Artificial Intelligence for Physical Layer Security in 6G Wireless Networks: Challenges and Opportunities- A Systematic Review
DOI:
https://doi.org/10.63746/njtd.v23i1.4355Keywords:
6G, Physical Layer Security (PLS), Artificial Intelligence, Machine Learning, Wireless Communications, Terahertz, Edge Intelligence.Abstract
Since the introduction of 5G systems, the Sixth Generation (6G) network has quickly become a key area of research interest. It is anticipated that 6G, which is envisioned as an integrated communication architecture spanning space, air, ground, and sea, will accommodate a variety of applications with demanding security and quality-of-service specifications. However, 6G has never-before-seen security flaws because of its open wireless channels, dynamic topology distributed architecture, and strong reliance on machine learning (ML), artificial intelligence (AI), and cutting-edge technologies like terahertz and quantum communications. These issues are difficult for traditional cryptography-based security procedures to handle, especially in low-cost, power-constrained Internet of Things (IoT) devices and large-scale computing environments.
In the past years, physical layer security (PLS) has been studied and indicated as a possible way to emancipate networks from classical, complexity-based security approaches. Multiple white papers on the vision for 6G incorporate PLS, as well as the IEEE International Network Generations Roadmap (INGR) 1st and 2nd Editions. Motivated by the above, a key point of this paper is to showcase how PLS and, in general, security controls at the PHY level can be exploited towards securing future networks. A supplementary method for protecting 6G systems is Physical Layer Security (PLS) approaches, which take advantage of intrinsic hardware flaws and the stochastic nature of wireless channels. Intelligent PLS solutions have proven to perform better than traditional security methods due to the quick developments in AI and ML.
In this paper, we present a comprehensive review of machine learning-enabled PLS strategies for 6G. We begin by outlining the vision of 6G, its key applications, enabling radio technologies, security threats, and security requirements. We then discuss prominent ML techniques that motivate the introduction of intelligent PLS mechanisms in 6G. Subsequently, we examine state-of-the-art scholarly contributions on ML-driven PLS across critical 6G radio technologies, including ambient backscatter communication, cell-free massive MIMO, terahertz communication, visible light communication, configurable intelligent surfaces (CIS), and molecular communication. Furthermore, we provide an in-depth analysis of ML-enabled PLS advancements such as intelligent attack detection, anti-jamming strategies, secure wireless transmission, physical layer authentication, and key generation. Finally, we highlight open challenges and future research opportunities to foster the development of intelligent, robust, and secure 6G systems. This review aims to serve as a reference point for researchers and practitioners while inspiring further exploration at the intersection of machine learning and physical layer security in 6G networks.
References
Abbas, S., Abu Talib, M., Nasir, Q., Idhis, S., Alaboudi, M., & Mohamed, A. (2024). Radio frequency fingerprinting techniques for device identification: a survey. International Journal of Information Security, 23(2), 1389-1427.
Aladi, A. A. (2023). Communication Security through Physical-Layer Techniques (Doctoral dissertation, The University of Manchester (United Kingdom)).
Aldirmaz-Colak, S., Namdar, M., Basgumus, A.,
Özyurt, S., Kulac, S., Calik, N., ... & Durak-Ata, L. (2025). A comprehensive review on ISAC for 6G: Enabling technologies, security, and AI/ML perspectives. IEEE Access.
Alhammadi, A., Shayea, I., El-Saleh, A. A., Azmi, M. H., Ismail, Z. H., Kouhalvandi, L., & Saad, S. A. (2024). Artificial intelligence in 6G wireless networks: Opportunities, applications, and challenges. International Journal of Intelligent Systems, (1), 8845070.
Alhoraibi, L., Alghazzawi, D., Alhebshi, R., & Rabie, O. B. J. (2023). Physical layer authentication in wireless networks-based machine learning approaches. Sensors, 23(4), 1814.
Barnard, P., Marchetti, N., & DaSilva, L. A. (2022). Robust network intrusion detection through explainable artificial intelligence (XAI). IEEE Networking Letters, 4(4), 167-171.
Basharat, S., Hassan, S. A., Pervaiz, H., Mahmood, A., Ding, Z., & Gidlund, M. (2022). Intelligent decision making framework for 6G network. China Communications, 19(3), 16-35.
Betzelos, C., Uzunidis, D., Karkazis, P., & Leligou, H. C. (2024, October). Enhancing Reliability in 6G Networks Based on User-Driven AI. In 2020 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1-6). IEEE.
Bloch, M., & Barros, J. (2011). Physical-Layer Security: From Information Theory to Security Engineering. Cambridge University Press.
