2021/10/14 by Weizhong Yan, Yan, Weizhong, Zhaoyuan Yang +3
Chemistry · Computer Science · #Adversarial Robustness in Machine Learning #Adversarial system #Algorithm #Anomaly Detection Techniques and Applications #Artificial intelligence #Computer science #Computer security #Cryptography and Security (cs.CR) #Data mining #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine learning #Mass Spectrometry Techniques and Applications #Vulnerability (computing) #Vulnerability assessment #cs.CR #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.07462
arxiv created 2021/10/14 · openalex publication_date 2021/10/14 · arxiv updated 2021/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With proliferation of deep learning (DL) applications in diverse domains, vulnerability of DL models to adversarial attacks has become an increasingly interesting research topic in the domains of Computer Vision (CV) and Natural Language Processing (NLP). DL has also been widely adopted to diverse PHM applications, where data are primarily time-series sensor measurements. While those advanced DL algorithms/models have resulted in an improved PHM algorithms' performance, the vulnerability of those PHM algorithms to adversarial attacks has not drawn much attention in the PHM community. In this paper we attempt to explore the vulnerability of PHM algorithms. More specifically, we investigate the strategies of attacking PHM algorithms by considering several unique characteristics associated with time-series sensor measurements data. We use two real-world PHM applications as examples to validate our attack strategies and to demonstrate that PHM algorithms indeed are vulnerable to adversarial attacks.