An Intelligent Distributed Intrusion Detection System using Genetic Algorithm

Information security has been a very active research over the last two decades. High connectivity and massive access to network resources combined with the widespread use of vulnerable software has resulted in hundreds of security breaches. An intruder may move between multiple nodes in the network to conceal the origin of attack. Distributed intrusion detection and prevention plays an increasingly important role in securing computer networks. To overcome the limitations of conventional intrusion detection systems, alerts are made in distributed intrusion detection system which are exchanged and correlated in a cooperative fashion. This paper presents an intelligent learning approach using Genetic Algorithm (GA) for distributed Intrusion Detection System (DIDS), which uses simple representation of rules and an effective fitness function. The proposed method is easier to implement while providing the flexibility. GA is used to select a subset of input features that increase the detection rate and decrease the false alarm rate. Also the generated rules can be used to detect the distributed network intrusions with effective and adaptive cost.