Machine Learning Based Intrusion Detection System

Abstract

Security is one of the fundamental issues for both computer systems and computer networks. Intrusion detection system (IDS) is a crucial tool in the field of network security. There are a lot of scopes for research in this pervasive field. Intrusion detection systems are designed to uncover both known and unknown attacks. There are many methods used in intrusion detection system to guard computers and networks from attacks. These attacks can be active or passive, network based or host based, or any combination of it. Current research uses machine learning techniques to make intrusion detection systems more effective against any kind of attack. This survey examines designing methodology of intrusion detection system and its classification types. It also reviews the trend of machine learning techniques used from past decade. Related studies comprise performance of various classifiers on KDDCUP99 and NSL-KDD dataset.