Evolutionary optimization of a fuzzy rule-based network intrusion detection system

The use of computer networks has increased significantly in recent years. This proliferation, in combination with the interconnection of networks via the Internet, has drastically increased their vulnerability to attack by malicious agents. The wide variety of attack modes has exacerbated the problem in detecting attacks. Many current intrusion detection systems (IDS) are unable to identify unknown or mutated attack modes or are unable to operate in a dynamic environment as is necessary with mobile networks. As a result, it has become increasingly important to find new ways to implement and manage intrusion detection systems. Evolutionary-based systems offer the ability to adapt to dynamic environments and to identify unknown attack methods. Fuzzy-based systems accommodate the imprecision associated with mutated and previously unidentified attack modes. This paper presents an evolutionary-fuzzy approach to intrusion detection that is shown to provide superior performance in comparison to other evolutionary approaches. In addition, the method demonstrates improved robustness in comparison to other evolutionary-based techniques.