our service offer with products according to signmaking like signage, commercial printing, vehicle lettering and adhesive coatings.
With all our services we want to satisfy your needs in the first place. Quality, innovation, flexibility and performance are the central points in our business activities. We constantly work to improve and hold our high quality standards.
Training and further education of our employees are the most important points in our company's philosophy. We employ about 50 trainees for jobs like painting and coating technican, floorer and signmaker. Every year 15 to 20 trainees finish the three years lasting apprenticeship with the final exam. The qualification of the craftsmen is constantly enhanced with training in new application technology or materials in cooperation with our suppliers.
You can find our locations in the east of Austria: in Vienna, St.Pölten, Krems and Zwettl.


Bad news for you, it looks like your roommate is having a bit more luck making friends! Last weekend they went out with a new group of people, and it's pretty clear that you weren't invited.
You didn't know what party it was. No one ever tells you about parties. The only invitations you get are from the girls down the hall who are always making cookies. Their cookies are good, but they are loud and annoying. You don't want to hang out with them.
Well, it was fun while it lasted. A whole semester of having breakfast together and having lunch together and having dinner together may be gone, but it will never be forgotten. Just remember, you're the only one that gets to listen to them breathing while they sleep. No one can take that away from you.


text:
This Might Be A Wiki
The TMBG Knowledge Base
Interpretations:Apophenia
From This Might Be A Wiki
Drum Tab
1 It's About A Kidnapping
2 It's about a person with mental issues
3 A superstitious girlfriend
It's About A Kidnapping[edit]
I've never made an interpretation before, so bear with me
I feel like this entire song is basically about a kidnapping, from the kidnapper's point of view. However, it's all jumbled up, and not in sequential order.
First off, the beginning lines ("How could the street light blink on and off and spell out all your thoughts?") are just him introducing himself to the victim.
Then, we have the line "It's only tea leaves stop being dramatic" is the narrator calming the victim whilst giving them some strong drug (maybe weed?).
He says something weird ("Everyone has got an alien in the gut. Someday it will emerge."), but he dismisses it ("That thing you thought you heard me say: I didn't say that thing")
"Everyone has got a feeling in the gut" is the narrator telling the kidnapped that everyone gets the feeling of worry when they join him (leading to the kidnapping
The "telling you what to do" lines are about the kidnapper, well, telling the victim what to do while they're drugged and hallucinating.
The lines "Pretty soon you'll be telling me that you've had enough and you're leaving me" is referring to the kidnapped realizing their situation and wanting to escape.
"Next thing you'll be saying that I've been hallucinating you all along" is the narrator mocking the victim's claims of kidnapping as outrageous by alluding what other "odd" thing they'll say next.
The victim calls the cops, ("Middleburgh Police Department how may I direct your call?") but they're slurring ("Please speak more slowly") and can't be understood. The same goes for "Person I don't recognize motioning to roll down the window".
--Robot Parade (talk) 20:57, 29 April 2019 (EDT)
It's about a person with mental issues[edit]
I think it's two people alternating speaking in a conversation, a mental doctor, & a mental patient with a bad case of apophenia (witch is a mental disorder when a person finds connections when there isn't any).
For example, the line "It's only tea leaves. Stop being dramatic." Or the line "Everyone has got an alien in the gut. Some day it will emerge. Don't look at me like that" probably indicating the mental patient saying some crazy stuff, & the doctor looking at them & thinking "What are you taking about." But that's just my take on it. —Preceding unsigned comment added by 73.254.44.208 (talk) 04:39, June 29, 2019
A superstitious girlfriend[edit]
Obviously it's taken from the perspective of a boyfriend/girlfriend (Next thing you'll be telling me that you've had enough and you're leaving me). The partner of the narrator thinks the blinking street lights are some weird Morse code thing and they live in the upside down from stranger things. Tea leaves are supposedly a way to see your future, so the tea leaves might give you some crazy ideas. It all adds up!
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text:
Q: Jquery UI Autocomplete: is "value" required? I am using jQuery UI's autocomplete with a remote data file. It works but it's slow. My JSONP is over a meg of 'value' junk characters and I'd like to minimize that. Here is the format:
[{"value": "Aaronsburg, PA"},
 {"value": "Abanda, AL"},
 {"value": "Abbeville, AL"}]

How do I get jQuery UI's Autocomplete to accept a remote data file of the form:
{["Aaronsburg, PA", "Abanda, AL", "Abbeville, AL"]}

or
["Aaronsburg, PA", "Abanda, AL", "Abbeville, AL"]

?

