A Literature Review on Machine Learning for Cyber Security Issues
Jay Kumar Jain, Akhilesh A. Waoo, Dipti Chauhan
Abstract
Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help. A subset of artificial intelligence (AI), machine learning (ML) focuses on the development of systems that can learn from past data, recognize patterns, and reach logical conclusions with little to no human involvement. The concept of cyber security involves guarding against hostile attack on digital systems such computers, servers, mobile devices, networks, and the data they are connected to. Accounting for cyber security where machine learning is used and using machine learning to enable cyber security are the two main components of combining cyber security and ML. We may benefit from this union in a number of ways, including by giving machine learning models better security, enhancing the effectiveness of cyber security techniques, and supporting the efficient detection of zero day threats with minimal human involvement. In this review paper, we combine ML and cyber security to talk about two distinct notions. We also talk about the benefits, problems, and difficulties of combining ML and cyber security. In addition, we explore several attacks and present a thorough analysis of various tactics in two different categories. Finally, we offer a few suggestions for future research.