Explainable adversarial learning framework on physical layer key generation combating malicious reconfigurable intelligent surface
| dc.contributor.author | Wei, Zhuangkun | |
| dc.contributor.author | Hu, Wenxiu | |
| dc.contributor.author | Zhang, Junqing | |
| dc.contributor.author | Guo, Weisi | |
| dc.contributor.author | McCann, Julie | |
| dc.date.accessioned | 2025-02-18T15:53:38Z | |
| dc.date.available | 2025-02-18T15:53:38Z | |
| dc.date.freetoread | 2025-02-18 | |
| dc.date.issued | 2025 | |
| dc.date.pubOnline | 2025-01-28 | |
| dc.description.abstract | Reconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL-SKG, a malicious RIS can poison legitimate channels and crack almost all existing PL-SKGs. In this work, we propose an adversarial learning framework that addresses Man-in-the-middle RIS (MITM-RIS) eavesdropping which can exist between legitimate parties, namely Alice and Bob. First, the theoretical mutual information gap between legitimate pairs and MITM-RIS is deduced. From this, Alice and Bob leverage adversarial learning to learn a common feature space that assures no mutual information overlap with MITM-RIS. Next, to explain the trained legitimate common feature generator, we aid signal processing interpretation of black-box neural networks using a symbolic explainable AI (xAI) representation. These symbolic terms of dominant neurons aid the engineering of feature designs and the validation of the learned common feature space. Simulation results show that our proposed adversarial learning- and symbolic-based PL-SKGs can achieve high key agreement rates between legitimate users, and is further resistant to an MITM-RIS Eve with the full knowledge of legitimate feature generation (NNs or formulas). This therefore paves the way to secure wireless communications with untrusted reflective devices in future 6G. | |
| dc.description.journalName | IEEE Transactions on Wireless Communications | |
| dc.description.sponsorship | Engineering and Physical Sciences Research Council, UK Research and Innovation | |
| dc.description.sponsorship | This work is supported by the Engineering and Physical Sciences Research Council: Communications Hub For Empowering Distributed ClouD Computing Applications And Research (CHEDDAR) grant id: EP/X040518/1 and EP/Y037421/1. | |
| dc.format.extent | pp. xx-xx | |
| dc.identifier.citation | Wei Z, Hu W, Zhang J, et al., (2025) Explainable adversarial learning framework on physical layer key generation combating malicious reconfigurable intelligent surface. IEEE Transactions on Wireless Communications, Available online 28 January 2025 | |
| dc.identifier.eissn | 1558-2248 | |
| dc.identifier.elementsID | 563513 | |
| dc.identifier.issn | 1536-1276 | |
| dc.identifier.issueNo | ahead-of-print | |
| dc.identifier.uri | https://doi.org/10.1109/twc.2025.3531799 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/23492 | |
| dc.identifier.volumeNo | ahead-of-print | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.publisher.uri | https://ieeexplore.ieee.org/document/10856736 | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | 4613 Theory Of Computation | |
| dc.subject | 40 Engineering | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 4006 Communications Engineering | |
| dc.subject | 4611 Machine Learning | |
| dc.subject | Networking & Telecommunications | |
| dc.subject | 4006 Communications engineering | |
| dc.subject | 4008 Electrical engineering | |
| dc.subject | 4606 Distributed computing and systems software | |
| dc.title | Explainable adversarial learning framework on physical layer key generation combating malicious reconfigurable intelligent surface | |
| dc.type | Article | |
| dcterms.dateAccepted | 2025-01-08 |