Publication

Abstract : Intrusion Detection is a way of detecting malicious packets in a network. Anomaly Intrusion Detection is to find novel attacks through malicious packets in the network. Decision Rules plays major role in identifying these packets. Fitness of the Decision Rules leads to a high secure network. Evolutionary Algorithms which are biologically inspired algorithms are precise in evaluating the Fitness of the Decision rules. In this paper, Proposed Fitness function of Evolutionary Algorithms is implemented on Traffic and Content Anomaly Intrusion Detection. The Detection rate and false rate of the Decision rules through Proposed Fitness and Existing Fitness Function of Evolutionary Algorithms are compared. A comparison of the Evolutionary Algorithm through Proposed Fitness function is also examined.