An Evolution Strategy Approach toward Rule-set Generation for Network Intrusion Detection Systems (IDS)
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With the increasing number of intrusions in system and network 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 optimal rules that can be used for detecting intrusions in intrusion detection systems.
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