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Adversary Resistant Deep Neural Networks with an Application to Malware Detection.

, , , , , , and . KDD, page 1145-1153. ACM, (2017)

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From proof-of-concept to exploitable., , , , , and . Cybersecurity, 2 (1): 12 (2019)Learning Adversary-Resistant Deep Neural Networks., , , , , , and . CoRR, (2016)Assessing Prompt Injection Risks in 200+ Custom GPTs., , , , and . CoRR, (2023)An Empirical Study of Spam and Prevention Mechanisms in Online Video Chat Services, , , , and . CoRR, (2012)Facilitating Vulnerability Assessment through PoC Migration., , , , , , and . CCS, page 3300-3317. ACM, (2021)From Physical to Cyber: Escalating Protection for Personalized Auto Insurance., , , , , , , and . SenSys, page 42-55. ACM, (2016)Understanding Malvertising Through Ad-Injecting Browser Extensions., , , , , , and . WWW, page 1286-1295. ACM, (2015)Enhancing group recommendation by incorporating social relationship interactions., , , , , , and . GROUP, page 97-106. ACM, (2010)BScout: Direct Whole Patch Presence Test for Java Executables., , , , , , , , , and . USENIX Security Symposium, page 1147-1164. USENIX Association, (2020)Adversarial Policy Training against Deep Reinforcement Learning., , , and . USENIX Security Symposium, page 1883-1900. USENIX Association, (2021)