A: jQuery UI Autocomplete can take any format of data. You have complete control the rendering of the list but in this case I think it might just work.
This demo (albeit with a local source) seems to work as expected.
If you need more control you can tailor the renderItem function to suit any data format:
var searches = [{
    label:'first',
    desc:'foo foo',
},
{
    label:'second',
    desc:'bar bar',
},
{
    label:'third',
    desc:'baz baz',
}];

$(function() {
    $('input').autocomplete({
      dataType: 'json',
      source: searches
    })
    .data('autocomplete')._renderItem = function(ul, item) {
        return $('

')
       .data('item.autocomplete', item)
       .append(item.desc)
       .appendTo(ul);
    };
});

Or there is an example on the jQuery site using remote JSONP as a source and customizes the label and value needed to populate the list.

A: Well, the only way I can think of to do this is by using php with fopen and fread, then format the data.

redpajama_set_name: RedPajamaStackExchange


The problem with a lot of corporate training is that employees don't retain the information for long, let alone apply it to their work. While skilled instructional designers can overcome this, businesses that need to implement just-in-time training often rely on in-house experts who lack training backgrounds. Fortunately, you can efficiently improve learning outcomes in corporate training by applying these simple instructional design principles based on cognitive science.


Quartz is a powerful stone that harmonizes and balances. It enhances energy and thoughts, and purifies the spiritual, mental, and physical. It is also a powerfully protective stone, bringing the purified energy in. Historically this crystal has been used to counter black magic, to perform diagnostic healing, and to communicate with spirits and other worlds.


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Vatican - 2 Euro, Caravaggio, 2021 (blister)
VATICAN 2 EURO COMMEMORATIVE COIN 2021: 450th Anniversary of the Birth of Caravaggio. Michelangelo Merisi, who was born in 1571 and died in 1610, was called "il Caravaggio" after the small town in the province of Bergamo where he lived as a child. His painting is one of the highest expressions of art of all time, both shocking and exciting. To celebrate the 450th anniversary of his birth, the Vatican City State Mint has produced a two euro coin in a double version in FDC and proof with engraving work by artist Chiara Principe. The image on the coin shows the painting entitled "Boy with a Basket of Fruit", a work made by Caravaggio in 1593 and exhibited in the halls of the Borghese Gallery in Rome. Caravaggio made this painting during his stay in Rome after having worked as an apprentice in Simone Peterzano's workshop. Although the work is an early one, it is already extraordinary for its analytical precision and the naturalistic sensitivity shown in the depiction of the basket of fruit.
Lithuania - 2 Euro, DZUKIJA, 2021
Finland – 2 Euro, Aland Autonomy, 2021
France - 2 Euro, 75 years of UNICEF, 2021
Greece - 100 Euro gold, OLYMPIAN GODDESS APHRODITE, 2021


