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In trigger-action IoT platforms, IoT devices report event conditions to IoThubs notifying their cyber states and let the hubs invoke actions in other IoTdevices based on functional dependencies defined as rules in a rule engine.These functional dependencies create a chain of interactions that help automatenetwork tasks. Adversaries exploit this chain to report fake event conditionsto IoT hubs and perform remote injection attacks upon a smart environment toindirectly control targeted IoT devices. Existing defense efforts usuallydepend on static analysis over IoT apps to develop rule-based anomaly detectionmechanisms. We also see ML-based defense mechanisms in the literature thatharness physical event fingerprints to determine anomalies in an IoT network.However, these methods often demonstrate long response time and lack ofadaptability when facing complicated attacks. In this paper, we propose tobuild a deep reinforcement learning based real-time defense system forinjection attacks. We define the reward functions for defenders and implement adeep Q-network based approach to identify the optimal defense policy. Ourexperiments show that the proposed mechanism can effectively and accuratelyidentify and defend against injection attacks with reasonable computationoverhead.

IoTWarden: A Deep Reinforcement Learning Based Real-time Defense System to Mitigate Trigger-action IoT Attacks