Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
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
Published inJournal of Information Warfare
© Peregrine Technical Solutions, 2020
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.
PublisherPeregrine Technical Solutions
ISSN Search the Publication Forum1445-3312
Publication in research information system
MetadataShow full item record
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