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Ohioans got short shrift as political map fight dragged on
By JULIE CARR SMYTH The Associated Press
FILE—Members of the Ohio Senate Government Oversight Committee hear testimony on one of the new map of state congressional districts in this file photo from, Nov. 16, 2021, at the Ohio Statehouse in Columbus, Ohio. Accusations have flown for months over whose delays are most to blame for Ohio's redistricting predicament, a mess of a political mapmaking fight that's left the state with unsettled political boundaries and no date for its Statehouse primaries. (AP Photo/Julie Carr Smyth)[ASSOCIATED PRESS/Julie Carr Smyth]
COLUMBUS, Ohio (AP) — Accusations have flown for months over who's to blame for Ohio's protracted redistricting predicament — a mess of a political mapmaking fight that's left the state without settled political maps and voters without a day for electing party nominees to Statehouse seats.
Voting rights groups blame Republicans at the Statehouse. GOP lawmakers blame national Democrats and the Ohio Supreme Court. The court implicitly faults the Ohio Redistricting Commission, which has taken its time by submitting repeated maps that were deemed unconstitutionally gerrymandered in favor of Republicans. Commissioners point fingers at the justices, special interest groups and the initial census delays.
An Associated Press review settled on one key finding: After hundreds of days of time with government statisticians, lawyers, judges and politicians, the public was the group given short shrift. The public airing of the legislative and congressional maps combined included a scant 64 days for their input.
The AP's look at how many days the maps have spent with various groups is based on legislative activity, court dockets, commission minutes and other reporting, and was verified using calendars, emails and other documents obtained through public records requests. Because of overlapping processes, these totals, which include weekends, exceed the actual 419 days that elapsed as of Tuesday since states were to receive their redistricting data.
CENSUS BUREAU: 134 days
FILE — One of the new map of Ohio congressional districts sits on display during a committee hearing in this file photo from, Nov. 16, 2021, at the Ohio Statehouse in Columbus, Ohio. Accusations have flown for months over whose delays are most to blame for Ohio's redistricting predicament, a mess of a political mapmaking fight that's left the state with unsettled political boundaries and no date for its Statehouse primaries. (AP Photo/Julie Carr Smyth)
Photo: ASSOCIATED PRESS/Julie Carr Smyth
The single largest delay in the process was caused by the Census Bureau, whose decennial head count of Americans ran into logistical snags due to the coronavirus pandemic. Those problems began under Republican President Donald Trump and continued under his successor Joe Biden, a Democrat.
Trump initially appeared to support a Census Bureau request to delay the release of redistricting data from March 31 to July 31, 2021. At the time, he said, "This is called an act of God. This is called a situation that has to be. They have to give it." Later, Republicans in the Senate stymied the request after a memorandum from Trump in the spring, which ordered that people in the country illegally be excluded from the apportionment count.
Under Biden, the bureau announced states wouldn't get redistricting data until Sept. 30. Ohio sued. Republican Attorney General Dave Yost accused the now-Democratic administration of trying "to drag its feet and bog this down in court." A settlement promised the information by Aug. 16, 2021. It arrived Aug. 12. State-hired experts turned the data around to lawmakers within a day.
GOP LAWMAKERS: 81 days
Lawmakers were to begin drawing a new congressional map right away. They didn't. After Democrats nixed a GOP proposal to constitutionally delay Ohio's redistricting timeline, majority Republicans chose to run out the clock on their part of the congressional mapmaking process, eating up 48 days. Later, House Republicans created a map on a Sunday and held it back until a public hearing that Wednesday. After the map was tossed, the Legislature had another 30 days to act. It did not.
REDISTRICTING COMMISSION: 103 days
The special map-drawing commission created by Ohio voters convened Aug. 6, 2021, to be ready for census information to drop the following week. Once the data arrived, though, the commission often wasn't in a hurry, public records show.
With Republican legislative leaders often holding sway, commission's lags included 10 days between the census data's arrival and the first field hearing on legislative maps, six days spent responding to court deadlines and a combined 43 days between various court rulings and its subsequent public activity. The tally also includes 28 days of inaction before approval of the first congressional map and 16 days of inaction before beginning its part of the work on a second map.
Albeit, during the latter span, the commission was at work fixing invalidated legislative maps and under tight court deadlines. But the lapse on congressional maps is still included here, since earlier legislative and commission inaction contributed to the overlap.
INTEREST GROUPS: 39 days
This total includes elapsed days between commission map approvals and the lawsuits or objections next filed by Democratic and voting rights groups, including the National Democratic Redistricting Committee led by former Attorney General Eric Holder, the ACLU, League of Women Voters, Ohio Organizing Collaborative and others. The total excludes happenings in a related federal lawsuit, which didn't impact the overarching timeline.
After a 20-day window between the commission's second congressional map and the filing of the ACLU's objection, GOP Secretary of State Frank LaRose — the state's elections chief and a redistricting commissioner — accused national Democrats and the Biden administration of a "time-eating litigation strategy" and Supreme Court justices of "dawdling."
When the League suspended its challenge to the congressional maps, it pointed back to Republicans' control of the calendar.
"Even we had no idea that after we won time and time and time again in court, that we would still see this play of running out the clock," Executive Director Jen Miller told reporters. "But that's what they've done. And it's awful, it's gross, in my opinion."
LEGAL ARGUMENTS: 183 days
The court wrangling is the most difficult to quantify, and the most inflated by overlaps in the congressional and legislative cases. It also begs questions involving blame. Are the suing parties responsible for these growing time spans? Are justices, who control briefing timetables? Or are commissioners responsible, after twice returning identical or near-identical maps to the court?
Here, the AP opted to include days of court activity that preceded oral arguments in the two redistricting fights, as well as periods of volleying briefs not easily attributable to a single group. Together, that added to 183 days — and counting.
Dockets show that, as opposing parties jockeyed for legal advantage, the Republicans' public attacks over timing lags often conflicted with their lawyers' arguments in court against haste. On both legislative and congressional maps, attorneys for the commission and for Republican officeholders who sit on the panel fought expedited evidence-gathering and briefing schedules, and maximized deadline windows provided to them by the court.
After chastising the 20 days it took the ACLU to object to congressional maps, and seeing the court punt arguments in the congressional case beyond the May 3 primary, LaRose told the court there "is simply no reason to expedite" the schedule. "Petitioners seem to doubt whether this Court can efficiently adjudicate their claims under a normal schedule," he wrote. Republican legislative leaders told the court the "quick schedule" that litigants proposed would only "confuse voters."
JUSTICES: 95 days
Ohio Supreme Court deliberations on redistricting cases had consumed 95 days, as of Tuesday. That represents time between oral arguments or various briefing deadlines and justices' final rulings. Like the redistricting commission, justices oftentimes were working two separate map processes simultaneously, but the days are counted here as distinct.
The longest stretch was the 35 days it took the court to rule on the commission's first set of legislative maps, a period during which justices went back to the parties to clarify additional legal arguments. In that decision, majority justices gave commissioners just 10 days to come up with new maps. The timing irony angered Statehouse Republicans, who later would begin talking about impeaching Chief Justice Maureen O'Connor, a fellow Republican. Subsequent rulings invalidating legislative and congressional maps have generally taken about two weeks. A decision on the latest round of legislative maps could come this week.
THE PUBLIC: 64 days
When all regional information-gathering sessions, legislative hearings, commission meetings and livestreamed mapmaking are combined, public activity on Ohio's new political maps took place on just 64 days. Granted, alongside those opportunities for public input, the commission's website was accepting independent map proposals all along the way. But an accounting of the days spent on Ohio's protracted redistricting process finds average Ohioans got less time than census number-crunchers, politicians, lawyers or justices.


