Multiuser Physical Layer Authentication in Internet of Things With Data Augmentation

Liao, Runfa; Wen, Hong; Chen, Songlin; Xie, Feiyi; Pan, Fei; Tang, Jie; Song, Huanhuan

doi:10.1109/jiot.2019.2960099

Cited by 66 publications

(30 citation statements)

References 34 publications

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“…Data augmentation techniques are mainly based on generating artificial data from the existing dataset. The limited data problem of PHY security DL networks has been considered by Liao et al in [267]. Liao et al previously worked on DL-assisted PHY authentication in [263].…”

Section: A Anti-spoofing Solutionsmentioning

confidence: 99%

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Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: A Anti-spoofing Solutionsmentioning

confidence: 99%

“…Liao et al previously worked on DL-assisted PHY authentication in [263]. In [267], the authors argue that wireless networks are especially vulnerable to limited data as a result of channel coherence time. Hence, the authors propose three data augmentation methods to improve the training speed and authentication accuracy.…”

Section: A Anti-spoofing Solutionsmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…2) DL-enabled applications: Numerous IoT and IIoT applications are employing the DL concept ranging from data mining of surveillance videos [34] to user authentication in the physical network layer [35], [36]. The excellent features of DL are the enormous capacity of recognizing patterns, which leads to many applications in smart cities.…”

Section: B Applicationsmentioning

confidence: 99%

“…Another example demonstrated in [36] uses authentication in the physical layer by comparing channel impulse response, such as channel state information, channel phase response, and received signal strength as the comparison is performed using a threshold, hence making it harder to authenticate where there are multiple nodes at the same time. The solution is to use Deep Neural Network (DNN) for faster and scalable authentication, which allowed for impressive results.…”

Section: B Authentication Systemsmentioning

confidence: 99%

“…Authentication Systems [8] Very reliable and low error rate There is a tradeoff between the robustness and security depending on the application [11] Greatly increase its authentication rate as the number of devices is the target If the number of targets is too low it can have a low rate of successes [59] High accuracy and can be applied to a highly resource-limited device Too dependent on the training of the RNN [36] High accuracy and fast training with data augmentation Can be resource demanding Surveillance [62] High accuracy even with low quality and unstable image High computational cost and not suitable for local processing [3] Low computational power, capable of running in a local device Cannot detect the anomalous behavior and lacks smoke and fire detection [63] Appropriate for both static and dynamic salience computation…”

Section: Conclusion and Future Trendsmentioning

confidence: 99%

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