A Feature Selection Approach for the Network Intrusion Detection System Based on the Artificial Neural Network

In-Seon Jeong¹, Seung-Jae Lee², Seung-Ho Kang³

Abstract

As sophisticated cyber-attacks using innovative techniques appears, it is difficult for the traditional intrusion detection system based on the simple rules to detect the novel type of attacks such as advanced persistent threat (APT) attack. Many recent researches have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, however, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to select the Pareto-optimal feature set such that satisfies two trade-off relationship requirements: accuracy and fast response. The comparison between the proposing approach and other previously proposed approaches is conducted against the NSL_KDD data set for the evaluation of the performance of the proposing method.

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In-Seon Jeong, Seung-Jae Lee, Seung-Ho Kang. A Feature Selection Approach for the Network Intrusion Detection System Based on the Artificial Neural Network. International Conference on Cyber Security and Connected Technologies (2017): 14. Print.