{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:07:52Z","timestamp":1761898072523,"version":"3.41.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:00:00Z","timestamp":1740182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"PETRAS National Centre of Excellence for IoT Systems Cybersecurity","award":["EP\/S035362\/1"],"award-info":[{"award-number":["EP\/S035362\/1"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Priv. Secur."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Recently, 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 article, 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.<\/jats:p>","DOI":"10.1145\/3711900","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:25:39Z","timestamp":1736853939000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-agent Deep Reinforcement Learning-based Key Generation for Graph Layer Security"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1566-9546","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"first","affiliation":[{"name":"school of cybersecurity, Northwestern Polytechnical University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3883-726X","authenticated-orcid":false,"given":"Zhuangkun","family":"Wei","sequence":"additional","affiliation":[{"name":"Imperial College London, London, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3524-3953","authenticated-orcid":false,"given":"Weisi","family":"Guo","sequence":"additional","affiliation":[{"name":"Cranfield University, Cranfield, United Kingdom of Great Britain and Northern Ireland"}]}],"member":"320","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). Retrieved from http:\/\/tensorflow.org\/. Software available from tensorflow.org."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2852765"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2877382"},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Lawrence Bassham Andrew Rukhin Juan Soto James Nechvatal Miles Smid Stefan Leigh M Levenson M Vangel Nathanael Heckert and D Banks. 2010. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. (2010-09-162010). Retrieved from https:\/\/tsapps.nist.gov\/publication\/get_pdf.cfm?pub_id=906762","DOI":"10.6028\/NIST.SP.800-22r1a"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2712158"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3098334"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-017-9314-z"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.3024621"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3103062"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3106351"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. (2014). DOI:10.48550\/ARXIV.1412.6980","DOI":"10.48550\/ARXIV.1412.6980"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800458"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.007.2100545"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3026466"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3138612"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2933962"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Timothy P. Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. (2015). DOI:10.48550\/ARXIV.1509.02971","DOI":"10.48550\/ARXIV.1509.02971"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.5555\/3091574.3091594"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2013.6567117"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2014.2310747"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Ryan Lowe Yi Wu Aviv Tamar Jean Harb Pieter Abbeel and Igor Mordatch. 2017. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. (2017). DOI:10.48550\/ARXIV.1706.02275","DOI":"10.48550\/ARXIV.1706.02275"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing Atari with Deep Reinforcement Learning. (2013). DOI:10.48550\/ARXIV.1312.5602","DOI":"10.48550\/ARXIV.1312.5602"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2014.012314.00178"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2933973"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036962"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1618130114"},{"key":"e_1_3_1_29_2","unstructured":"Lewis A Rossman et\u00a0al. 2000. EPANET 2: Users manual. (2000)."},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59625-9"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/DCOSS.2014.62","volume-title":"2014 IEEE International Conference on Distributed Computing in Sensor Systems","author":"Shafagh Hossein","year":"2014","unstructured":"Hossein Shafagh and Anwar Hithnawi. 2014. Security comes first, a public-key cryptography framework for the internet of things. In 2014 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, 135\u2013136."},{"key":"e_1_3_1_32_2","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"12","author":"Sutton Richard S","year":"1999","unstructured":"Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems 12 (1999).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_33_2","volume-title":"Python 3 Reference Manual","author":"Rossum Guido Van","year":"2009","unstructured":"Guido Van Rossum and Fred L. Drake. 2009. Python 3 Reference Manual. CreateSpace, Scotts Valley, CA."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900045"},{"key":"e_1_3_1_35_2","unstructured":"Zhuangkun Wei and Weisi Guo. 2021. Random matrix based physical layer secret key generation in static channels. arXiv:2110.12785. Retrieved from https:\/\/arxiv.org\/abs\/2110.12785"},{"key":"e_1_3_1_36_2","first-page":"1","article-title":"A multi-eavesdropper scheme against RIS secured los-dominated channel","author":"Wei Zhuangkun","year":"2022","unstructured":"Zhuangkun Wei, Weisi Guo, and Bin Li. 2022. A multi-eavesdropper scheme against RIS secured los-dominated channel. IEEE Communications Letters (2022), 1\u20131.","journal-title":"IEEE Communications Letters"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22103951"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2013.130911"},{"issue":"1","key":"e_1_3_1_39_2","first-page":"1","article-title":"Auto-key: Using autoencoder to speed up gait-based key generation in body area networks","volume":"4","author":"Wu Yuezhong","year":"2020","unstructured":"Yuezhong Wu, Qi Lin, Hong Jia, Mahbub Hassan, and Wen Hu. 2020. Auto-key: Using autoencoder to speed up gait-based key generation in body area networks. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1\u201323.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2965959"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2892461"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2929362"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2521718"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2877201"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800455"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3096384"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600267CM"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2018.2825158"}],"container-title":["ACM Transactions on Privacy and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711900","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:09Z","timestamp":1750295889000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,22]]},"references-count":47,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3711900"],"URL":"https:\/\/doi.org\/10.1145\/3711900","relation":{},"ISSN":["2471-2566","2471-2574"],"issn-type":[{"type":"print","value":"2471-2566"},{"type":"electronic","value":"2471-2574"}],"subject":[],"published":{"date-parts":[[2025,2,22]]},"assertion":[{"value":"2024-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}