Abstract
Edge-of-Things (EoT) is a new evolving computing model driven by the Internet of Things (IoT). It enables data processing, storage, and service to be shifted from the Cloud to nearby Edge devices/systems such as smartphones, routers, and base stations on the IoT paradigm. However, this architectural shift causes the security and privacy issues to migrate to the different layers of the Edge architecture. Therefore, detecting intrusion in such a distributed environment is difficult. In this scenario, an Intrusion Detection Systems is necessary. Here, we propose an approach to quickly and accurately detect intrusive activities in the EoT network, to realize the full potential of the IoT. Specifically, we propose a deep belief network (DBN) based on an advanced intrusion detection approach. We studied different detection models, by using different structures of DBNs, and compared them with existing detection techniques. Test results show that the proposed methodology performs essentially superior to the current state-of-the-art approaches.
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