Abstract
Nowadays, intelligent learning environments are efficient and interesting solutions for many complicated problems. Learning automata is an intelligent decision-making scheme that follows a learning strategy to select the optimal action through repeated interactions with its environment. In this paper, we adopt this learning system to develop a high-performance intrusion detection system. Intrusion detection systems are based on either anomaly detection or misuse detection approaches. Only known attacks can be detected by misuse detection, but anomaly detection is intelligent enough to detect all the unknown attacks. This paper was aimed to detect anomaly intrusive behaviors inside the network, in a learning manner. Simulative study, done based on KDD Cup 99 dataset, shows that the proposed approach outperforms existing schemes in terms of the attack detection ratio and the false alarm ratio.
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Notes
Linear Reward epsilon Penalty.
Linear Reward Inaction.
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Jamali, S., Jafarzadeh, P. An intelligent intrusion detection system by using hierarchically structured learning automata. Neural Comput & Applic 28, 1001–1008 (2017). https://doi.org/10.1007/s00521-015-2116-4
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DOI: https://doi.org/10.1007/s00521-015-2116-4