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It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors.

, , , , , and . USENIX Security Symposium, USENIX Association, (2024)

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Identifying malicious hosts involved in periodic communications., , , , and . NCA, page 11-18. IEEE Computer Society, (2017)On the Evaluation of Sequential Machine Learning for Network Intrusion Detection., , and . ARES, page 113:1-113:10. ACM, (2021)Attacking Logo-Based Phishing Website Detectors with Adversarial Perturbations., , , , , and . ESORICS (3), volume 14346 of Lecture Notes in Computer Science, page 162-182. Springer, (2023)It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors., , , , , and . USENIX Security Symposium, USENIX Association, (2024)Äre Adversarial Phishing Webpages a Threat in Reality?" Understanding the Users' Perception of Adversarial Webpages., , , , and . WWW, page 1712-1723. ACM, (2024)"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice., , , , , and . CoRR, (2022)"Hey Players, there is a problem...": On Attribute Inference Attacks against Videogamers., and . CoG, page 1-8. IEEE, (2024)On the effectiveness of machine and deep learning for cyber security., , , , and . CyCon, page 371-390. IEEE, (2018)Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems., , , , and . CoRR, (2021)Towards an Efficient Detection of Pivoting Activity., , , and . IM, page 980-985. IEEE, (2021)