An Intelligent Intrusion Detection System Using Genetic Algorithms and Features Selection

There has been a rapid growth in the numbers of attacks to the information and communication systems. Also, we witness smarter behaviors from the attackers. Thus, to prevent our systems from these attackers, we need to create smarter intrusion detection systems. In this paper, a new intelligent intrusion detection system has been proposed using genetic algorithms. In this system, at first, the network connection features were ranked according to their importance in detecting attack using information theory measures. Then, the network traffic linear classifiers based on genetic algorithms have been designed. These classifiers were trained and tested using KDD۹۹ data sets. A detection engine based on these classifiers was build and experimented. The experimental results showed a detection rate up till to ۹۲.۹۴%. This engine can be used in real-time mode.

کلیدواژه ها:

computer science ، Intrusion Detection Systems ، en ، Security ، Anomaly detection ، genetic algorithms ، Features selection