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SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection


Apr 30, 2023
Giovanni Apruzzese, Pavel Laskov, Johannes Schneider

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"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice


Dec 29, 2022
Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, David Freeman, Fabio Pierazzi, Kevin A. Roundy

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Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors


Dec 11, 2022
Giovanni Apruzzese, V. S. Subrahmanian

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SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning


Oct 24, 2022
Giovanni Apruzzese, Mauro Conti, Ying Yuan

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Attribute Inference Attacks in Online Multiplayer Video Games: a Case Study on Dota2


Oct 17, 2022
Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, Mauro Conti

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Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples


Jul 04, 2022
Giovanni Apruzzese, Rodion Vladimirov, Aliya Tastemirova, Pavel Laskov

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The Role of Machine Learning in Cybersecurity


Jun 20, 2022
Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de Oca, Wissam Mallouli, Luis Burdalo Rapa, Athanasios Vasileios Grammatopoulos, Fabio Di Franco

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SoK: The Impact of Unlabelled Data in Cyberthreat Detection


May 18, 2022
Giovanni Apruzzese, Pavel Laskov, Aliya Tastemirova

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Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike


Mar 18, 2022
Johannes Schneider, Giovanni Apruzzese

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* Accepted at IEEE Symposium on Security and Privacy (S&P) Workshop on Deep Learning and Security, 2022 

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The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems


Mar 09, 2022
Giovanni Apruzzese, Luca Pajola, Mauro Conti

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