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Modern IoT (Internet of Things) environments with thousands of low-end anddiverse IoT nodes with complex interactions among them and often deployed inremote and/or wild locations present some unique challenges that maketraditional node compromise detection services less effective. This paperpresents the design, implementation and evaluation of a fog-based architecturethat utilizes the concept of a digital-twin to detect compromised IoT nodesexhibiting malicious behaviors by either producing erroneous data and/or beingused to launch network intrusion attacks to hijack other nodes eventuallycausing service disruption. By defining a digital twin of an IoT infrastructureat a fog server, the architecture is focused on monitoring relevant informationto save energy and storage space. The paper presents a prototype implementationfor the architecture utilizing malicious behavior datasets to performmisbehaving node classification. An extensive accuracy and system performanceevaluation was conducted based on this prototype. Results show good accuracyand negligible overhead especially when employing deep learning techniques suchas MLP (multilayer perceptron).

An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario