Uniting cyber security and machine learning: Advantages, challenges and future research

Abstract

Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions.