A Survey on Anomaly Detection in Network Intrusion Detection System Using Particle Swarm Optimization Based Machine Learning Techniques

Abstract

The progress in the field of Computer Networks & Internet is increasing with tremendous volume in recent years. This raises important issues with regards to security. Several solutions emerged in the past which provide security at the host or network level. These traditional solutions like antivirus, firewall, spyware & authentication mechanism provide security to some extends but they still face the challenges of inherent system flaws & social engineering attacks. Some interesting solution emerged like Intrusion Detection & Prevention Systems but these too have some problems like detecting & responding in real time & discovering novel attacks. Several Machine Learning techniques like Neural Network, Support Vector Machine, Rough Set etc. Were proposed for making an efficient and Intelligent Network Intrusion Detection System. Also Particle Swarm Optimization is currently attracting considerable interest from the research community, being able to satisfy the growing demand of reliable & intelligent Intrusion Detection System (IDS). Recent development in the field of IDS shows that securing the network with a single technique proves to be insufficient to cater ever increasing threats, as it is very difficult to cope with all vulnerabilities of today’s network. So there is a need to combine all security technologies under a complete secure system that combines the strength of these technologies under a complete secure system that combines the strength of these technologies & thus eventually provide a solid multifaceted well against intrusion attempts. This paper gives an insight into how Particle Swarm Optimization and its variants can be combined with various Machine Learning techniques used for Anomaly Detection in Network Intrusion Detection System by researchers so as to enhance the performance of Intrusion Detection System.