Securing IoT Using Supervised Machine Learning
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48774-3_1
Reference21 articles.
1. Saran, N., Kesswani, N.: A comparative study of supervised machine learning classifiers for intrusion detection in internet of things. Proc. Comput. Sci. 218, 2049–2057 (2023). https://doi.org/10.1016/j.procs.2023.01.181
2. Tekin, N., Acar, A., Aris, A., Uluagac, A.S., Gungor, V.C.: Energy consumption of on-device machine learning models for IoT intrusion detection. Internet Things 21, 100670 (2023). https://doi.org/10.1016/j.iot.2022.100670
3. Farooq, U., Tariq, N., Asim, M., Baker, T., Al-Shamma’a, A.: Machine learning and the Internet of Things security: solutions and open challenges. J. Parallel Distrib. Comput. 62, 89–104 (2022). https://doi.org/10.1016/j.jpdc.2022.01.015
4. Ahmad, R., Alsmadi, I.: Machine learning approaches to IoT security: a systematic literature review. Internet Things 14, 100365 (2021). https://doi.org/10.1016/j.iot.2021.100365
5. Mohanta, B.K., Jena, D., Satapathy, U., Patnaik, S.: Survey on IoT security: challenges and solution using machine learning, artificial intelligence and blockchain technology. Internet of Things. 11, 100227 (2020). https://doi.org/10.1016/j.iot.2020.100227
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