MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection. (arXiv:2302.10739v1 [cs.LG])

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 classification models and Machine-Learning-as-a-Service
platforms means that query attacks pose a real threat to the security of these
systems. To deal with this, stateful defenses have been proposed to detect
query attacks and prevent the generation of adversarial examples by monitoring
and analyzing …

adversarial as-a-service attacks class classification deal defense detect detection knowledge machine malware malware detection ml models platforms query security service systems target threat vulnerable