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Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors.

, , , , , and . AISec@CCS, page 233-244. ACM, (2023)

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Nebula: Self-Attention for Dynamic Malware Analysis., , , and . CoRR, (2023)Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations., , , , and . CoRR, (2020)Robust Machine Learning for Malware Detection over Time., , , and . ITASEC, volume 3260 of CEUR Workshop Proceedings, page 169-180. CEUR-WS.org, (2022)Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries., , , , and . ITASEC, volume 2315 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving., , , , , , and . ICMLC, page 57-62. IEEE, (2023)Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis., , , , , , , , , and 1 other author(s). CoRR, (2024)A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection., , , and . CoRR, (2024)WAF-A-MoLE: evading web application firewalls through adversarial machine learning., , , and . SAC, page 1745-1752. ACM, (2020)WAF-A-MoLE: An adversarial tool for assessing ML-based WAFs., , , and . SoftwareX, (2020)AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples., , , , , , , and . CoRR, (2024)