IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
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DOI：
10.1109/dyspan53946.2021.9677264
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Tao Hou;Tao Wang;Zhuo Lu;Yao-Hong Liu;Y. Sagduyu
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作者：
Tao Hou;Tao Wang;Zhuo Lu;Yao-Hong Liu;Y. Sagduyu
随着物联网设备的激增，研究人员在机器学习的帮助下开发了各种物联网设备识别方法。然而，这些识别方法的安全性主要取决于收集的训练数据。在这项研究中，我们提出了一种名为IoTGAN的新型攻击策略，以操纵物联网设备的流量，使其可以逃避基于机器学习的物联网设备识别。在IoT GAN的开发中，我们面临两个主要的技术挑战：（i）如何在黑盒设置中获得判别模型，以及（ii）如何通过操纵模型向IoT流量添加扰动，以便在不影响IoT设备功能的情况下逃避识别。为了解决这些挑战，使用基于神经网络的替代模型来拟合黑盒设置中的目标模型，它在IoTGAN中用作判别模型。训练操纵模型以将对抗性扰动添加到物联网设备的流量中以规避替代模型。实验结果表明，IoTGAN能够成功实现攻击目标。我们还制定了有效的对策，以保护基于机器学习的物联网设备识别免受IoTGAN的破坏。
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.