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pro vyhledávání: '"Rashid, Aqib"'
Autor:
Rashid, Aqib, Such, Jose
ML models are known to be vulnerable to adversarial query attacks. In these attacks, queries are iteratively perturbed towards a particular class without any knowledge of the target model besides its output. The prevalence of remotely-hosted ML class
Externí odkaz:
http://arxiv.org/abs/2302.10739
Autor:
Rashid, Aqib, Such, Jose
Several moving target defenses (MTDs) to counter adversarial ML attacks have been proposed in recent years. MTDs claim to increase the difficulty for the attacker in conducting attacks by regularly changing certain elements of the defense, such as cy
Externí odkaz:
http://arxiv.org/abs/2302.00537
Autor:
Rashid, Aqib, Such, Jose
Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image recognition domain. The ML-based malware detection domain has received less attention despite its importance. Moreover, most w
Externí odkaz:
http://arxiv.org/abs/2202.07568
Autor:
Rashid, Aqib, Such, Jose
Publikováno v:
In Computers & Security November 2023 134