Author Description

The application of machine learning (ML) technique in cybersecurity is increasing than ever before. Starting from IP traffic classification, filtering malicious traffic for intrusion detection, ML is the one of the promising answers that can be effective against zero day threats. New research is being done by use of statistical traffic characteristics and ML techniques. This paper is a focused literature survey of machine learning and its application to cyber analytics for intrusion detection, traffic classification and applications such as email filtering. Based on the relevance and the number of citation each methods were identified and summarized. Because datasets are an important part of the ML approaches some well know datasets are also mentioned. Some recommendations are also provided on when to use a given algorithm.