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

Hyejeong Jeong; Jieun Yu; Wonjun Lee

doi:10.1109/INFOCOMWKSHPS51825.2021.9484569

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

Hyejeong Jeong, Jieun Yu, Wonjun Lee

School of Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

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 language	English
Title of host publication	IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665404433
DOIs	https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484569
Publication status	Published - 2021 May 10
Event	2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 - Virtual, Online Duration: 2021 May 9 → 2021 May 12

Publication series

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

Conference

Conference	2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021
City	Virtual, Online
Period	21/5/9 → 21/5/12

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

Access to Document

10.1109/INFOCOMWKSHPS51825.2021.9484569

Cite this

Jeong, H., Yu, J., & Lee, W. (2021). Poster abstract: A semi-supervised approach for network intrusion detection using generative adversarial networks. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 [9484569] (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484569

Poster abstract: A semi-supervised approach for network intrusion detection using generative adversarial networks. / Jeong, Hyejeong; Yu, Jieun; Lee, Wonjun.
IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9484569 (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeong, H, Yu, J & Lee, W 2021, Poster abstract: A semi-supervised approach for network intrusion detection using generative adversarial networks. in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021., 9484569, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021, Virtual, Online, 21/5/9. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484569

Jeong H, Yu J, Lee W. Poster abstract: A semi-supervised approach for network intrusion detection using generative adversarial networks. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9484569. (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021). doi: 10.1109/INFOCOMWKSHPS51825.2021.9484569

Jeong, Hyejeong ; Yu, Jieun ; Lee, Wonjun. / Poster abstract : A semi-supervised approach for network intrusion detection using generative adversarial networks. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021).

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

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.",

keywords = "Generative Adversarial Network, Network Intrusion Detection, Semi-supervised learning",

author = "Hyejeong Jeong and Jieun Yu and Wonjun Lee",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2088812). Wonjun Lee is the corresponding author. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 ; Conference date: 09-05-2021 Through 12-05-2021",

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doi = "10.1109/INFOCOMWKSHPS51825.2021.9484569",

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AB - 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.

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T3 - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021

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

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Y2 - 9 May 2021 through 12 May 2021

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

Abstract

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Keywords

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