References
[1] R. Roman, J. Zhou, and J. Lopez, “Challenges and solutions for securing the internet of things: A survey,” IEEE Internet Things J., vol. 7, no. 4, pp. 3367–3382, Oct. 2020, doi: 10.1109/JIOT.2020.2987889. [2] S. Asharf, A. Shakil, M. Hassan, and M. Habib, “Security and privacy in IoT: Architecture, challenges, and countermeasures,” J. Comput. Netw. Commun., vol. 2020, Art. no. 8838654, 2020, doi: 10.1155/2020/8838654. [3] P. Chopra, S. Gupta, and R. Singh, “Security challenges in IoT: A comprehensive survey,” J. Cyber Secur. Mobil., vol. 9, no. 2, pp. 203–220, 2020, doi: 10.13052/jcsm2245- 1439.922. [4] M. Alghamdi, “Lightweight cryptographic algorithms for IoT security: A survey,” J. Netw. Comput., vol. 5, no. 6, p. 5, 2022. [5] P. Gupta and S. Mukherjee, “Anomaly-based intrusion detection in IoT using machine learning,” Comput. Secur., vol. 107, p. 102303, 2021, doi: 10.1016/j.cose.2021.102303. [6] M. A. Al-Garadi, A. Mohamed, K.-K. R. Choo, and F. S. Al-Anzi, “A survey of machine and deep learning methods for internet of things security,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1646–1685, 2020. [7] A. Williams, J. Brown, and K. Wilson, “IoT security: A comparative study of security challenges and countermeasures,” Int. J. Secur. Netw., vol. 15, no. 2, pp. 129–141, 2020, doi: 10.1504/IJSN.2020.108839. [8] S. Hassan, F. Umer, and M. A. Pasha, “Secure IoT architecture: A comprehensive review,” IEEE Internet Things J., vol. 8, no. 3, pp. 1970–1983, 2021, doi: 10.1109/JIOT.2020.3019273. [9] P. Hilton, “The 2016 Dyn cyberattack: How IoT devices were used in a massive DDoS attack,” J. Cybersecur., vol. 4, no. 1, pp. 21–28, 2020, doi: 10.1093/cybsec/tyz018. [10] L. Pycroft and T. Z. Aziz, “Cybersecurity in healthcare: IoT devices and patient safety,” J. Med. Syst., vol. 44, no. 3, pp. 52–62, 2020, doi: 10.1007/s10916-020-15395. [11] M. Singh and S. Sharma, “Intrusion detection in IoT networks: A review,” J. Inf. Secur. Appl., vol. 65, p. 102978, 2022, doi: 10.1016/j.jisa.2022.102978. [12] A. Almomani, M. Desouki, and M. Ayyash, “Lightweight IDS for IoT networks using random forests,” Comput. Secur., vol. 90, p. 101683, 2020, doi: 10.1016/j.cose.2019.101683. [13] T. Smith, R. Patel, and Y. Li, “Convolutional neural networks for smart home anomaly detection,” Neurocomputing, vol. 441, pp. 205–217, 2021, doi: 10.1016/j.neucom.2020.08.045. [14] J. Kim, S. Lee, and Y. Park, “Hybrid intrusion detection system for industrial IoT,” IEEE Internet Things J., vol. 9, no. 1, pp. 125–136, 2022, doi: 10.1109/JIOT.2021.3095586. [15] S. Sharma, R. Gupta, and V. Kumar, “Federated learning for intrusion detection in IoT: Challenges and opportunities,” J. Inf. Secur. Appl., vol. 60, p. 102894, 2021, doi: 10.1016/j.jisa.2021.102894. [16] H. Zhang, Y. Wang, and Z. Liu, “Blockchain-based decentralized IDS for IoT networks,” J. Commun. Netw., vol. 23, no. 4, pp. 215–228, 2021, doi: 10.1109/JCN.2021.00025. [17] G. Singh and A. Sharma, “IoT-specific attack detection using hybrid models: A deep learning approach,” Int. J. Netw. Secur., vol. 22, no. 5, pp. 511–521, 2020. [18] T. Chen, X. Liu, and W. Zhang, “Distributed IDS for smart grids using collaborative anomaly detection,” J. Electr. Comput. Eng., vol. 2021, Art. no. 4562123, 2021, doi: 10.1155/2021/4562123. [19] M. Ahmed, S. Khan, and M. Ali, “Deep learning-based intrusion detection system for healthcare IoT,” J. Netw. Comput. Appl., vol. 209, p. 102589, 2023. [20] Y. Zhang, J. Lin, and L. Zhao, “Hybrid federated learning for IoT security,” IEEE Trans. Ind. Informat., vol. 18, no. 6, pp. 3765–3774, 2022, doi: 10.1109/TII.2021.3085415.
