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

TL;DR

This paper presents a multi-technique traffic obfuscation framework for IoT devices that significantly impairs traffic analysis, enhancing user privacy while balancing system performance impacts.

Contribution

It introduces a novel multi-technique framework combining six obfuscation methods to improve privacy against machine learning attacks in IoT traffic analysis.

Findings

Obfuscation reduces classifier accuracy, precision, recall, and F1 scores.

Framework remains effective against retrained neural network classifiers.

Higher obfuscation levels increase latency and communication overhead.

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.