Hiding in Plain Sight: An IoT Traffic Camouflage Framework for Enhanced Privacy

Abstract

The rapid growth of Internet of Things (IoT) devices has introduced significant challenges to privacy, particularly as network traffic analysis techniques evolve. While encryption protects data content, traffic attributes such as packet size and timing can reveal sensitive information about users and devices. Existing single-technique obfuscation methods, such as packet padding, often fall short in dynamic environments like smart homes due to their predictability, making them vulnerable to machine learning-based attacks. This paper introduces a multi-technique obfuscation framework designed to enhance privacy by disrupting traffic analysis. The framework leverages six techniques-Padding, Padding with XORing, Padding with Shifting, Constant Size Padding, Fragmentation, and Delay Randomization-to obscure traffic patterns effectively. Evaluations on three public datasets demonstrate significant reductions in classifier performance metrics, including accuracy, precision, recall, and F1 score. We assess the framework's robustness against adversarial tactics by retraining and fine-tuning neural network classifiers on obfuscated traffic. The results reveal a notable degradation in classifier performance, underscoring the framework's resilience against adaptive attacks. Furthermore, we evaluate communication and system performance, showing that higher obfuscation levels enhance privacy but may increase latency and communication overhead.