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Comparison of classification algorithms for Detection of Phishing Websites

INFORMATICA 2020年第1期31卷 143-160页

作者： Vaitkevicius, Paulius Marcinkevicius, Virginijus Vilnius Univ Inst Data Sci & Digital Technol Akad Str 4 LT-08412 Vilnius Lithuania

Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

关键词： phishing detection classification algorithms phishing datasets

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A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada's Arctic

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2015年第8期36卷 2252-2276页

作者： He, J. Harris, J. R. Sawada, M. Behnia, P. Univ Ottawa Lab Appl Geomat & GIS Sci Ottawa ON Canada Geol Survey Canada Ottawa ON Canada Geofirma Ltd Ottawa ON Canada

To map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to Maximum Likelihood Classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. classification performance was evaluated visually and statistically using the global classification accuracy, producer's and user's accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Rescaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.

关键词： classification algorithms LANDSAT satellites COMPARATIVE studies PETROLOGY PERFORMANCE evaluation

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Detection of Hazardous Liquids Using Microwave Data and Well-Known classification algorithms

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RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING 2020年第9期56卷 742-751页

作者： Efeoglu, Ebru Tuna, Gurkan Trakya Univ Edirne Turkey

The recent increase in terrorist attacks realized using liquid explosives has made it important to develop quick and reliable methods that can distinguish between nonhazardous liquids and other liquids that can be used in these explosives. Since the stability and sensitivity properties of microwave systems are high, microwave frequency band is preferred to differentiate hazardous liquids from non-hazardous liquids. In this study, a noncontact system based on electromagnetic response measurements of liquids in microwave frequency band is proposed to develop a classification approach that can be used in liquid scanners. Naive Bayes, linear discriminant analysis, qualitative data analysis, support vector machine, sequential minimal optimization, K-nearest neighbors classification algorithms are used to classify liquids and their classification performances are analyzed. The results of the set of classification experiments prove the success of the proposed measurement method. As the results prove, K-nearest neighbors is the most appropriate classification algorithm for hazardous liquid detection. Since it can be easily implemented and its detection process is fast, a classification system based on the proposed approach can be very useful in airports and shopping malls.

关键词： hazardous liquids microwave classification classification algorithms performance

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A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from landsat images

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PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 2002年第9期68卷 905-911页

作者： Melesse, AM Jordan, JD Univ Florida Dept Agr & Biol Engn Ctr Remote Sensing IFAS Gainesville FL 32611 USA

Satellite images are the most important source of land-cover data over a large range of temporal and spatial scales. However, the complete realization of satellite imagery as a source of land-cover information is limited by the presence of contaminants such as cloud and associated shadows in the image. These contaminants are not adequately handled with conventional image classification techniques such as the unsupervised maximum-likelihood technique. This study comprises a comparison of two classification algorithms, the fuzzy technique and an augmented form of the Iterative Self-Organizing Data Analysis (ISODATA) technique, which were used to discriminate low-altitude clouds and their shadows on a Landsat Thematic Mapper (TM) image of the Econlockhatchee River basin (Econ), in central Florida, Preliminary conventional unsupervised maxim um-likelihood classification of the image resulted in clouds being mixed with built-ups and shadows being mixed with water bodies. Regions containing these two kinds of mixed categories were first masked, then fuzzy and augmented ISODATA classifications were performed on them. The ISODATA classification algorithm was run on the TM visible/shortwave bands and augmented with scatter diagrams of surface temperature versus several vegetation indices;the fuzzy, algorithm was run on TM bands 1 through 5 and band 7. An accuracy assessment of the techniques was carried out using 40 randomly selected points within the image. Results of the classifications showed that both algorithms successfully discriminated clouds from other bright features, and shadows from other dark features, with an overall accuracy of greater than 80 percent.

关键词： .bands shadows fuzzy Isodata scatter diagrams classification algorithms Cloud-Shadow built-ups

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A Comprehensive Analysis of the Success of classification algorithms for the classification of Emotional States Based on the User's Behavioral Characteristics

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IEEE ACCESS 2023年 11卷 24953-24970页

作者： Drzik, David Magdin, M. Constantine Philosopher Univ Nitra Fac Nat Sci & Informat Dept Informat Nitra Slovakia Constantine Philosopher Univ Nitra Fac Nat Sci & Informat Dept Informat Nitra Slovakia

Behavioral characteristics are currently one of the most widely used ways how we can determine a user's emotional state. Authors in the contribution measures the possibilities of classifying the emotional state of the user based on the behavior when working with a computer and with a smartphone. The contribution follow to the results of the original work of the authors, which dealt with the development of the Emotnizer application, data collection and their processing and classification of the four basic emotions found simultaneously in Ekman and Russell's emotional model. The application has been completely redesigned and currently allows the classification of four basic dimensions (anger, joy, sadness and relaxation), while within these dimensions it is able to recognize and classify 20 different emotional states based on valence and arousal. In the article, we compare the results achieved by us (achieved by the 3-fold cross-validation method) with the results of the authors' most similar works from the analysis of the current state. We achieve the highest classification accuracy of up to 87.88% by classifying arousal (low, medium, high) with a neural network algorithm for the second data set. Among the classification results for valence (negative, neutral, positive), we proposed a neural network classifier that works with an accuracy of 85.61%.

