Latest

• Training Robust ML-based Raw-Binary Malware Detectors in Hours, not Months
• Group-based Robustness: A General Framework for Customized Robustness in the Real World
• Adversarial Training for Raw-Binary Malware Classifiers
• Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
• Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
• How Risky Are Real Users' IFTTT Applets?
• $n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers
• A General Framework for Adversarial Examples with Objectives
• A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats
• Comparing Hypothetical and Realistic Privacy Valuations
• On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
• Riding Out DOMsday: Toward Detecting and Preventing DOM Cross-Site Scripting
• Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition
• (Do Not) Track Me Sometimes: Users' Contextual Preferences for Web Tracking