Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning

The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Mos...

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Autores principales:	Zhendong Wang, Zeyu Li, Junling Wang, Dahai Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/610f37725d81425fa9970e803e066cec
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spelling	oai:doaj.org-article:610f37725d81425fa9970e803e066cec2021-11-08T02:35:37ZNetwork Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning1939-012210.1155/2021/9486949https://doaj.org/article/610f37725d81425fa9970e803e066cec2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9486949https://doaj.org/toc/1939-0122The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Most research on intrusion detection systems based on network anomalous traffic detection has focused on supervised learning; however, the process of obtaining labeled data often requires a lot of time and effort, as well as the support of network experts. Therefore, it is worthwhile investigating the development of label-free self-supervised learning-based approaches called BYOL which is a simple and elegant framework with sufficiently powerful feature extraction capabilities for intrusion detection systems. In this paper, we propose a new data augmentation strategy for intrusion detection data and an intrusion detection model based on label-free self-supervised learning, using a new data augmentation strategy to introduce a perturbation enhancement model to learn invariant feature representation capability and an improved BYOL self-supervised learning method to train the UNSW-NB15 intrusion detection dataset without labels to extract network traffic feature representations. Linear evaluation on UNSW-NB15 and transfer learning on NSK-KDD, KDD CUP99, CIC IDS2017, and CIDDS_001 achieve excellent performance in all metrics.Zhendong WangZeyu LiJunling WangDahai LiHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Zhendong Wang Zeyu Li Junling Wang Dahai Li Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
description	The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face of massive, high-dimensional network traffic with uneven sample distribution, how to be able to accurately detect anomalous traffic is the primary task of intrusion detection. Most research on intrusion detection systems based on network anomalous traffic detection has focused on supervised learning; however, the process of obtaining labeled data often requires a lot of time and effort, as well as the support of network experts. Therefore, it is worthwhile investigating the development of label-free self-supervised learning-based approaches called BYOL which is a simple and elegant framework with sufficiently powerful feature extraction capabilities for intrusion detection systems. In this paper, we propose a new data augmentation strategy for intrusion detection data and an intrusion detection model based on label-free self-supervised learning, using a new data augmentation strategy to introduce a perturbation enhancement model to learn invariant feature representation capability and an improved BYOL self-supervised learning method to train the UNSW-NB15 intrusion detection dataset without labels to extract network traffic feature representations. Linear evaluation on UNSW-NB15 and transfer learning on NSK-KDD, KDD CUP99, CIC IDS2017, and CIDDS_001 achieve excellent performance in all metrics.
format	article
author	Zhendong Wang Zeyu Li Junling Wang Dahai Li
author_facet	Zhendong Wang Zeyu Li Junling Wang Dahai Li
author_sort	Zhendong Wang
title	Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
title_short	Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
title_full	Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
title_fullStr	Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
title_full_unstemmed	Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning
title_sort	network intrusion detection model based on improved byol self-supervised learning
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/610f37725d81425fa9970e803e066cec
work_keys_str_mv	AT zhendongwang networkintrusiondetectionmodelbasedonimprovedbyolselfsupervisedlearning AT zeyuli networkintrusiondetectionmodelbasedonimprovedbyolselfsupervisedlearning AT junlingwang networkintrusiondetectionmodelbasedonimprovedbyolselfsupervisedlearning AT dahaili networkintrusiondetectionmodelbasedonimprovedbyolselfsupervisedlearning
_version_	1718443189939994624

Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning

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