An Efficient Intrusion detection system for network behaviors using Fuzzy logic based Rules

Abstract: Internet services and web applications have become an inextricable part of daily life, enabling communication and the management of personal information from anywhere. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on Fuzzy rules based AI approach for intrusion detection. The techniques are modeled using fuzzy logic with network profiling that uses simple data mining techniques to process the network data. The proposed system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host and Network based intrusion detection use fuzzy rules and machine learning along with self organizing hash maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them. Both network traffic and system audit data are used as inputs for both. Experimental results proves that system out performs other techniques.

Keywords: Anomaly detection, network behavior, intrusion detection, Data Centre Security