Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)
This addresses a critical security problem for manufacturing industries relying on IoT and AI for equipment maintenance, though it is incremental as it adapts existing adversarial methods to a new domain.
The paper tackles the vulnerability of deep learning-based prognostics and health management systems to adversarial attacks by applying techniques like FGSM and BIM to models such as LSTM, GRU, and CNN on a turbofan engine dataset, showing that these attacks cause serious defects in remaining useful life estimation and are highly transferable.
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.