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Home/Celebrity Net Worth/Athletes/Nazr Mohammed Net Worth
Nazr Mohammed Net Worth
Nazr Mohammed Net Worth 2022: Wiki Biography, Married, Family, Measurements, Height, Salary, Relationships
Nazr Mohammed net worth is
Nazr Mohammed salary is
Nazr Mohammed Wiki Biography
Nazr Tahiru Mohammed was born on the 5th September 1977 in Chicago, Illinois USA, and is a professional basketball player who last played for the Oklahoma City Thunder (2015-2016), and previously with teams including the Philadelphia 76ers (1998-2001), Atlanta Hawks (2001-2004), San Antonio Spurs (2005-2006), and Chicago Bulls (2012-2015), among others. His career began in 1998.
Have you ever wondered how rich Nazr Mohammed is, as of early 2017? According to authoritative sources, it has been estimated that Mohammed's net worth is as high as $25 million, earned largely through his successful career as basketball player.
Nazr Mohammed Net Worth $25 Million
Mohammed is of Ghanaian ancestry, but was raised in Chicago, where his father settled after immigrating from Ghana. He went to Kenwood Academy, from which he matriculated in 1995, then enrolled at the University of Kentucky, and in the first season began playing basketball for the Wildcats, however, his playing time was severely limited due to his weight of 315 pounds. In the first season, Wildcats won the NCAA Championship, and in the second they reached finals, but lost to the Arizona University.However, in the second and third seasons Nazr's playing time increased, as he shared the center position with Jamaal Magloire, and the two led the Kentucky Wildcats to the second NCAA Championship in 1998.
After the successful 1998 season, Nazr declared for the NBA Draft, and was selected by the Utah Jazz as the 29th overall pick, however, his rights were traded to the Philadelphia 76ers.
Unfortunately, Nazr's skills weren't in frequent use by then head coach Larry Brown, since Nazr played only around five minutes in 26 games, and managed to average 1.6 points per game, and 1.4 rebounds. After two and a half unsuccessful seasons at Philadelphia, Mohammed was traded to the Atlanta Hawks, where in his first season he played in 28 games, 19 of which were starts, and averaged his career high with 12.3 points and 9.0 rebounds per game. During the 2001-2002 season, Nazr played in all 82 games, 73 in the starting line-up, and posted 9.7 points per game, and 7.9 rebounds, which secured him a new contract with the team that only increased his net worth. Unfortunately, 2002-2003 and 2003-2004 seasons weren't so successful for Nazr, which resulted in a trade to the New York Knicks, where his stint lasted for a season and a half, after which he was traded to the San Antonio Spurs. In his first season with the new team, Nazr won the NBA Championship, and helped his team with 6.2 points, and 5.2 rebounds per game. After the Spurs, Mohammed spent two years in Detroit, playing for the Pistons, and then four seasons for the Charlotte Hornets, and from 2010 until 2012 he played for the Oklahoma City Thunders, after which Nazr joined the Chicago Bulls, and played for them until 2015, then decided to retire, however, he played five more games for the Oklahoma City Thunders during 2015-2016 season.
Regarding his personal life, Nazr has been married to Mandy since 2001, and the couple has three children. Over the years he became known for his Muslim faith, and he fasts for Ramadan.
Having achieved fame and fortune, Nazr wanted to repay to the community, and he started the Nazr Mohammed Foundation, through which he has organized numerous fundraisers for various charitable organizations and causes.
Salary $352,750
Date Of Birth September 5, 1977
Place Of Birth Chicago, Illinois, United States
Profession Basketball player
Education University of Kentucky
Spouse Mandy Mohammed
Children Amani Mohammed, Nasir Mohammed, Sanaa Mohammed
Parents Alhaji Na T. A. Mohammed, Mary Mohammed
Siblings Alhaji Mohammed
Facebook https://www.facebook.com/nazr
Twitter https://twitter.com/nazrmohammed
Gone in an Instant 2015 Documentary Himself
The 2005 NBA Finals 2005 TV Mini-Series Himself - San Antonio Spurs
$25 Million $352 $750 113 kg 113.4 1977 1977-09-05 2.08 Actress Alhaji Mohammed Alhaji Na T. A. Mohammed Amani Mohammed American Basketball player Chicago Illinois Jamaal Magloire Larry Brown Mandy Mohammed Mary Mohammed Nasir Mohammed Nazr Mohammed Nazr Mohammed Net Worth Sanaa Mohammed September 5 United States University of Kentucky
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Abu Dhabi climbs to sixth place in global oil & gas reserves ranking
Abu Dhabi has become the world's sixth-largest nation by oil and gas reserves, Abu Dhabi's Supreme Petroleum Council announced at a meeting earlier this week.
Illustration: An ADNOC offshore platform complex offshore Abu Dhabi / Image source: ADNOC
