Intrusion detection using artificial intelligence based ensembles

Network security is a serious global concern. The increasing prevalence of malware and incidents of attacks hinders the utilization of the Internet to its greatest benefit and incur significant economic losses. The traditional approaches in securing systems against threats are designing mechanisms that create a protective shield, almost always with vulnerabilities. This has created Intrusion Detection Systems to be developed that complement traditional approaches. However, with the advancement of computer technology, the behavior of intrusions has become complex that makes the work of security experts hard to analyze and detect intrusions. In order to address these challenges, Artificial Intelligence based techniques have become a promising solution. However, the individual techniques report low detection accuracy and high false positive rate in intrusion detection. The accuracy may be enhanced by using ensemble techniques comprised of multiple techniques. The multiple techniques can be combined in a number of ways to enhance the detection results.