An Incremental Learning Method Based on Dynamic Ensemble RVM for Intrusion Detection

Wu, Z; Gao, P; Cui, L; Chen, J

An Incremental Learning Method Based on Dynamic Ensemble RVM for Intrusion Detection

Wu, Z Gao, P Cui, L Chen, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network and Service Management, 2022, 19, (1), pp. 671-685
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Wu, Z
dc.contributor.author	Gao, P
dc.contributor.author	Cui, L
dc.contributor.author	Chen, J
dc.date.accessioned	2023-06-28T03:51:17Z
dc.date.available	2023-06-28T03:51:17Z
dc.date.issued	2022-03-01
dc.identifier.citation	IEEE Transactions on Network and Service Management, 2022, 19, (1), pp. 671-685
dc.identifier.issn	1932-4537
dc.identifier.issn	1932-4537
dc.identifier.uri	http://hdl.handle.net/10453/170938
dc.description.abstract	Due to the dynamic changes of network data over time, static intrusion detection systems cannot adapt well to the behavioral characteristics of the input network data, resulting in reduced detection accuracy. In addition, continuous input data streams will bring huge challenges to resource storage and computing costs. Therefore, we propose an intrusion detection method of dynamic ensemble incremental learning (DEIL-RVM), and realize a dynamically adjusted ensemble intrusion detection model. In which a new overall misclassification probability weight value (OMPW) based on incremental set or data chunk is designed as the basis for updating the ensemble model, and it can be used to prune and replace the poor base component in the ensemble model. We presented a probabilistic decision function taking into account the posterior probability of each base RVM model dividing the sample into each category. The RVM with high sparsity is used as the base component to obtain the good balance between the accuracy, robustness and resource consumption, which can sacrifice less time and storage cost in ensemble incremental learning while achieving higher detection accuracy and stability in network data streams.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Network and Service Management
dc.relation.isbasedon	10.1109/TNSM.2021.3102388
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	An Incremental Learning Method Based on Dynamic Ensemble RVM for Intrusion Detection
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-06-28T03:51:15Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	1

Abstract:

Due to the dynamic changes of network data over time, static intrusion detection systems cannot adapt well to the behavioral characteristics of the input network data, resulting in reduced detection accuracy. In addition, continuous input data streams will bring huge challenges to resource storage and computing costs. Therefore, we propose an intrusion detection method of dynamic ensemble incremental learning (DEIL-RVM), and realize a dynamically adjusted ensemble intrusion detection model. In which a new overall misclassification probability weight value (OMPW) based on incremental set or data chunk is designed as the basis for updating the ensemble model, and it can be used to prune and replace the poor base component in the ensemble model. We presented a probabilistic decision function taking into account the posterior probability of each base RVM model dividing the sample into each category. The RVM with high sparsity is used as the base component to obtain the good balance between the accuracy, robustness and resource consumption, which can sacrifice less time and storage cost in ensemble incremental learning while achieving higher detection accuracy and stability in network data streams.

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http://hdl.handle.net/10453/170938