Hybrid Model For Intrusion Detection System

The rapid growth of the computers that are interconnected, the crime rate has also increased and the ways to mitigate those crimes has become the important problem now. In the entire globe, organizations, higher learning institutions and governments are completely dependent on the computer networks which play a major role in their daily operations. Hence the necessity for protecting those networked systems has also increased. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. Currently available intrusion detection systems focus mainly on determining uncharacteristic system events in distributed networks using signature based approach. Due to its limitation of finding novel attacks, we propose a hybrid model based on improved fuzzy and data mining techniques, which can detect both misuse and anomaly attacks. Our aim is to reduce the amount of data retained for processing i.e., attribute selection process and also to improve the detection rate of the existing IDS using data mining technique. We then use improved Kuok fuzzy data mining algorithm, which in turn a modified version of APRIORI algorithm, for implementing fuzzy rules, which allows us to construct if-then rules that reflect common ways of describing security attacks. We applied fuzzy inference engine using mamdani inference mechanism with three variable inputs for faster decision making