A1 Journal article (refereed)
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems (2020)
Vähäkainu, P., Lehto, M., & Kariluoto, A. (2020). Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems. Journal of Information Warfare, 19(4), 57-69. https://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems
JYU authors or editors
Publication details
All authors or editors: Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti
Journal or series: Journal of Information Warfare
ISSN: 1445-3312
eISSN: 1445-3347
Publication year: 2020
Volume: 19
Issue number: 4
Pages range: 57-69
Publisher: Peregrine Technical Solutions
Publication country: United States
Publication language: English
Persistent website address: https://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/74117
Abstract
Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data platform service. Adversarial examples are malicious inputs to ML-models that provide erroneous model outputs while appearing to be unmodified. This kind of attack can fool the classifier and can prevent ML-models from generalizing well and from learning high-level representation; instead, the ML-model learns superficial dataset regularity. This study focuses on investigating, detecting, and preventing adversarial attacks towards a cloud data platform in the cyber-physical context.
Keywords: data security; cyber security; cyber attacks; intelligent systems; Internet of things; cloud services; artificial intelligence; machine learning
Free keywords: Artificial Intelligence; cloud data platform; adversarial attacks; defence mechanisms; machine learning
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 1