A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

Abstract

Cyber security is that the body of technologies, processes and practices designed to safeguard networks, computers, programs and knowledge from attack, harm or unauthorized access. During a computing context, the term security implies cyber security. This survey paper describes a targeted literature survey of machine learning (ML) and data processing (DM) strategies for cyber analytics in support of intrusion detection. This paper focuses totally on cyber intrusion detection as it applies to wired networks. With a wired network, associate oppose must experience many layers of defense at firewalls and operative systems, or gain physical access to the network. The quality of ML/DM algorithms is addressed, discussion of challenges for victimization ML/DM for cyber security is conferred, and some recommendations on once to use a given methodology area unit provided.