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Exploring Adversarial Examples in Malware Detection.

, , and . IEEE Symposium on Security and Privacy Workshops, page 8-14. IEEE, (2019)

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Vulnerability Disclosure in the Age of Social Media: Exploiting Twitter for Predicting Real-World Exploits., , and . USENIX Security Symposium, page 1041-1056. USENIX Association, (2015)Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits., , , , and . USENIX Security Symposium, page 377-394. USENIX Association, (2022)When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks., , , , and . USENIX Security Symposium, page 1299-1316. USENIX Association, (2018)Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks., , , , , , and . NeurIPS, page 6106-6116. (2018)Data-Driven Techniques For Vulnerability Assessments.. University of Maryland, College Park, MD, USA, (2021)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/28370).Exploring Adversarial Examples in Malware Detection., , and . AAAI Fall Symposium: ALEC, volume 2269 of CEUR Workshop Proceedings, page 11-16. CEUR-WS.org, (2018)Too Big to FAIL: What You Need to Know Before Attacking a Machine Learning System., , , and . Security Protocols Workshop, volume 11286 of Lecture Notes in Computer Science, page 150-162. Springer, (2018)Exploring Adversarial Examples in Malware Detection., , and . IEEE Symposium on Security and Privacy Workshops, page 8-14. IEEE, (2019)Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits., , , , and . CoRR, (2021)Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning., , , , , and . CoRR, (2017)