ITEMTK: IoT Traffic Exposure Monitoring Toolkit

The network traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and IoT manufacturers, and often also shared with their third parties to maintain and enhance user experiences. Unfortunately, on-path adversaries could fingerprint users’ sensitive and private information by analyzing these network traffic traces. While there’s a growing body of literature on defending against this side-channel attack—malicious IoT traffic analytics (TA), there’s currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework—IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. During the design of ITEMTK, we also identified a novel image-based attack capable of inferring sensitive user information, even when users implement the most robust preventative measures in their smart homes. We then present a new, initial, prototype-level defense mechanism—PrivacyProtector, which could effectively defend against a wide range of state-of-the-art machine learning-based TA attacks, including our newly proposed image-based TA attacks. ITEMTK’s flexibility allows users’ easy expansion by integrating new TA attack models and defenses to benchmark their future work.