A fuzzy-genetic approach to network intrusion detection

Computer networks have expanded significantly in use and numbers. This expansion makes them more vulnerable to attack by malicious agents. 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 is necessary to find new ways to implement and operate intrusion detection systems. Genetic-based systems offer to ability to adapt to changing environments, robustness to noise and the ability to identify unknown attack methods. This paper presents a fuzzy-genetic approach to intrusion detection that is shown to provide performance superior to other GA-based algorithms. In addition, the method demonstrates improved robustness in comparison to other GA-based techniques. Categories and Subject Descriptors C.2.0 [Computer Communications Networks] General

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Terrence P. Fries
• Keywords: Current Intrusion Detection, GECCO 2008, Intrusion Detection, Intrusion Detection Systems, Optimization