LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection

Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, exis...

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Autores principales:	Shengwei Lei, Chunhe Xia, Tianbo Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/e6378b4b481d4ad0bbe5a0e2862fcc38
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id	oai:doaj.org-article:e6378b4b481d4ad0bbe5a0e2862fcc38
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spelling	oai:doaj.org-article:e6378b4b481d4ad0bbe5a0e2862fcc382021-11-08T02:35:38ZLCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection1530-867710.1155/2021/6830372https://doaj.org/article/e6378b4b481d4ad0bbe5a0e2862fcc382021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6830372https://doaj.org/toc/1530-8677Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.Shengwei LeiChunhe XiaTianbo WangHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Shengwei Lei Chunhe Xia Tianbo Wang LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
description	Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.
format	article
author	Shengwei Lei Chunhe Xia Tianbo Wang
author_facet	Shengwei Lei Chunhe Xia Tianbo Wang
author_sort	Shengwei Lei
title	LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
title_short	LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
title_full	LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
title_fullStr	LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
title_full_unstemmed	LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
title_sort	lchi: low-order correlation and high-order interaction integrated model oriented to network intrusion detection
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/e6378b4b481d4ad0bbe5a0e2862fcc38
work_keys_str_mv	AT shengweilei lchilowordercorrelationandhighorderinteractionintegratedmodelorientedtonetworkintrusiondetection AT chunhexia lchilowordercorrelationandhighorderinteractionintegratedmodelorientedtonetworkintrusiondetection AT tianbowang lchilowordercorrelationandhighorderinteractionintegratedmodelorientedtonetworkintrusiondetection
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LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection

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