Learning Enabled Adaptive Multiple Attribute-based Physical Layer Authentication

Fang, Xiaojie; Yin, Xinyu; Mei, Lin; Zhang, Ning; Sha, Xuejun; Qiu, Jinghui

doi:10.1109/vtc2020-fall49728.2020.9348774

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…The point in the figure is close to the origin, indicating the good performance of physical layer authentication. It can be observed that when P f = 0 , P m obtained based on the scheme in Fang et al 16 is close to 1, but based on our scheme, P m is lower than 0.09 in all cases except for a single attribute of RSSI is utilized as authentication fingerprint. The points depicted based on our scheme are closer to the origin, and P m and P f can converge to 0 simultaneously, that is, the authentication performance is better.…”

Section: Performance Evaluationmentioning

confidence: 60%

“…The points depicted based on our scheme are closer to the origin, and P m and P f can converge to 0 simultaneously, that is, the authentication performance is better. For example, the closest point to the origin based on our scheme is ( 8 . 79 × 10 − 3 , 2 . 48 × 10 − 3 ) , while the closest point to the origin based on the scheme in Fang et al 16 is approximated as (0.01, 0.09). The dashed line in the figure is always lower than the solid line of the same color, which shows that the authentication achieved by our scheme is always better than that of Fang et al, 16 whether using single or multiple attributes as authentication fingerprints.…”

Section: Performance Evaluationmentioning

confidence: 97%

“…Authentication methods based on kernel machines fusing multiple attributes to achieve reliable authentication in time-varying environments are presented in Fang et al 1 The authors of this article also propose hierarchical authentication and progressive authorization with high complexity in Fang et al 15 This strategy is used to resist the risks associated with missed detections with one-time binary authentication. Fang et al 16 suggest selecting those physical layer attributes that have historically performed well for authentication as the basis for current authentication. However, it is also necessary to select the appropriate ML algorithm and carefully design the authentication process to improve authentication accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 14 characterizes the influence of different authentication schemes on authentication performance, which quantifies the P m versus P f . Fang et al 16 consider that different attributes are suitable for physical layer authentication in different communication scenarios. When encountering complex wireless scenarios, the security performance of authentication scheme will deteriorate if a fixed physical layer attribute is used as authentication fingerprints.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The solid line in the figure depicts the relationship between P m and P f implemented in Fang et al 16 based on different authentication fingerprints. The dashed line depicts the performance achieved based on our proposed scheme.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 4 more Smart Citations

Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things

Jing

Huang

et al. 2022

International Journal of Distributed Sensor Networks

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With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation–conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank–Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P f reduced to 0.0263% and the miss detection rate P m reduced to 0.3466%.

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