Poster Abstract: A Semi-Supervised Approach for Network Intrusion Detection Using Generative Adversarial Networks

被引：5

作者：

Jeong, Hyejeong ^{[1
]}

Yu, Jieun ^{[1
]}

Lee, Wonjun ^{[1
]}

机构：

[1] Korea Univ, Sch Cybersecur, Network & Secur Res Lab, Seoul, South Korea

来源：

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021) | 2021年

基金：

新加坡国家研究基金会;

关键词：

Network Intrusion Detection; Semi-supervised learning; Generative Adversarial Network;

D O I：

10.1109/INFOCOMWKSHPS51825.2021.9484569

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Network intrusion detection is a crucial task since malicious traffic occurs every second these days. Various research has been studied in this field and shows high performance. However, most of them are conducted in a supervised manner that needs a range of labeled data but it is hard to obtain. This paper proposes a semi-supervised Generative Adversarial Networks (GAN) model for network intrusion detection that requires only 10 labeled data per each flow type. Our model is evaluated using the publicly available CICIDS-2017 dataset and outperforms other malware traffic classification models.

引用

页数：2

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