A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios-Reference-Cited by-同舟云学术

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Published:2022 Issue: Volume:10 Page:124582-124594
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Perales Gomez Angel Luis¹^ORCID,Maimo Lorenzo Fernandez¹^ORCID,Clemente Felix J. Garcia¹^ORCID,Morales Javier Alejandro Maroto²^ORCID,Celdran Alberto Huertas³^ORCID,Bovet Gerome⁴^ORCID

Affiliation:

1. Departamento de Ingeniería y Tecnología de Computadores, University of Murcia, Espinardo, Murcia, Spain

2. École polytechnique fédérale de Lausanne, Lausanne, Switzerland

3. Department of Informatics (IfI), Communication Systems Group CSG, University of Zurich, Zürich, Switzerland

4. Cyber-Defence Campus within armasuisse Science & Technology, Thun, Switzerland

Funder

Spanish Ministry of Science, Innovation and Universities, State Research Agency, FEDER funds

Swiss Federal Office for Defense Procurement

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09964189.pdf?arnumber=9964189

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4. Imperceptible adversarial attacks on tabular data;ballet;Proc NIPS Workshop Robust AI Financial Services Data Fairness Explainability Trustworthiness Privacy (Robust AI FS),2019

5. Intriguing Properties of Adversarial ML Attacks in the Problem Space

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