Use of machine learning for the detection, identification, and mitigation of cyber-attacks

Abstract

With the increased use of internet services for various public and private purposes, the threat of cyber-attacks is also increasing. Machine learning methods are effective against these threats. This paper aims to review these methods in terms of their utility for the detection, identification, and mitigation of cyber-attacks now and in the future. Many reviews described and discussed various machine learning methods and how each method can be used against specific types of cyber threats. However, empirical evidence did not show any clear superiority of one method over the others. The differences in research contexts and methodologies could have contributed to this uncertainty. However, deep learning methods might have an edge over shallow learning methods. Emerging new trends of cyber-attacks demand renewed research on the problem. The invention of an entirely new concept to deal with various cybersecurity issues cannot be ruled out.