earticle

영어

A variety of intrusion prevention techniques, such as user authentication using passwords, avoidance of programming errors and information protection, has been used to protect computer systems. However information prevention alone is not sufficient to protect our systems as those systems become even more complex with the rapid growth and expansion of Internet technology and local network systems. Moreover, programming errors, firewall configuration errors and ambiguous or undefined security policies add to the system’s complexity. An Intrusion Detection system (IDS) is therefore needed as another layer to protect computer systems. The IDS is one of the most important techniques of information dynamic security technology. It is defined as a process of monitoring the events occurring in a computer system or network and analyzing them to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive. Current Intrusion Detection Systems have several known shortcomings, such as low accuracy (registering high False Positives and False Negatives); low real-time performance (processing a large amount of traffic in real time); limited scalability (storing a large number of user profiles and attack signatures); an inability to detect new attacks (recognizing new attacks when they are launched for the first time); and weak system reactive capabilities (efficiency of response). This makes the area of IDS an attractive research field. In recent years, researchers have investigated techniques such as artificial intelligence, autonomous agents and distributed systems for detecting intrusion in network environments. In this work we have realized an Intrusion Detection System based on Genetic algorithm (GA) approach. For evolving and testing new rules for intrusion detection system the KDD99Cup training and testing dataset were used.