Secure monitoring of Internet of vehicles in 6G networks through intelligent reflecting surfaces leveraging AI^☆

Abstract

The ensemble of Information and Communication Technology (ICT) and Artificial Intelligence (AI) has catalysed many developments and innovations in the automotive industry. 6G networks emerge as a promising technology for realising Intelligent Transport Systems (ITS), which benefits the drivers and society. As the network is highly heterogeneous and robust, the physical layer security and node reliability of the vehicles hold paramount significance. This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble (LSLE) of Machine Learning (ML) algorithms to predict the presence of intruders in the network. Furthermore, our work utilizes a Deep Convolutional Neural Network (DCNN) to detect obstacles by identifying the Region of Interest (ROI) in the images. As the network utilizes mm-waves with shorter wavelengths, Intelligent Reflecting Surfaces (IRS) are employed to redirect signals to legitimate nodes, thereby mitigating the malicious activity of intruders. The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy, False Positive Rate (FPR), Recall, F1-Score, and Precision. A consistent performance improvement with an average FPR of 85.08% and accuracy of 92.01% is achieved by the model. Thus, in the future, detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results.

Keywords

Intelligent reflecting surface / 6G / AI / Deep convolution neural network / Super learning / Meta learner / Intelligent transport systems

References

[1] J.-C. Chen, Machine learning-inspired algorithmic framework for intelligent reflect-ing surface-assisted wireless systems, IEEE Trans. Veh. Technol. 70 (10) (2021) 10671-10685.

[2] W. Tong, A. Hussain, W.X. Bo, S. Maharjan, Artificial intelligence for vehicle-to-everything: a survey, IEEE Access 7 (2019) 10823-10843.

[3] L. Liang, H. Peng, G.Y. Li, X. Shen, Vehicular communications: a physical layer per-spective, IEEE Trans. Veh. Technol. 66 (12) (2017) 10647-10659.

[4] GSMA C.V. Today, Connecting Vehicles Today and in the 5G Era with C-V2X (Cel-lular Vehicle-to-Everything), GSMA, 2019.

[5] S. Sharanya, S. Karthikeyan, Classifying malicious nodes in vanets using support vector machines with modified fading memory, J. Eng. Appl. Sci. 12 (1) (2017) 171-176.

[6] C. De Alwis, A. Kalla, Q.-V. Pham, P. Kumar, K. Dev, W.-J. Hwang, M. Liyanage, Survey on 6g frontiers: trends, applications, requirements, technologies and future research, IEEE Open J. Commun. Soc. 2 (2021) 836-886.

[7] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, M. Zorzi, Toward 6g networks: use cases and technologies, IEEE Commun. Mag. 58 (3) (2020) 55-61.

[8] C. Huang, A. Zappone, G.C. Alexandropoulos, M. Debbah, C. Yuen, Reconfigurable intelligent surfaces for energy efficiency in wireless communication, IEEE Trans. Wirel. Commun. 18 (8) (2019) 4157-4170.

[9] Q. Wu, R. Zhang, Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming, IEEE Trans. Wirel. Commun. 18 (11) (2019) 5394-5409.

[10] Q. M., W. X., T. Cui, et al., Coding metamaterials, digital metamaterials and pro-grammable metamaterials, Light: Sci. Appl. (2014).

[11] Q. Wu, S. Zhang, B. Zheng, C. You, R. Zhang, Intelligent reflecting surface-aided wire-less communications: a tutorial, IEEE Trans. Commun. 69 (5) (2021) 3313-3351.

[12] S. Abeywickrama, R. Zhang, Q. Wu, C. Yuen, Intelligent reflecting surface: practi-cal phase shift model and beamforming optimization, IEEE Trans. Commun. 68 (9)(2020) 5849-5863.

[13] S. Chen, Y.-C. Liang, S. Sun, S. Kang, W. Cheng, M. Peng, Vision, requirements, and technology trend of 6g: how to tackle the challenges of system coverage, capacity, user data-rate and movement speed, IEEE Wirel. Commun. 27 (2) (2020) 218-228.

[14] E. Basar, I. Yildirim, F. Kilinc, Indoor and outdoor physical channel modeling and efficient positioning for reconfigurable intelligent surfaces in mmwave bands, IEEE Trans. Commun. 69 (12) (2021) 8600-8611.

[15] X. Zhou, B. Maham, A. Hjorungnes, Pilot contamination for active eavesdropping, IEEE Trans. Wirel. Commun. 11 (3) (2012) 903-907.

[16] F. Tang, Y. Kawamoto, N. Kato, J. Liu, Future intelligent and secure vehicular net-work toward 6g: machine-learning approaches, Proc. IEEE 108 (2) (2019) 292-307.

[17] Z. Zhang, Y. Cao, Z. Cui, W. Zhang, J. Chen, A many-objective optimization based in-telligent intrusion detection algorithm for enhancing security of vehicular networks in 6g, IEEE Trans. Veh. Technol. 70 (6) (2021) 5234-5243.

[18] Y. Hui, N. Cheng, Z. Su, Y. Huang, P. Zhao, T.H. Luan, C. Li, Secure and personal-ized edge computing services in 6g heterogeneous vehicular networks, IEEE Internet Things J. 9 (8) (2021) 5920-5931.

[19] S.A. Khowaja, P. Khuwaja, K. Dev, I.H. Lee, W.U. Khan, W. Wang, N.M.F. Qureshi, M. Magarini, A secure data sharing scheme in community segmented vehicular social networks for 6g, IEEE Trans. Ind. Inform. 19 (1) (2022) 890-899.

