A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems.

Intrusion detection systems are devoted to monitor a network with aims at finding and avoiding anomalous events. In particular, we focus on misuse detection systems, which are trained to identify several known types of attacks. These can be unauthorized accesses, or denial of service attacks, among others. Whenever it scans a trace of a suspicious event, it is programmed to trigger an alert and/or to block this dangerous access to the system. Depending on the security policies of the network, the administrator may seek different requirements that will have a strong dependency on the behavior of the intrusion detection system. For a given application, the cost of raising false alarms could be higher than carrying out a preventive access lock. In other scenarios, there could be a necessity of correctly identifying the exact type of cyber attack to proceed in a given way. In this paper, we propose a multi-objective evolutionary fuzzy system for the development of a system that can be trained using different metrics. By increasing the search space during the optimization of the model, more accurate solutions are expected to be obtained. Additionally, this scheme allows the final user to decide, among a broad set of solutions, which one is better suited for the current network characteristics. Our experimental results, using the well-known KDDCup'99 problem, supports the quality of this novel approach in contrast to the state-of-the-art for evolutionary fuzzy systems in intrusion detection, as well as the C4.5 decision tree. [ABSTRACT FROM AUTHOR]