Third Eye: Context-Aware Detection for Hidden Terminal Emulation Attacks in Cognitive Radio-Enabled IoT Networks

Recently, the Internet of Things (IoT) technology has been drawing increasing attention because it has a great potential to positively impact human life in a broad range of applications. Nonetheless, the dense deployment of multiple co-located IoT networks that may follow different wireless protocols will essentially bring new network vulnerabilities. In this paper, we introduce a novel attack scenario in co-located cognitive radio (CR) enabled IoT networks, where a reactive attacker can emulate the radiation pattern of a hidden terminal (the attacker is from a different network) and can interfere with the transmissions from its hidden counterparts, namely the hidden terminal emulation (HTE) attack. As the dense deployment of IoT nodes-from different networks and technologies-will naturally create such hidden terminal scenarios among IoT devices of different networks, it provides the HTE attacker plausible deniability to reactively interfere with its hidden counterparts; hence, the state-of-the-art reactive attack detection techniques are infeasible in this scenario where benign hidden terminals could be flagged as reactive attackers. In this paper, we capture the behavior of a benign hidden terminal and an HTE attacker via parsimonious Markov models and propose a context-aware detection solution using the Markov chain hypothesis testing, namely the Third Eye. Though there has been extensive research on malicious interference detection, to the best of our knowledge, this work is the first that considers hidden terminals as benign interference sources, foresees this unique attack scenario, and leverages the existing carrier sensing technique as a natural and effective way to detect HTE attacks.