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

共 50 条

[31] Retinopathy Diagnosis Using Semi-supervised Multi-channel Generative Adversarial Network [J].

Xie, Yingpeng ;

Wan, Qiwei ;

Chen, Guozhen ;

Xu, Yanwu ;

Lei, Baiying .

OPHTHALMIC MEDICAL IMAGE ANALYSIS, 2019, 11855 :182-190

[32] A Semi-supervised Encoder Generative Adversarial Networks Model for Image Classification [J].

Fu, Xiao ;

Shen, Yuan-Tong ;

Li, Hong-Wei ;

Cheng, Xiao-Mei .

Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (03) :531-539

[33] SEMI-SUPERVISED LEARNING WITH GENERATIVE ADVERSARIAL NETWORKS FOR ARABIC DIALECT IDENTIFICATION [J].

Zhang, Chunlei ;

Zhang, Qian ;

Hansen, John H. L. .

2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, :5986-5990

[34] Semi-supervised Multi-category Classification with Generative Adversarial Networks [J].

Rastogi, Reshma ;

Gangnani, Ritesh .

PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PREMI 2019, PT I, 2019, 11941 :286-294

[35] SEMI-SUPERVISED VARIATIONAL GENERATIVE ADVERSARIAL NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [J].

Wang, Hao ;

Tao, Chao ;

Qi, Ji ;

Li, HaiFeng ;

Tang, YuQi .

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, :9792-9794

[36] Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder [J].

Hara, Kazuki ;

Shiomoto, Kohei .

NOMS 2020 - PROCEEDINGS OF THE 2020 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2020: MANAGEMENT IN THE AGE OF SOFTWARIZATION AND ARTIFICIAL INTELLIGENCE, 2020,

[37] A SEMI-SUPERVISED GENERATIVE ADVERSARIAL NETWORK FOR PREDICTION OF GENETIC DISEASE OUTCOMES [J].

Davi, Caio ;

Braga-Neto, Ulisses .

2021 IEEE 31ST INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2021,

[38] Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification [J].

Toutouh, Jamal ;

Nalluru, Subhash ;

Hemberg, Erik ;

O'Reilly, Una-May .

APPLIED SOFT COMPUTING, 2023, 148

[39] Semi-supervised Malicious Traffic Detection with Improved Wasserstein Generative Adversarial Network with Gradient Penalty [J].

Wang, Jiafeng ;

Liu, Ming ;

Yin, Xiaokang ;

Zhao, Yuhao ;

Liu, Shengli .

2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2022, :1916-1922

[40] Semi-Supervised Learning Based on Generative Adversarial Network and Its Applied to Lithology Recognition [J].

Li, Guohe ;

Qiao, Yinghan ;

Zheng, Yifeng ;

Li, Ying ;

Wu, Weijiang .

IEEE ACCESS, 2019, 7 :67428-67437

← 1 2 3 4 5 →