iPET: Privacy Enhancing Traffic Perturbations for IoT Communications

IoT devices are vulnerable to different kinds of threats and attacks. The devices constantly communicate with servers over the Internet, allowing an attacker to extract sensitive information by passively monitoring the network traffic. Recent research works have shown that a network attacker with a trained machine learning (ML) model can accurately fingerprint IoT devices based on the (encrypted) traffic flows of the devices. Such fingerprinting attacks are capable of revealing the make and model of the devices, which can further be used to extract detailed user activities.

In this work, we develop and propose iPET, a privacy enhancing traffic perturbation technique that counters ML-based fingerprinting attacks. iPET uses adversarial deep learning, specifically, Generative Adversarial Networks (GANs), to generate these perturbations. Unlike conventional GANs, a key idea of iPET is to deliberately introduce stochasticity in the model. This approach inhibits an attacker from recreating an identical perturbation model and using it for fingerprinting. We evaluate the effectiveness of our defense against state-of-the-art fingerprinting models three different attacker capabilities. Our evaluations on synthetic and real-world datasets demonstrate that iPET decreases the accuracy of even the most powerful attacker significantly.