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

TL;DR

IoTMonitor employs Hidden Markov Models to analyze sensor data and accurately identify critical attack nodes in trigger-action IoT platforms, enhancing security by understanding event chains and vulnerabilities.

Contribution

The paper introduces IoTMonitor, a novel HMM-based system that reconstructs event sequences and detects crucial attack nodes in IoT trigger-action scenarios, addressing security gaps in existing methods.

Findings

High accuracy in event sequence reconstruction

Effective identification of crucial attack nodes

Demonstrated success on PEEVES datasets

Abstract

With the emergence and fast development of trigger-action platforms in IoT settings, security vulnerabilities caused by the interactions among IoT devices become more prevalent. The event occurrence at one device triggers an action in another device, which may eventually contribute to the creation of a chain of events in a network. Adversaries exploit the chain effect to compromise IoT devices and trigger actions of interest remotely just by injecting malicious events into the chain. To address security vulnerabilities caused by trigger-action scenarios, existing research efforts focus on the validation of the security properties of devices or verification of the occurrence of certain events based on their physical fingerprints on a device. We propose IoTMonitor, a security analysis system that discerns the underlying chain of event occurrences with the highest probability by observing a chain of physical evidence collected by sensors. We use the Baum-Welch algorithm to estimate transition and emission probabilities and the Viterbi algorithm to discern the event sequence. We can then identify the crucial nodes in the trigger-action sequence whose compromise allows attackers to reach their final goals. The experiment results of our designed system upon the PEEVES datasets show that we can rebuild the event occurrence sequence with high accuracy from the observations and identify the crucial nodes on the attack paths.