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%

A Distributed Architecture for Spam Mitigation on 4G Mobile Networks

The 4G of mobile networks is considered a technology-opportunistic and user-centric system combining the economical and technological advantages of various transmission technologies. Part of its new architecture dubbed as the System Architecture Evolution, 4G mobile networks will implement an evolved packet core. Although this will provide various critical advantages, it will however expose telecom networks to serious IP-based attacks. One often adopted solution by the industry to mitigate such attacks is based on a centralized security architecture. This centralized approach nonetheless, requires large processing resources to handle huge amount of traffic, which results in a significant over dimensioning problem in the centralized nodes causing this approach to fail from achieving its security task.\\ In this thesis, we primarily contribute by highlighting on two Spam flooding attacks, namely RTP VoIP SPIT and SMTP SPAM and demonstrating, through simulations and comparisons, their feasibility and DoS impact on 4G mobile networks and subsequent effects on mobile network operators. We further contribute by proposing a distributed architecture on the mobile architecture that is secure by mitigating those attacks, efficient by solving the over dimensioning problem and cost-effective by utilizing `off the shelf' low-cost hardware in the distributed nodes. Through additional simulation and analysis, we reveal the viability and effectiveness of our approach

Introduction to the Volume on Digital Media, Youth, and Credibility

This chapter argues that understanding credibility is particularly complex -- and consequential -- in the digital media environment, especially for youth audiences, who have both advantages and disadvantages due to their relationship with contemporary technologies and their life experience. The chapter explains what is, and what is not, new about credibility in the context of digital media and discusses the major thrusts of current credibility concerns for scholars, educators, and youth

Applied Machine Learning for Cybersecurity in Spam Filtering and Malware Detection

Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict the malicious attacks. This research emphasizes two vulnerable areas of cybersecurity that could be easily exploited. First, we show that spam filtering is a well known problem that has been addressed by many authors, yet it still has vulnerabilities. Second, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. Nonetheless, AutoAI is not perfect, and data scientists can still design better models. In this thesis I show that although there are efficient mechanisms to prevent malicious attacks, there are still vulnerabilities that could be easily exploited. In the visual spoofing experiment, we show that using a classifier trained on data using Latin alphabet, to classify a message with a combination of Latin and Cyrillic letters leads to much lower classification accuracy. In Malware prediction experiment, our model has been able to predict malware attacks on Microsoft computers and got higher accuracy than any well known Auto AI

Architectures for the Future Networks and the Next Generation Internet: A Survey

Networking research funding agencies in the USA, Europe, Japan, and other countries are encouraging research on revolutionary networking architectures that may or may not be bound by the restrictions of the current TCP/IP based Internet. We present a comprehensive survey of such research projects and activities. The topics covered include various testbeds for experimentations for new architectures, new security mechanisms, content delivery mechanisms, management and control frameworks, service architectures, and routing mechanisms. Delay/Disruption tolerant networks, which allow communications even when complete end-to-end path is not available, are also discussed

A framework to evaluate user experience of end user application security features

The use of technology in society moved from satisfying the technical needs of users to giving a lasting user experience while interacting with the technology. The continuous technological advancements have led to a diversity of emerging security concerns. It is necessary to balance security issues with user interaction. As such, designers have adapted to this reality by practising user centred design during product development to cater for the experiential needs of user - product interaction. These User Centred Design best practices and standards ensure that security features are incorporated within End User Programs (EUP). The primary function of EUP is not security, and interaction with security features while performing a program related task does present the end user with an extra burden. Evaluation mechanisms exist to enumerate the performance of the EUP and the user’s experience of the product interaction. Security evaluation standards focus on the program code security as well as on security functionalities of programs designed for security. However, little attention has been paid to evaluating user experience of functionalities offered by embedded security features. A qualitative case study research using problem based and design science research approaches was used to address the lack of criteria to evaluate user experience with embedded security features. User study findings reflect poor user experience with EUP security features, mainly as a result of low awareness of their existence, their location and sometimes even of their importance. From the literature review of the information security and user experience domains and the user study survey findings, four components of the framework were identified, namely: end user characteristics, information security, user experience and end user program security features characteristics. This thesis focuses on developing a framework that can be used to evaluate the user experience of interacting with end user program security features. The framework was designed following the design science research method and was reviewed by peers and experts for its suitability to address the problem. Subject experts in the fields of information security and human computer interaction were engaged, as the research is multidisciplinary. This thesis contributes to the body of knowledge on information security and on user experience elements of human computer interaction security regarding how to evaluate user experience of embedded InfoSec features. The research adds uniquely to the literature in the area of Human Computer Interaction Security evaluation and measurement in general, and is specific to end user program security features. The proposed metrics for evaluating UX of interacting with EUP security features were used to propose intervention to influence UX in an academic setup. The framework, besides presenting UX evaluation strategies for EUP security features, also presents a platform for further academic research on human factors of information security. The impact can be evaluated by assessing security behaviour, and successful security breaches, as well as user experience of interaction with end user programs