IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
This addresses security vulnerabilities in IoT device identification systems, posing a threat to network monitoring and security, and is incremental as it adapts GAN-based adversarial attacks to a specific domain.
The authors tackled the problem of evading machine learning-based IoT device identification by proposing IoTGAN, a novel attack strategy that manipulates IoT traffic with adversarial perturbations, achieving successful evasion in experiments.
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.