Lightweight anomaly detection framework for IoT
Tagliaro Beasley, Bianca
O'Mahony, George D.
Gómez Quintana, Sergi
Institute of Electrical and Electronics Engineers (IEEE)
Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.
Anomaly detection , ARIMA , Embedded systems , IoT , Low power , Machine learning , SARIMA , Security
Tagliaro Beasley, B., O'Mahony, G. D., Gómez Quintana, S., Temko, A. and Popovici, E. (2020) 'Lightweight anomaly detection framework for IoT', 2020 31st Irish Signals and Systems Conference (ISSC), Letterkenny, Ireland, 11-12 June. doi: 10.1109/ISSC49989.2020.9180205
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