HomeSentinel: Intelligent Anti-Fingerprinting for IoT Traffic in Smart Homes

Recent studies have demonstrated that malicious adversaries are capable of fingerprinting Internet of Things (IoT) devices in a smart home and further causing privacy breaches. However, many existing anti-fingerprinting schemes, either by traffic padding or traffic mutation, are less effective in defending against state-of-the-art fingerprinting methods. To meet this gap, we in this paper propose the HomeSentinel, an intelligent anti-fingerprinting scheme to counter IoT traffic fingerprinting in smart homes. Specifically, we first design a LightGBM-based IoT traffic extraction model to accurately distinguish IoT traffic from raw network traffic in a smart home without user operations. Second, we develop a dummy IoT traffic generation model to produce dummy IoT traffic in desired spatial-temporal patterns. Third, an IoT traffic mixing strategy is crafted to heuristically merge dummy IoT traffic with real IoT traffic in desired spatial-temporal patterns. Extensive experiments on three real-world datasets (i.e., two public and one custom) demonstrate that our proposed HomeSentinel scheme can effectively defend against state-of-the-art IoT traffic fingerprinting methods, and outperforms existing IoT traffic anti-fingerprinting schemes. Further, real-world experiments are conducted on a self-built testbed show that, reasonably low communication delays can be caused when implementing the HomeSentinel in smart homes.