You Can Glimpse but You Cannot Identify: Protect IoT Devices From Being Fingerprinted

With pervasive IoT networking, traffic-analysis-based IoT fingerprinting techniques have been well researched. For example, by integrating blockchain technology and device fingerprinting, authentication of devices connected to a network can be achieved. Though the primary motivations are identifying vulnerabilities and implementing access control, the techniques could be exploited to trace IoT users’ privacy. We propose a traffic morphing scheme to protect IoT devices from being identified by fingerprinting models. The scheme mainly consists of a morphing policy learning algorithm, a rewarding model, and a time-series-based feature estimation algorithm. Backed by the timely rewarding model, a learning agent produces an optimal policy that perturbs the target fingerprinting model while preserving the original traffic function. The estimation algorithm predicts the feature vectors of unfinished flows to enable live traffic morphing. The scheme's advantage is that it requires minimal knowledge of the fingerprinting model and supports live morphing. Experimental results show that over 81% of the IoT devices become unidentifiable, and the scheme degrades the average F1 score of mainstream fingerprinting models from 0.996 to 0.526. For certain devices and target models, the scheme even reaches 100% effectiveness.