Abstract
The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall productivity through extensive utilization of Internet-of-Things (IoT) and Deep Learning (DL). Unfortunately, IoT sensors and DL algorithms are both prone to cyber-attacks. For instance, DL algorithms are known for their susceptibility to adversarial examples. Such adversarial attacks are vastly under-explored in the PdM domain. This is because the adversarial attacks in the computer vision domain for classification tasks cannot be directly applied to the PdM domain for multivariate time series (MTS) regression tasks. In this work, we propose an end-to-end methodology to design adversarially robust PdM systems by extensively analyzing the effect of different types of adversarial attacks and proposing a novel adversarial defense technique for DL-enabled PdM models. First, we propose novel MTS Projected Gradient Descent (PGD) and MTS PGD with random restarts (PGD_r) attacks. Then, we evaluate the impact of MTS PGD and PGD_r along with MTS Fast Gradient Sign Method (FGSM) and MTS Basic Iterative Method (BIM) on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Bi-directional LSTM based PdM system. Our results using NASA's turbofan engine dataset show that adversarial attacks can cause a severe defect (up to 11X) in the RUL prediction, outperforming the effectiveness of the state-of-the-art PdM attacks by 3X. Furthermore, we present a novel approximate adversarial training method to defend against adversarial attacks. We observe that approximate adversarial training can significantly improve the robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM defense methods by offering 3X more robustness.
Abstract (translated)
先进的预测维护(PdM)技术已经在降低复杂机器的维护成本和停机时间的同时,通过广泛利用物联网(IoT)和深度学习(DL)提高了整体生产率而取得了巨大的成功。不幸的是,IoT传感器和DL算法都容易受到网络攻击。例如,DL算法因其对反例的易受到攻击的特性而众所周知。这种对偶攻击在PdM领域被广泛忽视了。这是因为在计算机视觉领域用于分类任务的对偶攻击不能直接应用于PdM领域用于多变量时间序列(MTS)回归任务。在这项工作中,我们提出了一种端到端方法,以设计对偶 robust的PdM系统,通过广泛分析不同类型的对偶攻击的影响,并为具有DL功能的PdM模型提出了一种新的对偶防御技术。首先,我们提出了一种新的MTS Projected Gradient Descent(PGD)和MTS PGD随机重启攻击(PGD_r)。然后,我们评估了MTS PGD和PGD_r与MTS快速梯度 Sign Method(FGSM)和MTS基本迭代方法(BIM)对LSTM、 Gated Recurrent Unit(GRU)、卷积神经网络(CNN)和双向LSTM based pdM系统的对偶影响。使用NASA的涡轮风扇引擎数据集,我们的结果显示,对偶攻击可以导致严重的缺陷(高达11X),在RUL预测中引起严重缺陷,比最先进的PdM攻击方法提高了3X。此外,我们还提出了一种 novel approximate对偶训练方法,以防御对偶攻击。我们观察到,approximate对偶训练可以显著改善PdM模型的鲁棒性(高达54X),比最先进的PdM防御方法提供了3X更多的鲁棒性。
URL
https://arxiv.org/abs/2301.10822
