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The number of Internet of Things (IoT) devices is expected to reach 50 billion devices by 2020 and the devices are increasingly diverse. They are disrupting traditional security measures. Mobile Network Operators (MNOs) have limited control over customers’ IoT devices, as they are deployed on the customer premises. MNOs need to deploy effective security controls at their end to protect their assets. Huge amounts of data are generated by IoT devices, which can be exploited to understand device behaviours. The proposed research program aims at finding novel solutions to the problem of detecting abnormal behaviour in IoT environments. When abnormal network traffic is detected, two solutions can be adopted: blocking the traffic, or sending it for deeper analysis. The first solution may disconnect legitimate IoT devices, as certain behavior deviations are quite normal, e.g., bandwidth fluctuation. The second solution attempts to learn more about IoT devices and refine the learned behaviour model. This is a real-time and continuous learning process that adapts the model to a changing environment, e.g., new device types. Therefore, sophisticated IoT fingerprinting exploiting machine learning algorithms is the ultimate objective to achieve.
Oluwatosin Falola;Shaveta Dandyan;Nirja Joshi
University of Regina
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