A hybrid cryptographic and deep learning approach for IoT device security
*Ahmed Fakhir MutarCorresponding authordiazrodr.od@gmail.comDepartment of Computer Engineering Faculty of Electrical and Computer Engineering University of TabrizTabriz, IranView full profile → , Leyli Mohammad Khanlinguye365@msu.eduDepartment of Computer Engineering Faculty of Electrical and Computer Engineering University of TabrizTabriz, IranView full profile → , Hojjat Emamiemami@ubonab.ac.irDepartment of Computer Engineering Faculty of Engineering University of BonabBonab, IranView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 04 Feb 2025
- Published Online:
- 26 Jun 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2289
- Pages:
- 1437–1449
Abstract
Keywords
Subject Classifications
References
[1] P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” Computers and Electrical Engineering, vol. 105, p. 108543 (2023).
[2] S. Izza, M. Benssalah, and K. Drouiche, “An enhanced scalable and secure RFID authentication protocol for WBAN within an IoT environment,” Journal of Information Security and Applications, vol. 58, p. 102705 (May 2021), doi: 10.1016/j.jisa.2020.102705.
[3] F. Hategekimana, T. J. Whitaker, M. J. Hossain Pantho, and C. Bobda, “IoT Device security through dynamic hardware isolation with cloud-Based update,” Journal of Systems Architecture, vol. 109, p. 101827 (Oct. 2020) doi: 10.1016/j.sysarc.2020.101827.
[4] O. Abu Waraga, M. Bettayeb, Q. Nasir, and M. Abu Talib, “Design and implementation of automated IoT security testbed,” Computers & Security, vol. 88, p. 101648 (Jan. 2020), doi: 10.1016/j.cose.2019.101648.
[5] P. Rani, M. Kumar, A. K. Das, N. Kumar, and M. Alazab, “Federated Learning-Based Misbehaviour Detection for the 5G-Enabled Internet of Vehicles,” IEEE Transactions on Consumer Electronics (2023).
[6] M. S. Mehmood, M. R. Shahid, A. Jamil, R. Ashraf, T. Mahmood, and A. Mehmood, “A Comprehensive Literature Review of Data Encryption Techniques in Cloud Computing and IoT Environment,” in 2019 8th International Conference on Information and Communication Technologies (ICICT), Karachi, Pakistan: IEEE, pp. 54–59 (Nov. 2019). doi: 10.1109/ICICT47744.2019.9001945.
[7] C. Beaman, A. Barkworth, T. D. Akande, S. Hakak, and M. K. Khan, “Ransomware: Recent advances, analysis, challenges and future research directions,” Computers & security, vol. 111, p. 102490 (2021).
[8] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 7, pp. 3523–3542 (2021).
[9] M. Kumar, A. Singh, A. K. Das, N. Kumar, M. Alazab, and A. Jolfaei, “Healthcare Internet of Things (H-IoT): Current Trends, Future Prospects, Applications, Challenges, and Security Issues,” Electronics, vol. 12, no. 9, p. 2050 (Apr. 2023), doi: 10.3390/electronics12092050.
[10] U. Tariq, I. Ahmed, A. K. Bashir, and K. Shaukat, “A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review,” Sensors, vol. 23, no. 8, p. 4117 (Apr. 2023), doi: 10.3390/s23084117.
[11] K. Tange, M. De Donno, X. Fafoutis, and N. Dragoni, “A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities,” IEEE Commun. Surv. Tutorials, vol. 22, no. 4, pp. 2489–2520 (2020), doi: 10.1109/COMST.2020.3011208.
[12] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine Learning in IoT Security: Current Solutions and Future Challenges,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1686–1721 (2020), doi: 10.1109/COMST.2020.2986444.
[13] A. O. Akmandor, H. Yin, and N. K. Jha, “Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors,” IEEE Trans. Multi-Scale Comp. Syst., vol. 4, no. 4, pp. 914–930 (Oct. 2018), doi: 10.1109/TMSCS.2018.2864297.
[14] K. Mandal, M. Rajkumar, P. Ezhumalai, D. Jayakumar, and R. Yuvarani, “WITHDRAWN: Improved security using machine learning for IoT intrusion detection system,” Materials Today: Proceedings, p. S2214785320377889 (Dec. 2020), doi: 10.1016/j.matpr.2020.10.187.
[15] T. Gu, A. Abhishek, H. Fu, H. Zhang, D. Basu, and P. Mohapatra, “Towards Learning-automation IoT Attack Detection through Reinforcement Learning,” in 2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Cork, Ireland: IEEE, pp. 88–97 (Aug. 2020). doi: 10.1109/WoWMoM49955.2020.00029.
[16] M. M. N. Aboelwafa, K. G. Seddik, M. H. Eldefrawy, Y. Gadallah, and M. Gidlund, “A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT,” IEEE Internet Things J., vol. 7, no. 9, pp. 8462–8471 (Sep. 2020), doi: 10.1109/JIOT.2020.2991693.
[17] S. Jha, D. Prashar, H. V. Long, and D. Taniar, “Recurrent neural network for detecting malware,” Computers & Security, vol. 99, p. 102037 (2020).
[18] A. Pinhero, M. A. Zaveri, S. Sharma, “Malware detection employed by visualization and deep neural network,” Computers & Security, vol. 105, p. 102247 (2021).
