IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification

TL;DR

This paper introduces IoTGAN, a novel adversarial attack method that manipulates IoT device traffic to evade machine learning-based identification, highlighting security vulnerabilities and proposing countermeasures.

Contribution

The paper presents IoTGAN, a new black-box attack technique using neural networks to generate adversarial traffic perturbations without affecting device functionality.

Findings

IoTGAN successfully evades ML-based IoT device identification.

Countermeasures can mitigate IoTGAN's effectiveness.

Demonstrates security risks in current IoT identification methods.

Abstract

With the proliferation of IoT devices, researchers have developed a variety of IoT device identification methods with the assistance of machine learning. Nevertheless, the security of these identification methods mostly depends on collected training data. In this research, we propose a novel attack strategy named IoTGAN to manipulate an IoT device's traffic such that it can evade machine learning based IoT device identification. In the development of IoTGAN, we have two major technical challenges: (i) How to obtain the discriminative model in a black-box setting, and (ii) How to add perturbations to IoT traffic through the manipulative model, so as to evade the identification while not influencing the functionality of IoT devices. To address these challenges, a neural network based substitute model is used to fit the target model in black-box settings, it works as a discriminative model in IoTGAN. A manipulative model is trained to add adversarial perturbations into the IoT device's traffic to evade the substitute model. Experimental results show that IoTGAN can successfully achieve the attack goals. We also develop efficient countermeasures to protect machine learning based IoT device identification from been undermined by IoTGAN.