[20] J. Zhu, R. Schober, V.K. Bhargava, Secure transmission in multicell massive mimo systems, IEEE Trans. Wirel. Commun. 13 (9) (2014) 4766-4781.

[21] J. Zhu, R. Schober, V.K. Bhargava, Linear precoding of data and artificial noise in se-cure massive mimo systems, IEEE Trans. Wirel. Commun. 15 (3) (2015) 2245-2261.

[22] J. Wang, J. Lee, F. Wang, T.Q. Quek, Jamming-aided secure communication in mas-sive mimo Rician channels, IEEE Trans. Wirel. Commun. 14 (12) (2015) 6854-6868.

[23] B. Chen, C. Zhu, L. Shu, M. Su, J. Wei, V.C. Leung, J.J. Rodrigues, Securing uplink transmission for lightweight single-antenna ues in the presence of a massive mimo eavesdropper, IEEE Access 4 (2016) 5374-5384.

[24] H. Wei, D. Wang, X. Hou, Y. Zhu, J. Zhu,Secrecy analysis for massive mimo systems with internal eavesdroppers, in: 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), IEEE, 2015, pp. 1-5.

[25] Y.O. Basciftci, C.E. Koksal, A. Ashikhmin, Securing massive mimo at the physical layer, in: 2015 IEEE Conference on Communications and Network Security (CNS), IEEE, 2015, pp. 272-280.

[26] A. Renda, P. Ducange, F. Marcelloni, D. Sabella, M.C. Filippou, G. Nardini, G. Stea, A. Virdis, D. Micheli, D. Rapone, et al., Federated learning of explainable ai models in 6g systems: towards secure and automated vehicle networking, Information 13 (8)(2022) 395.

[27] D. Choudhary, R. Pahuja, Deep learning approach for encryption techniques in ve-hicular networks, Wirel. Pers. Commun. 125 (1) (2022) 1-27.

[28] Y. Cao, S. Xu, J. Liu, N. Kato, Toward smart and secure v2x communication in 5g and beyond: a uav-enabled aerial intelligent reflecting surface solution, IEEE Veh. Technol. Mag. 17 (1) (2022) 66-73.

[29] J.B.A. Nathan Blaunstein, Multipath Phenomena in Cellular Networks Artech House Mobile Communications Series Mobile Communications Series, Artech House, 2022.

[30] Z. Zhang, Y. Cao, Z. Cui, W. Zhang, J. Chen, A many-objective optimization based in-telligent intrusion detection algorithm for enhancing security of vehicular networks in 6g, IEEE Trans. Veh. Technol. 70 (6) (2021) 5234-5243.

[31] E.P. Valentini, G.P. Rocha Filho, R.E. De Grande, C.M. Ranieri, L.A.P. Júnior, R.I. Meneguette, A novel mechanism for misbehavior detection in vehicular networks, IEEE Access 11 (2023) 68113-68126.

[32] E.A. Shams, A. Rizaner, A.H. Ulusoy, Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks, Comput. Secur. 78 (2018) 245-254.

[33] S.S. Priscila, A. Sharma, S. Vanithamani, F. Ahmad, R. Mahaveerakannan, A.J. Al-rubaie, V. Jagota, B.K. Singh, [retracted] risk-based access control mechanism for Internet of vehicles using artificial intelligence, Secur. Commun. Netw. 2022 (1)(2022) 3379843.

[34] H.H.R. Sherazi, R. Iqbal, F. Ahmad, Z.A. Khan, M.H. Chaudary, Ddos attack detec-tion: a key enabler for sustainable communication in Internet of vehicles, Sustain. Comput. Inform. Syst. 23 (2019) 13-20.

[35] M. Adil, J. Ali, M. Attique, M.M. Jadoon, S. Abbas, S.R. Alotaibi, V.G. Menon, A. Farouk, Three byte-based mutual authentication scheme for autonomous Internet of vehicles, IEEE Trans. Intell. Transp. Syst. 23 (7) (2021) 9358-9369.

[36] Y. Djenouri, A. Belhadi, D. Djenouri, G. Srivastava, J.C.-W. Lin, A secure intelligent system for Internet of vehicles: case study on traffic forecasting, IEEE Trans. Intell. Transp. Syst. 24 (11) (2023) 13218-13227.

[37] L. Song, G. Sun, H. Yu, X. Du, M. Guizani, Fbia: a fog-based identity authentication scheme for privacy preservation in Internet of vehicles, IEEE Trans. Veh. Technol. 69 (5) (2020) 5403-5415.

[38] H. Sedjelmaci, N. Kaaniche, A. Boudguiga, N. Ansari, Secure attack detection frame-work for hierarchical 6g-enabled internet of vehicles, IEEE Trans. Veh. Technol.

[39] E. Schoitsch, C. Schmittner, Z. Ma, T. Gruber, The need for safety and cyber-security co-engineering and standardization for highly automated automotive vehicles, in: Advanced Microsystems for Automotive Applications 2015: Smart Systems for Green and Automated Driving, Springer, 2016, pp. 251-261.

[40] R. Taghizadeh-Mehrjardi, N. Hamzehpour, M. Hassanzadeh, B. Heung, M.G. Goy-daragh, K. Schmidt, T. Scholten, Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping, Geoderma 399 (2021) 115108.