论文标题
物联网中的基于机器学习的对抗机器学习
Adversarial Machine Learning based Partial-model Attack in IoT
论文作者
论文摘要
随着物联网（IoT）成为Internet的下一个逻辑阶段的出现，因此必须在支持各种应用程序时了解物联网系统的脆弱性。由于机器学习已应用于许多物联网系统中，因此在对抗机器学习方法后需要研究机器学习的安全含义。在本文中，我们仅通过控制一小部分传感设备来提出一个基于对抗机器学习的对抗机器学习的局部模型攻击。我们的数值结果表明，这种攻击的可行性是在物联网设备控制有限的情况下破坏数据融合的决策，例如，当对手设计机中只有8个物联网设备中的8个，攻击成功率达到83 \％。这些结果表明，即使对手操纵一小部分IoT设备，物联网系统的机器学习引擎也很容易受到攻击，并且这些攻击的结果严重破坏了物联网系统操作。
As Internet of Things (IoT) has emerged as the next logical stage of the Internet, it has become imperative to understand the vulnerabilities of the IoT systems when supporting diverse applications. Because machine learning has been applied in many IoT systems, the security implications of machine learning need to be studied following an adversarial machine learning approach. In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices. Our numerical results demonstrate the feasibility of this attack to disrupt the decision making in data fusion with limited control of IoT devices, e.g., the attack success rate reaches 83\% when the adversary tampers with only 8 out of 20 IoT devices. These results show that the machine learning engine of IoT system is highly vulnerable to attacks even when the adversary manipulates a small portion of IoT devices, and the outcome of these attacks severely disrupts IoT system operations.
