Study of Machine Learning for Cloud Assisted IoT Security as a Service
Data Science and Cybersecurity Center, Howard University, Washington, DC 20059, USA
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Authors to whom correspondence should be addressed.
Academic Editor: Fatos Xhafa
Sensors 2021, 21(4), 1034; https://doi.org/10.3390/s21041034
Received: 5 December 2020
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Revised: 25 January 2021
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Accepted: 28 January 2021
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Published: 3 February 2021
(This article belongs to the Section Internet of Things)
Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.
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MDPI and ACS Style
Alsharif, M.; Rawat, D.B. Study of Machine Learning for Cloud Assisted IoT Security as a Service. Sensors 2021, 21, 1034. https://doi.org/10.3390/s21041034
AMA Style
Alsharif M, Rawat DB. Study of Machine Learning for Cloud Assisted IoT Security as a Service. Sensors. 2021; 21(4):1034. https://doi.org/10.3390/s21041034
Chicago/Turabian StyleAlsharif, Maram, and Danda B. Rawat. 2021. "Study of Machine Learning for Cloud Assisted IoT Security as a Service" Sensors 21, no. 4: 1034. https://doi.org/10.3390/s21041034
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