IoTGAN：由 GAN 驱动的针对基于机器学习的 IoT 设备识别的伪装攻击
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
AI总结：
本文提出 IoTGAN，通过黑盒替代模型与对抗流量操纵模型伪装 IoT 设备流量以规避机器学习识别，并给出有效防御措施。
摘要：
随着 IoT 设备的普及，研究人员已经在机器学习的辅助下开发了多种 IoT 设备识别方法。然而，这些识别方法的安全性大多依赖于所收集的训练数据。在本研究中，我们提出一种名为 IoTGAN 的新型攻击策略，用于操纵 IoT 设备的流量，使其能够规避基于机器学习的 IoT 设备识别。在开发 IoTGAN 的过程中，我们面临两个主要技术挑战：（i）如何在黑盒环境中获得判别模型；以及（ii）如何通过操纵模型向 IoT 流量添加扰动，从而在规避识别的同时不影响 IoT 设备的功能。为应对这些挑战，我们使用基于神经网络的替代模型在黑盒环境中拟合目标模型，该模型在 IoTGAN 中充当判别模型。同时训练一个操纵模型，将对抗性扰动添加到 IoT 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。我们还开发了有效的防御措施，以保护基于机器学习的 IoT 设备识别免受 IoTGAN 的破坏。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
