6G Security Challenges and Potential Solutions

Although the fifth generation wireless networks are yet to be fully investigated, the vision and key elements of the 6th generation (6G) ecosystem have already come into discussion. In order to contribute to these efforts and delineate the security and privacy aspects of 6G networks, we survey how security may impact the envisioned 6G wireless systems with the possible challenges and potential solutions. Especially, we discuss the security and privacy challenges that may emerge with the 6G requirements, novel network architecture, applications and enabling technologies including distributed ledger technologies, physical layer security, distributed artificial intelligence (AI)/ machine learning (ML), Visible Light Communication (VLC), THz bands, and quantum communication

[1]  Tarik Taleb,et al.  ZSM Security: Threat Surface and Best Practices , 2020, IEEE Network.

[2]  Mehdi Bennis,et al.  On-Device Federated Learning via Blockchain and its Latency Analysis , 2018, ArXiv.

[3]  Guan Gui,et al.  6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence , 2020, IEEE Wireless Communications.

[4]  Wei Chen,et al.  The Roadmap to 6G: AI Empowered Wireless Networks , 2019, IEEE Communications Magazine.

[5]  Falko Dressler,et al.  Towards security in nano-communication: Challenges and opportunities , 2012, Nano Commun. Networks.

[6]  Michał Choraś,et al.  Defending network intrusion detection systems against adversarial evasion attacks , 2020, Future Gener. Comput. Syst..

[7]  Erik G. Larsson,et al.  Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts , 2020, Science China Information Sciences.

[8]  Tianqing Zhu,et al.  Security and privacy in 6G networks: New areas and new challenges , 2020, Digit. Commun. Networks.

[9]  Theocharis Theocharides,et al.  Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications , 2018, 2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP).

[10]  P. Varalakshmi,et al.  Multifaceted trust management framework based on a trust level agreement in a collaborative cloud , 2017, Comput. Electr. Eng..

[11]  Ruidong Li,et al.  Blockchain-Based Data Security for Artificial Intelligence Applications in 6G Networks , 2020, IEEE Network.

[12]  Nei Kato,et al.  When Machine Learning Meets Privacy in 6G: A Survey , 2020, IEEE Communications Surveys & Tutorials.

[13]  Jianjun Ma,et al.  Security and eavesdropping in terahertz wireless links , 2018, Nature.

[14]  Mohammad Dehghani Soltani,et al.  Physical Layer Security for Visible Light Communication Systems: A Survey , 2019, IEEE Communications Surveys & Tutorials.

[15]  Davide Bacco,et al.  Feasibility of Quantum Communications in Aquatic Scenario , 2018, 2018 IEEE Photonics Conference (IPC).

[16]  Shree Krishna Sharma,et al.  Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future , 2019, IEEE Access.

[17]  Shunliang Zhang,et al.  Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities , 2020, Comput. Networks.

[18]  Massimiliano Pierobon,et al.  Secrecy Capacity and Secure Distance for Diffusion-Based Molecular Communication Systems , 2019, IEEE Access.

[19]  Anupam Joshi,et al.  Preventing Poisoning Attacks On AI Based Threat Intelligence Systems , 2018, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

[20]  Ripon Patgiri,et al.  6G Communication Technology: A Vision on Intelligent Healthcare , 2020, Health Informatics.

[21]  Madhusanka Liyanage,et al.  The Role of Blockchain in 6G: Challenges, Opportunities and Research Directions , 2020, 2020 2nd 6G Wireless Summit (6G SUMMIT).