Poster abstract: A semi-supervised approach for network intrusion detection using generative adversarial networks

Hyejeong Jeong, Jieun Yu, Wonjun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665404433
DOIs
Publication statusPublished - 2021 May 10
Event2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 - Virtual, Online
Duration: 2021 May 92021 May 12

Publication series

NameIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

Conference

Conference2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
CityVirtual, Online
Period21/5/921/5/12

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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