论文标题
制定基于深度学习预后的对抗示例（扩展版）
Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)
论文作者
论文摘要
在制造业中，意外的失败被认为是主要的操作风险，因为它们可以阻止生产力并造成巨大的损失。最先进的预后和健康管理（PHM）系统结合了深度学习（DL）算法和物联网（IoT）设备，以确定设备的健康状况，从而降低停机时间，维护成本并提高生产率。不幸的是，物联网传感器和DL算法都容易受到网络攻击的影响，因此对PHM系统构成了重大威胁。在本文中，我们采用了从计算机视觉域的对抗性示例制作技术，并将其应用于PHM域。具体而言，我们使用快速梯度符号方法（FGSM）和基本迭代方法（BIM）制作对抗性示例，并将它们应用于基于长期的短期记忆（LSTM），门控复发单元（GRU）和基于卷积神经网络（CNN）的PHM模型。我们使用NASA的Turbofan发动机数据集评估了对抗攻击的影响。获得的结果表明，所有评估的PHM模型都容易受到对抗攻击的影响，并且可能在其余有用的寿命估计中造成严重的缺陷。获得的结果还表明，精心设计的对抗性实例是高度转移的，可能会对PHM系统造成重大损害。
In manufacturing, unexpected failures are considered a primary operational risk, as they can hinder productivity and can incur huge losses. State-of-the-art Prognostics and Health Management (PHM) systems incorporate Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain the health status of equipment, and thus reduce the downtime, maintenance cost and increase the productivity. Unfortunately, IoT sensors and DL algorithms, both are vulnerable to cyber attacks, and hence pose a significant threat to PHM systems. In this paper, we adopt the adversarial example crafting techniques from the computer vision domain and apply them to the PHM domain. Specifically, we craft adversarial examples using the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and apply them on the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) based PHM models. We evaluate the impact of adversarial attacks using NASA's turbofan engine dataset. The obtained results show that all the evaluated PHM models are vulnerable to adversarial attacks and can cause a serious defect in the remaining useful life estimation. The obtained results also show that the crafted adversarial examples are highly transferable and may cause significant damages to PHM systems.
