Azure Security Center

Tender, Peter De; Rendón, David; Erskine, Samuel

doi:10.1007/978-1-4842-4910-9_5

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, the cloud layer also has its vulnerabilities [43], however, typically third-party cloud services such as AWS, Azure, etc. have their security measures [44], [45] which makes them less vulnerable when compared to the physical and edge layer.…”

Section: Adversarial Attacks On Deep Learningmentioning

confidence: 99%

Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)

Mode¹,

Hoque²

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Adversarial Attacks On Deep Learningmentioning

confidence: 99%