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The Journal of Supercomputing

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11-12-2019

Enhancing network intrusion detection classifiers using supervised adversarial training

Authors: Chuanlong Yin, Yuefei Zhu, Shengli Liu, Jinlong Fei, Hetong Zhang

Published in: The Journal of Supercomputing | Issue 9/2020

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Abstract

The performance of classifiers has a direct impact on the effectiveness of intrusion detection system. Thus, most researchers aim to improve the detection performance of classifiers. However, classifiers can only get limited useful information from the limited number of labeled training samples, which usually affects the generalization of classifiers. In order to enhance the network intrusion detection classifiers, we resort to adversarial training, and a novel supervised learning framework using generative adversarial network for improving the performance of the classifier is proposed in this paper. The generative model in our framework is utilized to continuously generate other complementary labeled samples for adversarial training and assist the classifier for classification, while the classifier in our framework is used to identify different categories. Meanwhile, the loss function is deduced again, and several empirical training strategies are proposed to improve the stabilization of the supervised learning framework. Experimental results prove that the classifier via adversarial training improves the performance indicators of intrusion detection. The proposed framework provides a feasible method to enhance the performance and generalization of the classifier.

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Title: Enhancing network intrusion detection classifiers using supervised adversarial training
Authors: Chuanlong Yin
Yuefei Zhu
Shengli Liu
Jinlong Fei
Hetong Zhang
Publication date: 11-12-2019
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 9/2020
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-019-03092-1

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