The SPC announced increases in the country's hydrocarbon reserves of 7 billion STB of oil and 58 TSCF of conventional gas, moving the UAE from seventh to the sixth position in both global oil and gas reserves ranking with a total of 105 billion STB of recoverable oil and 273 trillion SCF of conventional gas.
"These significant additions to the UAE's hydrocarbon reserves mark a historic milestone for the country since the last major update of its reserves base three decades ago and underpin its position as a stable and long-term energy provider to the world," Abu Dhabi's national oil company ADNOC said in a statement.
In addition, the Supreme Petroleum Council announced the discovery of unconventional recoverable gas resources totaling 160 TSCF. According to the SPC, this is a first for the region, "ushering in a new era of large scale unconventional hydrocarbon resources development."
"The increase in reserves and new discoveries follows ADNOC's extensive drilling of dozens of new exploration and appraisal wells across Abu Dhabi's conventional and unconventional oil and gas fields over the past few years. In addition, the start of production in new fields, for example, SARB, Umm Lulu, Haliba, and Bu Haseer, has enabled ADNOC to verify and further upgrade the originally expected reserves' volumes," SPC said.
SPC also added that the ongoing maturation of ADNOC's field development plans towards its 5 million barrels per day (mmbpd) oil production capacity target by 2030, and expanded oil recovery schemes, as well as ongoing in-fill drilling, have provided the base for an increase in ADNOC's reserves.
Also, if you're interested in showcasing your company, product, or technology on Offshore Energy Today, please contact us via our advertising form, where you can also see our media kit.
Posted on November 6, 2019 with tags abu dhabi, ADNOC.
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Santiago Castillo
Quick Facts of Santiago Castillo
Social Media of : Santiago Castillo
Santiago Castillo is a TikTok star who is famous for his interesting and fun lip-sync videos on an app called Musically. He is also available on other social media platforms like Instagram, twitch, Snapchat, etc.
Santiago was born on June 9, 2003, in Venezuela. There is no information about his father's and mother's names. Likewise, he has shared a photo of his family where he has two brothers. Recently he moved to ST. Petersburg Florida, USA. He hasn't revealed his religion and ethnicity. His Horoscope is Gemini. His nationality is Venezuelan. Currently, he is 19 years old.
Talking about his educational career there is no information about where he attended his school but right now, he is in High school. He is also a very passionate football player.
By profession, he is a TikTok star and a social media personality who is famous for his interesting and fun lip-sync video due to which he has got a huge fan following. He has been provided the Title of "Ace TikTok Comedian due to his lip-sync video. He has also been able to Label as a "Muser" for his ability to build a large fan following on various social media accounts.
As a TikTok star, he has collaborated with many other Tik Tokers like Kevin Gibbons, Ell, Terin Joseph, Kathryn, etc., and many others. In the year 2019, He cosplayed Joaquin Phoenix "The Joker" for Halloween. Along with that he also created a duet video with Wow Hadiya.
Looking at his personal life he is single right now as he is more focused on his career rather than being in any kind of relationship. Along with that, he doesn't share much information about his personal life. There is no information about his past relationships and affairs. He hasn't been involved in any kinds of controversy and scandals and has been able to maintain good character among the people.
Awards and Net worth
There is no information of him getting any kinds of awards till now, but he might win in his upcoming years as he is a very talented TikTok content creator. Likewise, he has a large fan following.
As we talk about his salary and Net worth there is no information about him getting any kind of salary, but his estimated net worth is around $5 million US dollars. He generates money through his TikTok, brand Endorsements, etc.
Body Measurement and social media
No information about his body measurement, weight, Dress size, Shoe size, Biceps, Chest Size, etc. as they are still under review. Looking at his height he has a height of around 5 feet 8 inches. He has got a brown eye as same as his hair color.
He is active on social media like Instagram and TikTok where he has got 60k followers on Instagram and 1.3 million followers on TikTok.
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Q: Sort Array of random numbers using insertion sort Trouble Shooting My insertionSort
Here is my code...
        ArrayList<Integer> num2 = new ArrayList<Integer>();
        for (int b = 0; b < 300; b++)
        {
            num2.add((int)(Math.random() * 1000));
        }

