Learning from Context: A Multi-view Deep Learning Architecture for Malware Detection
Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input […]
Topic <span>Glass-box ML</span>
Interpretable ML
Our interpretable ML research focuses on inventing, prototyping, and operationalizing methods to explain the “thought processes” of our security machine learning systems. This work has resulted in multiple successful commercial model shipments and patents.
Measuring the Speed of the Red Queen’s Race; Adaption and Evasion in Malware
Security is a constant cat-and-mouse game between those trying to keep abreast of and detect novel malware, and the authors […]
Getting Insight Out Of and Back Into Deep Neural Networks
Deep learning has emerged as a powerful tool for classifying malicious software artifacts, however the generic black-box nature of these […]