Abstract
The Internet of things has emerged as a technology that is affecting a lot of domains such as manufacturing and automation, smart traffic systems, security, disaster management, etc. Security and user authentication are challenging due to the large number of connected devices and the magnitude of data shared among the devices. Typically, some digital fingerprint in terms of the features of the data stream to be transmitted is embedded in the data streams, but they can be extracted in case the adversary analyses the data stream and records it for a long period with a sufficient number of samples. Moreover, large length stochastic features would inevitably increase the system computation overhead and latency at the gateway. While lesser overhead can be settled that would result in higher bit errors and chances of attacks. In this paper, a deep learning-based approach is used to detect possible attacks based on the statistical features embedded into the bitstream transmitted. Additionally, the channel state information has been utilized for enhancing the Quality of Service of the system. The performance metrics are the bit error rate, number of epochs for training, and mean square error of the deep learning model.
Similar content being viewed by others
Date Availability
All data generated or analyzed during this study are included in this published article.
References
K. Zhao and L. Ge, A Survey on the Internet of Things Security, 2013 Ninth International Conference on Computational Intelligence and Security, IEEE 2013, pp. 663–667.
Q. Abbas, S.A. Hassan, H.K. Qureshi, K. Dev, and H. Jung, “A comprehensive survey on age of information in massive IoT networks,” in Computer Communications , 2022.
Hossain, M. A., Hossain, A. R., & Ansari, N. (2022). Numerology-capable UAV-MEC for future generation massive IoT networks. IEEE Internet of Things Journal, 9(23), 23860–23868.
Q. Gou, L. Yan, Y. Liu and Y. Li, “Construction and Strategies in IoT Security System,” 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, IEEE 2013, pp. 1129–1132
Lee, B. M., & Yang, H. (2022). Energy efficient scheduling and power control of massive MIMO in massive IoT networks. Expert Systems with Applications, 200, 116920.
Namvar, N., Saad, W., Bahadori, N., & Kelley, B. (2016, December). Jamming in the internet of things: A game-theoretic perspective. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
Song, K., Wang, Q., Peng, L., Li, C., & Wu, X. (2021). Secrecy energy efficiency optimization for DF relaying IoT systems with passive eavesdropping terminal. Journal of Physical Communications, 44, 1–28.
Saravanan, V., Sreelatha, P., Atyam, N. R., Madiajagan, M., Saravanan, D., & Sultana, H. P. (2023). Design of deep learning model for radio resource allocation in 5G for massive iot device. Sustainable Energy Technologies and Assessments, 56, 103054.
Song, K., Yang, J. C., & Fang, B. X. (2011). Security model and key technologies for the Internet of things. The Journal of China Universities of Posts and Telecommunications, 18(2), 109–112.
Pecorella, T., Brilli, L., & Mucchi, L. (2016). The role of physical layer security in IoT: A novel perspective. Journal of Inforamtion, MDPI, 7(3), 1–17.
Kalkan, K., & Zeadally, S. (2018). Securing internet of things with software defined networking. IEEE Communications Magazine, 56(9), 186–192.
Sarrab, M., & Alnaeli, S. M. (2018, November). Critical aspects pertaining security of iot application level software systems. In 2018 IEEE 9th annual information technology, electronics and mobile communication conference (IEMCON) (pp. 960-964). IEEE.
K. S. Germain and F. Kragh, "Mobile Physical-Layer Authentication Using Channel State Information and Conditional Recurrent Neural Networks," 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1–6.
Mohamad, F., Haroun, T., Haroun, M. F., & Gulliver, T. A. (2021). Secure OFDM with peak-to-average power ratio reduction using the spectral phase of chaotic signals. Entropy, 23(11), 1380.
Chen, Y., Zhang, T., Liu, Y., & Qiao, X. (2020). Physical layer security in noma-enabled cognitive radio networks with outdated channel state information. IEEE Access, 8, 159480–159492.
J. Shen Bo Liu; Yaya Mao; Rahat Ullah; Jianxin Ren; Jianye Zhao; Shuaidong Chen., "Enhancing the Reliability and Security of OFDM-PON Using Modified Lorenz Chaos Based on the Linear Properties of FFT," in Journal of Lightwave Technology, vol. 39, no. 13, pp. 4294–4299, July1, 2021.
Istiaque Ahmed, K., Tahir, M., Hadi Habaebi, M., Lun Lau, S., & Ahad, A. (2021). Machine learning for authentication and authorization in IoT: Taxonomy, challenges and future research direction. Sensors, 21(15), 5122sssssss.
Sadique, J. J., Ullah, S. E., Islam, M. R., Raad, R., Kouzani, A. Z., & Mahmud, M. A. P. (2021). Transceiver design for full-duplex uav based zero-padded ofdm system with physical layer security. IEEE Access, 9, 59432–59445.
T Burton, K Rasmussen, “Private Data Exfiltration from Cyber-Physical Systems Using Channel State Information”, in Proceedings of Private Data Exfiltration from Cyber-Physical Systems Using Channel State Information, ACM 2021, pp.223–235.
