A1 Journal article (refereed)
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems (2020)

Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (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

Open Access: Publication channel is not openly available


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

Preliminary JUFO rating: 1

Last updated on 2020-02-10 at 14:29