Chataut, R., Nankya, M., & Akl, R. (2024). 6G networks and the AI revolution—Exploring technologies, applications, and emerging challenges. Sensors, 24(6), 1888.
Chen, S., Liang, Y.-C., Sun, S., Kang, X., & Li, Y. (2020). Machine learning for wireless sensing, localization, and communications. IEEE Communications Magazine, 58(6), 80-86.
Dang, S., Amin, O., Shihada, B., & Alouini, M. S. (2020). What should 6G be?. Nature Electronics, 3(1), 20-29.
El-Hajj, M. (2025). Secure and trustworthy open radio access network (O-RAN) optimization: A zero-trust and federated learning framework for 6G networks. Future Internet, 17(6), 233.
Ge, Y., & Fan, J. (2021). Robust secure beamforming for intelligent reflecting surface assisted full-duplex MISO systems. IEEE Transactions on Information Forensics and Security, 17, 253-264.
Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024). Artificial Intelligence Trust, risk and security management (AI trism): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240, 122442.
Hoang, T. M., Vahid, A., Tuan, H. D., & Hanzo, L. (2024). Physical layer authentication and security design in the machine learning era. IEEE Communications Surveys & Tutorials, 26(3), 1830-1860.
Hong, S., Pan, C., Ren, H., Wang, K., & Nallanathan, A. (2020). Artificial-noise-aided secure MIMO wireless communications via intelligent reflecting surface. IEEE Transactions on Communications, 68(12), 7851-7866.
Hoque, S., Aydeger, A., Zeydan, E., & Liyanage, M. (2025). A survey on distributed denial of service attack mitigation for 5G and beyond. IEEE Open Journal of the Communications Society.
Huang, D., Al-Hourani, A., Sithamparanathan, K., Rowe, W. S., Bulot, L., & Thompson, A. (2021). Deep learning methods for device authentication using RF fingerprinting. In 2021 15th International Conference on Signal Processing and Communication Systems (ICSPCS) (pp. 1-7). IEEE.
Jiang, W., Han, B., Habibi, M. A., & Schotten, H. D. (2021). The road towards 6G: A comprehensive survey. IEEE Open Journal of the Communications Society, 2, 334-366.
Jornet, J. M., & Akyildiz, I. F. (2014). Graphene-based plasmonic nano-antenna for terahertz band communication in nanonetworks. IEEE Journal on Selected Areas in Communications, 31(12), 685-694.
Kaur, M., Alzubi, A. A., Walia, T. S., Yadav, V., Kumar, N., Singh, D., & Lee, H. N. (2023). EGCrypto: A low-complexity elliptic galois cryptography model for secure data transmission in IoT. IEEE Access, 11, 90739-90748.
Kazmi, S. H. A., Hassan, R., Qamar, F., Nisar, K., & Ibrahim, A. A. A. (2023). Security concepts in emerging 6G communication: Threats, countermeasures, authentication techniques and research directions. Symmetry, 15(6), 1147.
Kim, T. H., Kumar, G., Saha, R., Buchanan, W. J., Devgun, T., & Thomas, R. (2021). LiSP-XK: extended light-weight signcryption for IoT in resource-constrained environments. IEEE Access, 9, 100972-100980.
Kuchuk, H., & Malokhvii, E. (2024). Integration of IoT with cloud, fog, and edge computing: a review. Advanced Information Systems, 8(2), 65-78.
Kukli?ski, S., Tomaszewski, L., Ko?akowski, R., & Chemouil, P. (2021). 6G-LEGO: A framework for 6G network slices. Journal of Communications and Networks, 23(6), 442-453.
Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 57(8), 84-90.
Lo Cigno, R., Gringoli, F., Bartoletti, S., Cominelli, M., Ghiro, L., & Zanini, S. (2025). Communication and sensing: Wireless PHY-layer threats to security and privacy for IoT systems and possible countermeasures. Information, 16(1), 1-18.
Lockey, S., Gillespie, N., Holm, D., & Someh, I. A. (2021). A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions.
Lu, Y., Shi, Y., & Zhang, J. (2022). Learning-based physical layer security: Challenges and opportunities. IEEE Wireless Communications, 29(2), 62-69.
Mahmoud, H., Ismail, T., Baiyekusi, T., & Idrissi, M. (2024). Advanced security framework for 6G networks: Integrating deep learning and physical layer security. Network, 4(4), 453-467.
Mahmoud, H., Ismail, T., Baiyekusi, T., & Idrissi, M. (2024). Advanced security framework for 6G networks: Integrating deep learning and physical layer security. Network, 4(4), 453-467.