关键词： classification algorithms Behavioral sciences Keyboards Task analysis Random forests Anxiety disorders Emotion recognition Behavioral characteristics classification emotional states

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Detecting Arabic Spam Reviews in Social Networks Based on classification algorithms

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2022年第1期21卷 1–13页

作者： Najadat, Hassan Alzubaidi, Mohammad A. Qarqaz, Islam Jordan Univ Sci & Technol Dept Comp Informat Syst Irbid Jordan Yarmouk Univ Comp Engn Dept Irbid 21163 Jordan Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan

Reviews or comments that users leave on social media have great importance for companies and business entities. New product ideas can be evaluated based on customer reactions. However, this use of social media is complicated by those who post spam on social media in the form of reviews and comments. Designing methodologies to automatically detect and block social media spam is complicated by the fact that spammers continuously develop new ways to leave their spam comments. Researchers have proposed several methods to detect English spam reviews. However, few studies have been conducted to detect Arabic spam reviews. This article proposes a keyword-based method for detecting Arabic spam reviews. Keywords or Features are subsets of words from the original text that are labelled as important. A term's weight, Term Frequency-Inverse Document Frequency (TF-IDF) matrix, and filter methods (such as information gain, chi-squared, deviation, correlation, and uncertainty) have been used to extract keywords from Arabic text. The method proposed in this article detects Arabic spam in Facebook comments. The dataset consists of 3,000 Arabic comments extracted from Facebook pages. Four different machine learning algorithms are used in the detection process, including C4.5, kNN, SVM, and Naive Bayes classifiers. The results show that the Decision Tree classifier outperforms the other classification algorithms, with a detection accuracy of 92.63%.

关键词： Arabic language Facebook spam detection social networks classification algorithms

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DDoS Detection System: Using a Set of classification algorithms Controlled by Fuzzy Logic System in Apache Spark

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第3期16卷 936-949页

作者： Alsirhani, Amjad Sampalli, Srinivas Bodorik, Peter Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

Distributed denial of service (DDoS) attacks are a major security threat against the availability of conventional or cloud computing resources. Numerous DDoS attacks, which have been launched against various organizations in the last decade, have had a direct impact on both vendors and users. Many researchers have attempted to tackle the security threat of DDoS attacks by combining classification algorithms with distributed computing. However, their solutions are static in terms of the classification algorithms used. In fact, current DDoS attacks have become so dynamic and sophisticated that they are able to pass the detection system thereby making it difficult for static solutions to detect. In this paper, we propose a dynamic DDoS attack detection system based on three main components: 1) classification algorithms;2) a distributed system;and 3) a fuzzy logic system. Our framework uses fuzzy logic to dynamically select an algorithm from a set of prepared classification algorithms that detect different DDoS patterns. Out of the many candidate classification algorithms, we use Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), and Random Forest as candidate algorithms. We have evaluated the performance of classification algorithms and their delays and validated the fuzzy logic system. We have also evaluated the effectiveness of the distributed system and its impact on the classification algorithms delay. The results show that there is a trade-off between the utilized classification algorithms' accuracies and their delays. We observe that the fuzzy logic system can effectively select the right classification algorithm based on the traffic status.

关键词： DDoS attack DDoS detection fuzzy logic system machine learning classification algorithms apache spark apache Hadoop cloud computing

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Comparison of different classification algorithms in clinical decision-making

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EXPERT SYSTEMS 2007年第1期24卷 17-31页

作者： Uebeyli, Elif Derya TOBB Ekon Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey

This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.

关键词： classification algorithms automated diagnostic systems clinical decision-making diagnostic accuracy

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Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup1期21卷 2092-2109页

作者： Sen, Baha Peker, Musa Yildirim Beyazit Univ Fac Engn Dept Comp Engn Ankara Turkey Karabuk Univ Fac Engn Dept Comp Engn Karabuk Turkey

This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms. In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

关键词： EEG signals classification algorithms FCBF selection algorithm epilepsy disease

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Nonlinear damage detection using linear ARMA models with classification algorithms

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SMART STRUCTURES AND SYSTEMS 2020年第1期26卷 23-33页

作者： Chen, Liujie Yu, Ling Fu, Jiyang Ng, Ching-Tai Guangzhou Univ Sch Civil Engn Guangzhou Higher Educ Mega Ctr 230 Wai Huan Xi Rd Guangzhou Peoples R China Jinan Univ MOE Key Lab Disaster Forecast & Control Engn 601 West Huangpu Ave Guangzhou Peoples R China Guangzhou Higher Educ Mega Ctr Guangzhou Univ Tamkang Univ Joint Res Ctr Engn St 230 Wai Huan Xi Rd Guangzhou Peoples R China Univ Adelaide Sch Civil Environm & Min Engn North Terrace Adelaide SA 5005 Australia

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

关键词： nonlinear damage detection time series analysis linear autoregressive moving average model vector space cosine similarity classification algorithms

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