Network intrusion detection via machine learning

Yujia Cui; Kehan Yu; Jinren Zhou

doi:10.1117/12.2624888

13 December 2021 Network intrusion detection via machine learning

Yujia Cui, Kehan Yu, Jinren Zhou

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871B (2021) https://doi.org/10.1117/12.2624888
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

The risk and severity of network intrusion has clearly received great attention in the last decade. Meanwhile, machine learning methods have been widely employed in the area of cybersecurity. This paper introduces the network intrusion attacks and detection systems and gives an overview of literature on various machine learning models to achieve network intrusion detection, including logistic regression, k-nearest neighbors, neural networks, random forest, decision tree, and k-means clustering. We find that as the dataset gets larger, the machine learning methods yield better performance significantly. Furthermore, we discuss the prospects mentioned in the literature and put forward some key prominent future research directions in network intrusion detection systems.

Citation Download Citation

Yujia Cui, Kehan Yu, and Jinren Zhou "Network intrusion detection via machine learning", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871B (13 December 2021); https://doi.org/10.1117/12.2624888

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