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With the emergence and fast development of trigger-action platforms in IoTsettings, security vulnerabilities caused by the interactions among IoT devicesbecome more prevalent. The event occurrence at one device triggers an action inanother device, which may eventually contribute to the creation of a chain ofevents in a network. Adversaries exploit the chain effect to compromise IoTdevices and trigger actions of interest remotely just by injecting maliciousevents into the chain. To address security vulnerabilities caused bytrigger-action scenarios, existing research efforts focus on the validation ofthe security properties of devices or verification of the occurrence of certainevents based on their physical fingerprints on a device. We propose IoTMonitor,a security analysis system that discerns the underlying chain of eventoccurrences with the highest probability by observing a chain of physicalevidence collected by sensors. We use the Baum-Welch algorithm to estimatetransition and emission probabilities and the Viterbi algorithm to discern theevent sequence. We can then identify the crucial nodes in the trigger-actionsequence whose compromise allows attackers to reach their final goals. Theexperiment results of our designed system upon the PEEVES datasets show that wecan rebuild the event occurrence sequence with high accuracy from theobservations and identify the crucial nodes on the attack paths.

IoTMonitor: A Hidden Markov Model-based Security System to Identify Crucial Attack Nodes in Trigger-action IoT Platforms