Abstract
With the drastic increase in internet usage, various categories of attacks have also evolved. Conventional intrusion detection techniques to counter these attacks have failed and thus substantial systems are needed to eliminate these attacks before they inflict huge damage. With the ability of computational intelligence systems to adapt, exhibit fault tolerance, high computational speed and error resilience against noisy information, a hybrid genetic fuzzy rule based inference engine has been designed in this paper. The fuzzy logic constructs precise and flexible patterns while the genetic algorithm based on evolutionary computation helps in attaining an optimal solution, thus their collaboration will increase the robustness of intrusion detection system. The proposed network intrusion detection system will be able to classify normal behavior as well as anomalies in the network. Detailed analysis has been done on DARPA-KDD99 dataset to specify the behavior of each connection.
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Chadha, K., Jain, S. (2015). Hybrid Genetic Fuzzy Rule Based Inference Engine to Detect Intrusion in Networks. In: Buyya, R., Thampi, S. (eds) Intelligent Distributed Computing. Advances in Intelligent Systems and Computing, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-11227-5_17
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DOI: https://doi.org/10.1007/978-3-319-11227-5_17
Publisher Name: Springer, Cham
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