J Zhao, B Liu, Y Mao, R Ullah, J Ren, S Chen, High security OFDM-PON with a physical layer encryption based on 4D-hyperchaos and dimension coordination optimization”, OSA publications, vol.28, issue.14, pp.21236–21246.
Wang, H.-M., Bai, J., & Dong, L. (2020). Intelligent reflecting surfaces assisted secure transmission without eavesdropper’s CSI. IEEE Signal Processing Letters, 27, 1300–1304.
Bordel, B., Alcarria, R., Robles, T., & Iglesias, M. S. (2021). Data authentication and anonymization in iot scenarios and future 5G networks using chaotic digital watermarking. IEEE Access, 9, 22378–22398.
Ferdowsi, A., & Saad, W. (2018). Deep learning-based dynamic watermarking for secure signal authentication in the internet of things. IEEE International Conference on Communications (ICC), 2018, 1–6.
M. El-hajj, M. Chamoun, A. Fadlallah and A. Serhrouchni, "Analysis of authentication techniques in Internet of Things (IoT)," 2017 1st Cyber Security in Networking Conference (CSNet), 2017, pp. 1–3.
Moosavi, S. R., Gia, T. N., Rahmani, A. M., & Nigussie, E. (2015). SEA: A secure and efficient authentication and authorization architecture for IoT-based healthcare using smart gateways. Procedia Computer Science, 52, 452–459.
MNO Sadiku, Elements of Electromagnetics, 4th Edition, Oxford University Press.
S. Sathyadevan, Vejesh V, R. Doss and L. Pan, "Portguard - an authentication tool for securing ports in an IoT gateway," 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017, pp. 624–629.
PSF Sheron, KP Sridhar, S Baskar, “A decentralized scalable security framework for end to end authentication of future IoT communication”, Special Issue on Cross layer innovations in Internet of Things and Advanced Microprocessor Optimization methods for the Internet of Things, Wiley Online Library, vol. 31, no. 12, pp.1–12.
Eriksson, J., Ollila, E., & Koivunen, V. (2010). Essential statistics and tools for complex random variables. IEEE Transactions on Signal Processing, 58(10), 5400–5408.
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
P. G. Madhavan, Recurrent neural network for time series prediction, Proceedings of the 15th annual international conference of the IEEE engineering in medicine and biology societ, (1993), pp. 250–251.
Conitzer, V., & Sandholm, T. (2008). New complexity results about nash equilibria. Games of Economic Behaviour, 63(2), 621–641.
Hu, J., Li, W., & Zhou, W. (2019). Central limit theorem for mutual information of large mimo systems with elliptically correlated channels. IEEE Transactions on Information Theory, 65(11), 7168–7180.
Reynaldi, A., Lukas, S., & Margaretha, H. (2012). Backpropagation and levenberg-marquardt algorithm for training finite element neural network. Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation, 2012, 89–94.
H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu and N. D. Sidiropoulos, Learning to optimize: Training deep neural networks for wireless resource management, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2017, pp. 1–6.
Zhou, F., Zhou, H., Yang, Z., & Gu, L. (2021). IF2CNN: Towards non-stationary time series feature extraction by integrating iterative filtering and convolutional neural networks. Expert Systems with Applications, 170, 114527.
B. Neekzad, K. Sayrafian-Pour, J. Perez and J. S. Baras, Comparison of ray tracing simulations and millimeter wave channel sounding measurements, 2007 IEEE 18th international symposium on personal, indoor and mobile radio communications, (2007), pp. 1-5.
Talak, R., Karaman, S., & Modiano, E. (2020). Improving age of information in wireless networks with perfect channel state information. IEEE/ACM Transactions on Networking, 28(4), 1765–1778.
Katorin, Y. F., Makshanov, A. V., Danilin, G. V., Yemelyanov, V. A., & Ovcharenko, I. K. (2020). Improving the QoS multiservice networks: New methods, impact on the security of transmitted data. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2020, 341–344.
Earle, B., Al-Habashna, A., Wainer, G., Li, X., & Xue, G. (2021). Prediction of 5G new radio wireless channel path gains and delays using machine learning and CSI feedback. Annual Modeling and Simulation Conference (ANNSIM), 2021, 1–11.
Acknowledgements
The authors would like to extend their gratitude towardsthe faculty members of Department of Electronics Engineering, Rajkiya Engineering College, Kannauj, India. The suggestions and constructive criticism has helped in polishing the paper and making it more comprehensible.
Funding
The author(s) received no financial support for this article's research, authorship, and/or publication.
Author information
Authors and Affiliations
Contributions
All authors contributed equally to prepare this work.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest.
Compliance with Ethical Standards
All procedures performed in studies involving human participants were in accordance with the ethical standards.
Consent to Participate
Not applicable.
Consent for Publication
The Author transfers his copyrights to the publisher.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kumar, R., Joshi, G., Chauhan, A.K.S. et al. A Deep Learning and Channel Sounding Based Data Authentication and QoS Enhancement Mechanism for Massive IoT Networks. Wireless Pers Commun 130, 2495–2514 (2023). https://doi.org/10.1007/s11277-023-10389-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10389-1