Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things

Shen, S; Xie, L; Zhang, Y; Wu, G; Zhang, H; Yu, S

Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things

Shen, S Xie, L Zhang, Y Wu, G Zhang, H Yu, S

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 5302-5315
Issue Date:: 2023-01-01

Closed Access

	Filename	Description	Size
	Joint_Differential_Game_and_Double_Deep_Q-Networks_for_Suppressing_Malware_Spread_in_Industrial_Internet_of_Things.pdf	Published version	14.25 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shen, S
dc.contributor.author	Xie, L
dc.contributor.author	Zhang, Y
dc.contributor.author	Wu, G
dc.contributor.author	Zhang, H
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T02:49:30Z
dc.date.available	2024-03-12T02:49:30Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2023, 18, pp. 5302-5315
dc.identifier.issn	1556-6013
dc.identifier.issn	1556-6021
dc.identifier.uri	http://hdl.handle.net/10453/176544
dc.description.abstract	Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision-making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help smart factories, smart cities and smart homes. In this paper, a two-layer malware spread-patch model IIPV is proposed based on a hybrid patches distribution method according to the simple edge equipments and limited central computer resources of IIoT. The spread process of malware in IIoT was deeply analyzed using differential game and a differential game model was established. Then optimization theory was further used to solve the optimization problem extracted by introducing subjective effort parameters to obtain the optimal control strategies of devices for malware and patches. In addition, we combined the deep reinforcement learning algorithm into the model IIPV to design a new algorithm DDQN-PV suitable for suppressing the spread of malware in IIoT during the experiments. Finally, the effectiveness of model IIPV and algorithm DDQN-PV are verified by numerous comparative experiments.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security
dc.relation.isbasedon	10.1109/TIFS.2023.3307956
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Strategic, Defence & Security Studies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Joint Differential Game and Double Deep Q-Networks for Suppressing Malware Spread in Industrial Internet of Things
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T02:49:28Z
pubs.publication-status	Published
pubs.volume	18

Abstract:

Industrial Internet of Things (IIoT), which has the capability of perception, monitoring, communication and decision-making, has already exposed more security problems that are easy to be invaded by malware because of many simple edge devices that help smart factories, smart cities and smart homes. In this paper, a two-layer malware spread-patch model IIPV is proposed based on a hybrid patches distribution method according to the simple edge equipments and limited central computer resources of IIoT. The spread process of malware in IIoT was deeply analyzed using differential game and a differential game model was established. Then optimization theory was further used to solve the optimization problem extracted by introducing subjective effort parameters to obtain the optimal control strategies of devices for malware and patches. In addition, we combined the deep reinforcement learning algorithm into the model IIPV to design a new algorithm DDQN-PV suitable for suppressing the spread of malware in IIoT during the experiments. Finally, the effectiveness of model IIPV and algorithm DDQN-PV are verified by numerous comparative experiments.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176544