Byeongjun Min, Jihun Ryu, Dongkyoo Shin, Dongil Shin

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 2, pp. 65-72, Feb. 2021
https://doi.org/10.3745/KTSDE.2021.10.2.65,   PDF Download:
Keywords: Intrusion Dectection, Deep Learning, Over Sampling, Feature selection
Abstract

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

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