Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing alg...

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Autores principales:	Zengri Zeng, Wei Peng, Baokang Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/c3af11d54071445a8ff31338efc7b075
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spelling	oai:doaj.org-article:c3af11d54071445a8ff31338efc7b0752021-11-15T01:19:23ZImproving the Accuracy of Network Intrusion Detection with Causal Machine Learning1939-012210.1155/2021/8986243https://doaj.org/article/c3af11d54071445a8ff31338efc7b0752021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8986243https://doaj.org/toc/1939-0122In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.Zengri ZengWei PengBaokang ZhaoHindawi-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 Zengri Zeng Wei Peng Baokang Zhao Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
description	In recent years, machine learning (ML) algorithms have been approved effective in the intrusion detection. However, as the ML algorithms are mainly applied to evaluate the anomaly of the network, the detection accuracy for cyberattacks with multiple types cannot be fully guaranteed. The existing algorithms for network intrusion detection based on ML or feature selection are on the basis of spurious correlation between features and cyberattacks, causing several wrong classifications. In order to tackle the abovementioned problems, this research aimed to establish a novel network intrusion detection system (NIDS) based on causal ML. The proposed system started with the identification of noisy features by causal intervention, while only the features that had a causality with cyberattacks were preserved. Then, the ML algorithm was used to make a preliminary classification to select the most relevant types of cyberattacks. As a result, the unique labeled cyberattack could be detected by the counterfactual detection algorithm. In addition to a relatively stable accuracy, the complexity of cyberattack detection could also be effectively reduced, with a maximum reduction to 94% on the size of training features. Moreover, in case of the availability of several types of cyberattacks, the detection accuracy was significantly improved compared with the previous ML algorithms.
format	article
author	Zengri Zeng Wei Peng Baokang Zhao
author_facet	Zengri Zeng Wei Peng Baokang Zhao
author_sort	Zengri Zeng
title	Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
title_short	Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
title_full	Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
title_fullStr	Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
title_full_unstemmed	Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
title_sort	improving the accuracy of network intrusion detection with causal machine learning
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/c3af11d54071445a8ff31338efc7b075
work_keys_str_mv	AT zengrizeng improvingtheaccuracyofnetworkintrusiondetectionwithcausalmachinelearning AT weipeng improvingtheaccuracyofnetworkintrusiondetectionwithcausalmachinelearning AT baokangzhao improvingtheaccuracyofnetworkintrusiondetectionwithcausalmachinelearning
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Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

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