Multi-agent deep reinforcement learning-based key generation for graph layer security

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dc.contributor.authorWang, Liang
dc.contributor.authorWei, Zhuangkun
dc.contributor.authorGuo, Weisi
dc.date.accessioned2025-03-03T11:36:04Z
dc.date.available2025-03-03T11:36:04Z
dc.date.freetoread2025-03-03
dc.date.issued2025-05
dc.date.pubOnline2025-01-14
dc.descriptionAll research work was conducted whilst all authors were at Cranfield University.
dc.description.abstractRecently, the emergence of Internet of Things (IoT) devices has posed a challenge for securing information and avoiding attacks. Most of the cryptography solutions are based on physical layer security (PLS), whose idea is to fully exploit the properties of wireless channel state information (CSI) for generating symmetric keys between two communication nodes. However, accurate channel estimation is vulnerable for attackers and relies on powerful signal processing capability, which is not suitable for low-power IoT devices. In this paper, we expect to apply graph layer security (GLS) to exploit the common features of physical dynamics detected by IoT sensors placed in networked systems to generate keys for data encryption and decryption, which we believe is a new frontier to security for both industry and academic research. We propose a distributed key generation algorithm based on multi-agent deep reinforcement learning (MADRL) approach, which enables communication nodes to cooperatively generate symmetric keys based on their locally detected physical dynamics (e.g., water/gas/oil/electrical pressure/flow/voltage) with low computational complexity and without information exchange. In order to demonstrate the feasibility, we conduct and evaluate our key generation algorithm in both a simulated and real water distribution network. The experimental results show that the proposed algorithm has considerable performance in terms of randomness, bit agreement rate (BAR), and so on.
dc.description.journalNameACM Transactions on Privacy and Security
dc.description.sponsorshipThis work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1.
dc.identifier.citationWang L, Wei Z, Guo W. (2025) Multi-agent deep reinforcement learning-based key generation for graph layer security. ACM Transactions on Privacy and Security, Volume 28, Issue 2, May 2025, Article number 18
dc.identifier.eissn2471-2574
dc.identifier.elementsID562430
dc.identifier.issn2471-2566
dc.identifier.issueNo2
dc.identifier.paperNo18
dc.identifier.urihttps://doi.org/10.1145/3711900
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23559
dc.identifier.volumeNo28
dc.languageEnglish
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.urihttps://dl.acm.org/doi/10.1145/3711900
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4613 Theory Of Computation
dc.subject40 Engineering
dc.subject46 Information and Computing Sciences
dc.subject4006 Communications Engineering
dc.subject4605 Data Management and Data Science
dc.subject4606 Distributed Computing and Systems Software
dc.subjectStrategic, Defence & Security Studies
dc.subject4604 Cybersecurity and privacy
dc.subject4609 Information systems
dc.subjectMulti-agent deep reinforcement learning
dc.subjectPhysical layer security
dc.subjectGraph layer security
dc.subjectIoT devices
dc.subjectPhysical dynamics
dc.titleMulti-agent deep reinforcement learning-based key generation for graph layer security
dc.typeArticle
dcterms.dateAccepted2024-12-20

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