Mahyoub, M., AbdulGhaffar, A., Alalade, E., Ndubisi, E., & Matrawy, A. (2024). Security analysis of critical 5G interfaces. IEEE Communications Surveys & Tutorials, 26(4), 2382-2410.
Mao, B., Liu, J., Wu, Y., & Kato, N. (2023). Security and privacy on 6G network edge: A survey. IEEE Communications Surveys & Tutorials, 25(2), 1095-1127.
Mitev, M., Pham, T. M., Chorti, A., Barreto, A. N., & Fettweis, G. (2023). Physical layer security—from theory to practice. IEEE BITS the Information Theory Magazine, 3(2), 67-79.
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Li, J., & Poor, H. V. (2021). Federated learning for smart healthcare: A survey. ACM Computing Surveys, 55(3), 1-37.
Olawoyin, L. A., Wu, Y., Yang, H., Salihu, B. A., & Abana, M. A. (2015). Secrecy enhancing in wireless communication with a full-duplexing at the receiver. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1-5). IEEE.
Oukebdane, M. A., Shah, A. S., Azad, A. K., Ekoru, J., & Madahana, M. (2025). Unraveling the nexus of ML and 6G: Challenges, Opportunities, and Future Directions. IEEE Access.
Paolini, E., Valcarenghi, L., Maggiani, L., & Andriolli, N. (2023). Real-time clustering based on deep embeddings for threat detection in 6G networks. IEEE Access, 11, 115827-115835.
Park, J., Samarakoon, S., Shiri, H., Abdel-Aziz, H., Nishio, T. K., Elgabli, A., & Bennis, M. (2020). Extreme urllc: Vision, challenges, and key enablers. arXiv preprint arXiv:2001.09683.
Poor, H. V., & Schaefer, R. F. (2017). Wireless physical layer security. Proceedings of the National Academy of Sciences, 114(1), 19-26.
Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021). The roadmap to 6G security and privacy. IEEE Open Journal of the Communications Society, 2, 1094-1122.
Qu, K., Ye, J., Li, X., & Guo, S. (2024). Privacy and security in ubiquitous integrated sensing and communication: Threats, challenges and future directions. IEEE Internet of Things Magazine, 7, 52-58.
Rappaport, T. S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G. N., Schulz, J. K., Samimi, M., & Gutierrez, F. (2013). Millimeter wave mobile communications for 5G cellular: It will work! IEEE Access, 1, 335-349.
Rappaport, T. S., Xing, Y., MacCartney, G. R., Molisch, A. F., Mellios, E., & Zhang, J. (2019). Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models. IEEE Transactions on Antennas and Propagation, 65(12), 6213-6230.
Ray, P. P. (2025). A review on LLMs for IoT ecosystem: State-of-the-art, lightweight models, use cases, key challenges, future directions. Authorea Preprints.
Rifa-Pous, H., Garcia-Font, V., Nunez-Gomez, C., & Salas, J. (2024). Security, trust and privacy challenges in AI-driven 6G networks. arXiv preprint arXiv:2409.10337.
Saad, W., Bennis, M., & Chen, M. (2020). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134-142.
Saeed, M. M., Saeed, R. A., Abdelhaq, M., Alsaqour, R., Hasan, M. K., & Mokhtar, R. A. (2023). Anomaly detection in 6G networks using machine learning methods. Electronics, 12(15), 3300.
Sakraoui, S., Derdour, M., Ahmim, A., Almukhlifi, R., Ahmim, M., & Ullah, I. (2025). TL2AB: Trusted lightweight authentication using AI and blockchain for 6G networks. Internet of Things, *101661*.
Sanenga, A., Mapunda, G. A., Jacob, T. M. L., Marata, L., Basutli, B., & Chuma, J. M. (2020). An overview of key technologies in physical layer security. Entropy, 22(11), 1261.
Shen, S., Yu, C., Zhang, K., Ni, J., & Ci, S. (2021). Adaptive and dynamic security in AI-empowered 6G: From an energy efficiency perspective. IEEE Communications Standards Magazine, 5(3), 80-88.
Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2021). AI and 6G security: Opportunities and challenges. In *2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)* (pp. 616-621). IEEE.
Visave, J. (2025). Transparency in AI for emergency management: Building trust and accountability. AI and Ethics, 1-14.
Wang, H., Lv, T., Cao, Y., Li, W., Zeng, J., Huang, P., & Khan, M. K. (2025). Navigating the dual-use nature and security implications of reconfigurable intelligent surfaces in next-generation wireless systems. arXiv preprint.
Zhang, J., Letaief, K. B., Sun, S., & Cheng, J. (2021). Physical layer security for 6G: A survey. IEEE Communications Surveys & Tutorials, 23(4), 2454-2482.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form can be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
