Genetic Algorithm for framing rules for Intrusion Detection

With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. The detection of attacks against computer networks is becoming a harder problem to solve in the field of Network security. Intrusion Detection is an essential mechanism to protect computer systems from many attacks. As the transmission of data over the internet increases the need to protect connected system also increases. Therefore, unwanted intrusions take place when the actual software systems are running. A brief overview of Intrusion Detection System, genetic algorithm and related detection techniques was presented. Developing rules manually through incorporation of attack signatures results in meaningful but weak as it is difficult to define thresholds. In this paper the method of learning the Intrusion Detection, rules based on genetic algorithms was presented. The experimental results are demonstrated on the KDD cup 99 intrusion detection data set. In our experiments the characters of an attack such as smurf and warezmaster were summarized through the KDD 99 data set and the effectiveness and robustness of the approach are proved.