Identification of Intrusions in Network for Large Data Base using Soft Computing Approach
Tanveer Khan, Zuber Farooqui, Vineet Richhariya · 2012
Nowadays Intrusion Detection Systems (IDS) are very important for every information technology company which is concerned with security and sensitive systems. Even if a lot of research was already done on this topic, the perfect IDS have still not been found and it stays a hot and challenging area in computer security research. Current intrusion detection techniques mainly focus on discovering abnormal system events in computer networks and distributed communication systems. Most of the existing IDS use all 41 features in the network to evaluate and look for intrusive pattern some of these features are redundant and irrelevant. The drawback of this approach is time-consuming detection process and degrading the performance of Intrusion detection systems. In this proposed system Principal Component Analysis (PCA) is used to reduce the number of features in KDD dataset. After reducing feature, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. The experimental results clearly show that the proposed system achieved comparable and some cases higher rate in identifsying whether the trasaction (records) in network are intrusive activity or normal activity.