Phishing e-mail filtering mechanism using heuristic technique

Phishing is a new type of network attack where the attacker creates a replica of an existing Web page to fool users (e.g., by using specially designed e-mails or instant messages) into submitting personal, financial, or password data to what they think is their service provides’ Web site. In this research paper, I proposed a novel method to phishing email filtering by the use of end-host based anti-phishing algorithm, which is call Link Guard and content based filtering by the use of knowledge discovery by utilizing the generic characteristics of the hyperlinks in phishing attacks. These characteristics are derived by analyzing the phishing data archive provided by the Anti-Phishing Working Group (APWG). Because it is based on the generic characteristics of phishing attacks, Link Guard can detect not only known but also unknown phishing attacks. Our experimental analysis verified that Link Guard is effective to detect and prevent both known and unknown phishing attacks with minimal false negatives. This research also showed that Link Guard is light weighted and can detect and prevent phishing attacks in real time

Systematic Literature Review

Mutemi, A., & Bação, F. (2023). E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review. Big Data Mining and Analytics, 1-27. https://doi.org/10.26599/BDMA.2023.9020023The e-commerce industry's rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, machine learning, and cloud computing have revitalized research and applications in this domain. While machine learning and data mining techniques are popular in fraud detection, specific reviews focusing on their application in ecommerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of machine learning algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key machine learning and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.publishersversionepub_ahead_of_prin

Phishing Detection: Analysis of Visual Similarity Based Approaches

Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently. Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS), image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study. Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches

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%

Diagnosis of the disease using an ant colony gene selection method based on information gain ratio using fuzzy rough sets

With the advancement of metagenome data mining science has become focused on microarrays. Microarrays are datasets with a large number of genes that are usually irrelevant to the output class; hence, the process of gene selection or feature selection is essential. So, it follows that you can remove redundant genes and increase the speed and accuracy of classification. After applying the gene selection, the dataset is reduced and detection of differentially abundant genes facilitated with more accuracy. This will, in turn, increases the power of genes which are correctly detected statistically differentially abundant in two or more phenotypes. The method presented in this study is a two-stage method for functional analysis of metagenomes.  The first stage uses a combination of the filter and wrapper gene selection method, which includes the ant colony algorithm and utilizes fuzzy rough sets to calculate the information gain ratio as an evaluation measure in the ant colony algorithm. The set of features from the first stage is used as input in the second stage, and then the negative binomial distribution is used to detect genes which are statistically differentially abundant in two or more phenotypes. Applying the proposed method on a microarray dataset it becomes clear that the proposed method increases the accuracy of the classifier and selects a subset of genes that have a minimum length and maximum accuracy

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

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

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Cyber Peace

Cyberspace is increasingly vital to the future of humanity and managing it peacefully and sustainably is critical to both security and prosperity in the twenty-first century. These chapters and essays unpack the field of cyber peace by investigating historical and contemporary analogies, in a wide-ranging and accessible Open Access publication

An Investigation into the Critical Success Factors for E-Banking Frauds Prevention in Nigeria

E-Banking frauds is an issue experienced globally and continues to prove costly to both banks and customers. Frauds in e-banking services occur due to various compromises in security, ranging from weak authentication systems to insufficient internal controls. Although some security frameworks to address this issue of fraud have been proposed, the problem of e-banking fraud remains due to the inability of these framework to deal with organisational issues. With limited research in this area, the study sets out to identify the organisational Critical Success Factors (CSF) for E-Banking Frauds Prevention in Nigeria by applying CSF theory. A framework is proposed to help improve security from an organisational perspective. The study adopted a mixture of philosophical paradigms which led to the triangulation of research methods; Literature Review, Survey and Case Studies. The Literature Review involved the synthesis of existing literature and identified potential CSF for frauds prevention in e-banking. A total of 28 factors were identified and a conceptual framework was proposed. A 5-point Likert scale survey questionnaire was sent to retail bank staff in Nigeria to rate the criticality of the factors. A total of 110 useable responses were received at a response rate of 23.9%. Similar interrelated factors were grouped using a Principal Component Analysis. Finally, case studies with 4 banks in Nigeria were carried out to deepen our understanding. The study identified a total of 10 CSF which spanned across strategic, operational and technological factor categories. These included ‘Management Commitment’, ‘Engagement of Subject Matter Experts’ and ‘Multi-Layer Authentication’ amongst others. In addition, new CSF such as ‘Risk-Based Transactional Controls’, ‘People Awareness & Training’ and ‘Bank Agility via Data Driven Decision Making’ were identified. Finally, these CSF were grouped into an e-banking frauds prevention framework. This study is a pioneer study that extends theory to propose a CSF-based frauds prevention framework for banks in Nigeria