Seven pitfalls of using data science in cybersecurity

Machine learning, a subset of artificial intelligence, is used for many problems where a data-driven approach is required and the problem space involves either classification or prediction. The hype surrounding machine learning, coupled with the ease of use of machine learning tools can lead to a (mistaken) belief that machine learning is a panacea for all problems and simply feeding large volumes of data to an algorithm will generate a sensible and usable answer. In this chapter, we explore several pitfalls that a data scientist must evaluate in order to obtain some tangible meaning from the results provided by a machine learning algorithm. There is some evidence to suggest that algorithm choice is not a discriminator. In particular, we explore the importance of feature set selection and evaluate the inherent problems in relying on synthetic data

Botnet detection techniques: review, future trends, and issues

NoIn recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.University of Malaya, Malaysia (No. FP034-2012A