Open Access

ARTICLE

Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

Muhammad Waqas^1,3,*, Shehr Bano², Fatima Hassan², Shanshan Tu¹, Ghulam Abbas², Ziaul Haq Abbas⁴

1 Engineering Research Center of Intelligent Perception and Autonomous Control, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan
3 School of Engineering, Edith Cowan University, Joondalup Perth, 6027, WA Australia
4 Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, 23460, Pakistan

* Corresponding Author: Muhammad Waqas. Email:

Computers, Materials & Continua 2022, 73(3), 4489-4499. https://doi.org/10.32604/cmc.2022.029539

Received 06 March 2022; Accepted 25 April 2022; Issue published 28 July 2022

Abstract

Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks. Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target. These methods perform well but only in stationary scenarios. We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios. The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data. The authentication of the received signal is considered a binary classification problem. The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information. Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks. It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile. The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

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Keywords

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