Comparative DQN-Improved Algorithms for Stochastic Games-Based Automated Edge Intelligence-Enabled IoT Malware Spread-Suppression Strategies

Shen, Y; Shepherd, C; Ahmed, CM; Yu, S; Li, T

Comparative DQN-Improved Algorithms for Stochastic Games-Based Automated Edge Intelligence-Enabled IoT Malware Spread-Suppression Strategies

Shen, Y Shepherd, C Ahmed, CM Yu, S

Li, T

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (12), pp. 22550-22561
Issue Date:: 2024-06-15

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Field	Value	Language
dc.contributor.author	Shen, Y
dc.contributor.author	Shepherd, C
dc.contributor.author	Ahmed, CM
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Li, T
dc.date.accessioned	2024-08-21T05:35:22Z
dc.date.available	2024-08-21T05:35:22Z
dc.date.issued	2024-06-15
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (12), pp. 22550-22561
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180491
dc.description.abstract	Massive volumes of malware spread incidents continue to occur frequently across the Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial intelligence (AI) can provide assistance for automatically converging to an optimal strategy. By merging AI into edge computing, we consider an edge intelligence-enabled IoT (EIIoT) environment and provide a stochastic learning strategy for suppressing the spread of IoT malware. In particular, we introduce stochastic game theory to symbolise the whole process of the confrontation between IoT malware and edge nodes. Built upon the theoretical framework to demonstrate the specific spread-suppression architecture, we apply the improved deep Q-network (DQN) algorithms including double DQN for malware spread (DDQMS), dueling DQN for malware spread and dueling DDQMS that can deduce the optimal EIIoT malware spread-suppression strategy with better performance. Through experiments, we investigate the influence of related parameters on learning strategy selection, recommending the optimal parameters setting of automated EIIoT malware spread-suppression. We also compare the performance of the proposed three DQN-improved algorithms.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2024.3381281
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Comparative DQN-Improved Algorithms for Stochastic Games-Based Automated Edge Intelligence-Enabled IoT Malware Spread-Suppression Strategies
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
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
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:35:20Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	12

Abstract:

Massive volumes of malware spread incidents continue to occur frequently across the Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial intelligence (AI) can provide assistance for automatically converging to an optimal strategy. By merging AI into edge computing, we consider an edge intelligence-enabled IoT (EIIoT) environment and provide a stochastic learning strategy for suppressing the spread of IoT malware. In particular, we introduce stochastic game theory to symbolise the whole process of the confrontation between IoT malware and edge nodes. Built upon the theoretical framework to demonstrate the specific spread-suppression architecture, we apply the improved deep Q-network (DQN) algorithms including double DQN for malware spread (DDQMS), dueling DQN for malware spread and dueling DDQMS that can deduce the optimal EIIoT malware spread-suppression strategy with better performance. Through experiments, we investigate the influence of related parameters on learning strategy selection, recommending the optimal parameters setting of automated EIIoT malware spread-suppression. We also compare the performance of the proposed three DQN-improved algorithms.

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http://hdl.handle.net/10453/180491