| 1. |
- Pan, Fei, et al.
(författare)
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Clone Detection Based on Physical Layer Reputation for Proximity Service
- 2019
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 3948-3957
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Tidskriftsartikel (refereegranskat)abstract
- Proximity-based service (ProSe) provides direct communications among smart sensor nodes in proximity which aims at reserving resource consumption and alleviating the load in base stations, which is a promising solution for smart sensor systems that possess limited computing and energy resources. During the ProSe direct communications, most of the prior art security methods are usually provided by the ProSe function and are based on complex cryptography. However, despite the computing complexity, it is difficult for cryptographic methods to detect clone attack which is a common kind of attack in sensor systems. Clone nodes feature different physical positions but claim colliding IDs with captured nodes. Thus, clone nodes can be detected by spatial differences, in particular, by the surveillance of physical layer channel state information (CSI). However, CSI is not absolute static due to the random noise in wireless propagation environment. Accordingly, the detection accuracy varies with the stability of CSI. To address this challenge, we take the first attempt to introduce physical layer reputation and then elaborate the physical layer reputation based clone detection protocol to detect clone attack in multiple scenarios. The proposed protocol significantly improves the detection rate and false alarm rate and it is validated both by simulations and realizations.
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| 2. |
- Pan, Fei, et al.
(författare)
-
Threshold-Free Physical Layer Authentication Based on Machine Learning for Industrial Wireless CPS
- 2019
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Ingår i: IEEE Transactions on Industrial Informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 15:12, s. 6481-6491
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Tidskriftsartikel (refereegranskat)abstract
- Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.
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