Intelligent Intrusion Detection System Using Log Cluster Knowledge Inference Bayes in Complex Event Processing

Abstract

Intrusion Detection Systems (IDS) are critical components in a secure network environment, permitting for initial discovery of malicious actions along with attacks. By means of using the data provided by IDS, it is probable to relate proper countermeasures and to alleviate attacks that extremely determine the security of a network. Widespread research was done in the field of IDS design to construct highly scalable IDS without compromising efficiency and security. The purpose of the proposed work is to develop Intelligent IDS using Log Cluster Knowledge Inference Bayes (IIDS-LCKIB) in Complex Event Processing (CEP) Environment. IIDS-LCKIB is used to examine the network traffic data effectivelyIIDS-LCKIB provide the better CEP Environment. Further, it endeavours to decrease the rate of False Positive for the solicitation of network intrusion systems in the real-world and to focus on security along with scalability in Network traffic data. In addition, it tries to test and estimate the performance using New Mathematical IIDS-LCKIB in CEP Environment. The parameters simulation is tested in Java/J2EE software.