SNARE: Spatio-temporal Network-level Automatic Reputation Engine

Current spam filtering techniques classify email based on content and IP reputation blacklists or whitelists. Unfortunately, spammers can alter spam content to evade content based filters, and spammers continually change the IP addresses from which they send spam. Previous work has suggested that filters based on network-level behavior might be more efficient and robust, by making decisions based on how messages are sent, as opposed to what is being sent or who is sending them. This paper presents a technique to identify spammers based on features that exploit the network-level spatio temporal behavior of email senders to differentiate the spamming IPs from legitimate senders. Our behavioral classifier has two benefits: (1) it is early (i.e., it can automatically detect spam without seeing a large amount of email from a sending IP address-sometimes even upon seeing only a single packet); (2) it is evasion-resistant (i.e., it is based on spatial and temporal features that are difficult for a sender to change). We build classifiers based on these features using two different machine learning methods, support vector machine and decision trees, and we study the efficacy of these classifiers using labeled data from a deployed commercial spam-filtering system. Surprisingly, using only features from a single IP packet header (i.e., without looking at packet contents), our classifier can identify spammers with about 93% accuracy and a reasonably low false-positive rate (about 7%). After looking at a single message spammer identification accuracy improves to more than 94% with a false rate of just over 5%. These suggest an effective sender reputation mechanism

Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics

Survey on highly imbalanced multi-class data

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multi-class data

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%

Phishing Detection Using Natural Language Processing and Machine Learning

Phishing emails are a primary mode of entry for attackers into an organization. A successful phishing attempt leads to unauthorized access to sensitive information and systems. However, automatically identifying phishing emails is often difficult since many phishing emails have composite features such as body text and metadata that are nearly indistinguishable from valid emails. This paper presents a novel machine learning-based framework, the DARTH framework, that characterizes and combines multiple models, with one model for each composite feature, that enables the accurate identification of phishing emails. The framework analyses each composite feature independently utilizing a multi-faceted approach using Natural Language Processing (NLP) and neural network-based techniques and combines the results of these analyses to classify the emails as malicious or legitimate. Utilizing the framework on more than 150,000 emails and training data from multiple sources, including the authors’ emails and phishtank.com, resulted in the precision (correct identification of malicious observations to the total prediction of malicious observations) of 99.97% with an f-score of 99.98% and accurately identifying phishing emails 99.98% of the time. Utilizing multiple machine learning techniques combined in an ensemble approach across a range of composite features yields highly accurate identification of phishing emails

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields