Antyscam – practical web spam classifier

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.

Detecting cyber threats through social network analysis: short survey

This article considers a short survey of basic methods of social networks analysis, which are used for detecting cyber threats. The main types of social network threats are presented. Basic methods of graph theory and data mining, that deals with social networks analysis are described. Typical security tasks of social network analysis, such as community detection in network, detection of leaders in communities, detection experts in networks, clustering text information and others are considered

Review of the machine learning methods in the classification of phishing attack

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail

Improved techniques for phishing email detection based on random forest and firefly-based support vector machine learning algorithms.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2014.Electronic fraud is one of the major challenges faced by the vast majority of online internet users today. Curbing this menace is not an easy task, primarily because of the rapid rate at which fraudsters change their mode of attack. Many techniques have been proposed in the academic literature to handle e-fraud. Some of them include: blacklist, whitelist, and machine learning (ML) based techniques. Among all these techniques, ML-based techniques have proven to be the most efficient, because of their ability to detect new fraudulent attacks as they appear.There are three commonly perpetrated electronic frauds, namely: email spam, phishing and network intrusion. Among these three, more financial loss has been incurred owing to phishing attacks. This research investigates and reports the use of MLand Nature Inspired technique in the domain of phishing detection, with the foremost objective of developing a dynamic and robust phishing email classifier with improved classification accuracy and reduced processing time.Two approaches to phishing email detection are proposed, and two email classifiers are developed based on the proposed approaches. In the first approach, a random forest algorithm is used to construct decision trees,which are,in turn,used for email classification. The second approach introduced a novel MLmethod that hybridizes firefly algorithm (FFA) and support vector machine (SVM). The hybridized method consists of three major stages: feature extraction phase, hyper-parameter selection phase and email classification phase. In the feature extraction phase, the feature vectors of all the features described in Section 3.6 are extracted and saved in a file for easy access.In the second stage, a novel hyper-parameter search algorithm, developed in this research, is used to generate exponentially growing sequence of paired C and Gamma (γ) values. FFA is then used to optimize the generated SVM hyper-parameters and to also find the best hyper-parameter pair. Finally, in the third phase, SVM is used to carry out the classification. This new approach addresses the problem of hyper-parameter optimization in SVM, and in turn, improves the classification speed and accuracy of SVM. Using two publicly available email datasets, some experiments are performed to evaluate the performance of the two proposed phishing email detection techniques. During the evaluation of each approach, a set of features (well suited for phishing detection) are extracted from the training dataset and used to constructthe classifiers. Thereafter, the trained classifiers are evaluated on the test dataset. The evaluations produced very good results. The RF-based classifier yielded a classification accuracy of 99.70%, a FP rate of 0.06% and a FN rate of 2.50%. Also, the hybridized classifier (known as FFA_SVM) produced a classification accuracy of 99.99%, a FP rate of 0.01% and a FN rate of 0.00%

Intelligent instance selection techniques for support vector machine speed optimization with application to e-fraud detection.

Doctor of Philosophy in Computer Science. University of KwaZulu-Natal, Durban 2017.Decision-making is a very important aspect of many businesses. There are grievous penalties involved in wrong decisions, including financial loss, damage of company reputation and reduction in company productivity. Hence, it is of dire importance that managers make the right decisions. Machine Learning (ML) simplifies the process of decision making: it helps to discover useful patterns from historical data, which can be used for meaningful decision-making. The ability to make strategic and meaningful decisions is dependent on the reliability of data. Currently, many organizations are overwhelmed with vast amounts of data, and unfortunately, ML algorithms cannot effectively handle large datasets. This thesis therefore proposes seven filter-based and five wrapper-based intelligent instance selection techniques for optimizing the speed and predictive accuracy of ML algorithms, with a particular focus on Support Vector Machine (SVM). Also, this thesis proposes a novel fitness function for instance selection. The primary difference between the filter-based and wrapper-based technique is in their method of selection. The filter-based techniques utilizes the proposed fitness function for selection, while the wrapper-based technique utilizes SVM algorithm for selection. The proposed techniques are obtained by fusing SVM algorithm with the following Nature Inspired algorithms: flower pollination algorithm, social spider algorithm, firefly algorithm, cuckoo search algorithm and bat algorithm. Also, two of the filter-based techniques are boundary detection algorithms, inspired by edge detection in image processing and edge selection in ant colony optimization. Two different sets of experiments were performed in order to evaluate the performance of the proposed techniques (wrapper-based and filter-based). All experiments were performed on four datasets containing three popular e-fraud types: credit card fraud, email spam and phishing email. In addition, experiments were performed on 20 datasets provided by the well-known UCI data repository. The results show that the proposed filter-based techniques excellently improved SVM training speed in 100% (24 out of 24) of the datasets used for evaluation, without significantly affecting SVM classification quality. Moreover, experimental results also show that the wrapper-based techniques consistently improved SVM predictive accuracy in 78% (18 out of 23) of the datasets used for evaluation and simultaneously improved SVM training speed in all cases. Furthermore, two different statistical tests were conducted to further validate the credibility of the results: Freidman’s test and Holm’s post-hoc test. The statistical test results reveal that the proposed filter-based and wrapper-based techniques are significantly faster, compared to standard SVM and some existing instance selection techniques, in all cases. Moreover, statistical test results also reveal that Cuckoo Search Instance Selection Algorithm outperform all the proposed techniques, in terms of speed. Overall, the proposed techniques have proven to be fast and accurate ML-based e-fraud detection techniques, with improved training speed, predictive accuracy and storage reduction. In real life application, such as video surveillance and intrusion detection systems, that require a classifier to be trained very quickly for speedy classification of new target concepts, the filter-based techniques provide the best solutions; while the wrapper-based techniques are better suited for applications, such as email filters, that are very sensitive to slight changes in predictive accuracy

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble