Multi-criteria decision support for feature selection in network anomaly detection system

The growth of computer networks from LAN to cloud, virtualisation and mobility always keeps intrusion detection system (IDS) as a critical component in the field of network security infrastructure. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. The market for security solutions for next-generation is rapidly evolving and constantly changing to accommodate today's threat. Many intrusion detection techniques, methods and algorithms are implemented to detect these novel attacks. But there's no clear feature set, uncertainty bounds established as a baseline for dynamic environments. The main objective of this paper is to determine and provide the best feature selection for next generation dynamic environments using multi-criteria decision making, decision tree learning with emphasis on optimisation (contingency of weight allocation) and handling large datasets.