An Evolution Strategy Approach toward RuleSet Generation for Network Intrusion Detection
Herve Kabamba Mbikayi
Herve Kabamba Mbikayi, Guest Researcher at Leiden Institute of Advanced Computer Science (LIACS), Leiden University, The Netherlands Research and Teaching Assistant at Institute Superior de Commerce de Kinshas
Manuscript received on November 01, 2012. | Revised Manuscript received on November 02, 2012. | Manuscript published on November 05, 2012. | PP: 201-205 | Volume-2 Issue-5, November 2012. | Retrieval Number: E1042102512/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the increasing number of intrusions in systems’ and networks’ infrastructures, Intrusion Detection Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of Evolutionary Algorithms in IDS has proved its maturity over the times. Although most of the research works have been based on the use of genetic algorithms in IDS, this paper presents an approach toward the generation of rules for the identification of anomalous connections using evolution Strategies . The emphasis is given on how the problem can be modeled into ES primitives and how the fitness of the population can be evaluated in order to find the local optima, therefore resulting in an optimal rules that can be used for detecting intrusions in intrusion detection systems..
Keywords: Intrusion detection systems, evolution strategy, evolutionary algorithms.