IoTGAN: An attack strategy to manipulate an IoT device's traffic to evade machine learning based IoT device identification

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Machine Learning

Cryptography and Security

Networking and Internet Architecture

IoTGAN: An attack strategy to manipulate an IoT device's traffic to evade machine learning based IoT device identification

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

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 device identification.

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.

Authors

Tao Hou, Tao Wang, Zhuo Lu, Yao Liu, Yalin Sagduyu

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