Scalable Infrastructure for Malware Labeling and Analysis
One of the best-known secrets of machine learning (ML) is that the most reliable way to get more accurate models […]
Presentations Our work in our own words
Hacking Facial Recognition Systems
Facial recognition is becoming ubiquitous, but how it works is often confused with standard image classification. In this short, high-level […]
Loss is More! Improving Malware Detectors by Learning Additional Tasks
Malware detection is perhaps the most common use case of machine learning for information security (ML-Sec/AI-Sec). ML-Sec malware detectors consist […]
Security data science – Getting the fundamental right
https://www.youtube.com/watch?v=XfT0Ju4vhvI&t=1254 A data science team is now table stakes for most security operations, however data science for security poses unique […]
Some Mistakes are More Mistaken Than Others: Using Cost-Matrix Clustering to Address Misclassification Cost Asymmetries in Website Content Classification
Website content classification has several salient characteristics as a machine learning problem, but perhaps the most salient is that it […]
Estimating Uncertainty for Binary Classifiers
In practical applications of binary classification, knowing the uncertainty of the prediction can be almost as important as knowing the […]
AI DevOps: Behind the Scenes of a Global AV’s Machine Learning Infrastrcture
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 […]
An Introduction to Machine Learning and Deep Learning
Machine learning is the science of developing programs that can automatically learn from data. First, this talk will give some […]
Garbage In, Garbage Out: How Purportedly Great Machine Learning Models can be Screwed Up by Bad Data
As processing power and deep learning techniques have improved, deep learning has become a powerful tool to detect and classify […]