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 editorsVähäkainu, Petri; Lehto, Martti; Kariluoto, Antti

Journal or seriesJournal of Information Warfare

ISSN1445-3312

eISSN1445-3347

Publication year2020

Volume19

Issue number4

Pages range57-69

PublisherPeregrine Technical Solutions

Publication countryUnited States

Publication languageEnglish

Persistent website addresshttps://www.jinfowar.com/journal/volume-19-issue-4/adversarial-attack%E2%80%99s-impact-machine-learning-model-cyber-physical-systems

Publication open accessNot 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.


Keywordsdata securitycyber securitycyber attacksintelligent systemsInternet of thingscloud servicesartificial intelligencemachine learning

Free keywordsArtificial Intelligence; cloud data platform; adversarial attacks; defence mechanisms; machine learning


Contributing organizations


Ministry reportingYes

Reporting Year2020

JUFO rating1


Last updated on 2024-22-04 at 10:45