        if (choice == 2)
        {
            System.out.println("Insertion Sort chosen\nUnsorted Array List: " + num2);
            insertionSort(num2);
            System.out.println("Insertion sorted Array List: " + num2);
        }//choice 2 end

public static void insertionSort(ArrayList<Integer> num2)
{
    for(int f = 1; f < num2.size(); f++)
    {
        int key = num2.get(f);
        for (int g = f-1; g >= 0; g--)
        {
            if (key < num2.get(g))
            {
                num2.set(g+1, num2.get(g));
                if (g == 0)
                {
                    num2.set(0, key);
                }
            }
            else
            {
                num2.set(g+1, key);
            }
        }
    }
}

Output:
Insertion Sort chosen
Unsorted Array List: [42, 944, 500, 442, 277, 178, 881, 267, 705, 365, 481, 882, 221, 835, 329, 312, 97, 1,...
Insertion sorted Array List: [1, 479, 479, 479, 479, 718, 785, 785, 988, 988, 988, 988, 988, 988, 988, 988, 988,...
if anyone can help me figure out why a bunch of the larger numbers just start repeating themselves because it is frustrating.

A: When you cannot find where the problems is, you can make use of the breakpoint to help you. Set one or several breakpoints where you think the codes here are critical. For example, when you debug your insertionSort function, you can set your breakpoint at Line 5 to see what changes the inner loop have made on your array. And you will find that the inner loop have done something outside your expectation. Then you can continue to set the breakpoint inside the inner loop to see where the problem is.
Actually, your problem is that when you find the right position to put your key, you should break the inner loop. Otherwise, since the key is larger than all the numbers before the proper position, all the numbers before except the first one (since your code is num2.set(g+1, key);, and when g = 0, it changes the second element) will be replaced by the key.
So, you only need to add a break.
public static void insertionSort(ArrayList<Integer> num2)
{
    for(int f = 1; f < num2.size(); f++)
    {
        int key = num2.get(f);
        for (int g = f-1; g >= 0; g--)
        {
            if (key < num2.get(g))
            {
                num2.set(g+1, num2.get(g));
                if (g == 0)
                {
                    num2.set(0, key);
                }
            }
            else
            {
                num2.set(g+1, key);
                break;
            }
        }
    }
}


We're off to a great start this year, and I look forward to getting to know all of you!
Both items will be kept in class and needed every day.
I am unable to click on the form to fill out for my child – Blythe Cohen 6th/Kilgore. I appreciate any direction so I can complete the form.