Measuring the Speed of the Red Queen's Race - Adaption and Evasion in Malware

Explore the dynamic arms race between malware authors and security practitioners in this 26-minute Black Hat conference talk. Delve into the application of machine learning in cybersecurity to quantify the pace at which malware evolves in response to detection efforts. Examine the use of statistical methods to analyze historical data, model confidence, and label data. Gain insights into the adaptation and evasion techniques employed by malware creators and the challenges faced by security experts in staying ahead of emerging threats. Learn how deep learning approaches are being utilized to measure and understand the speed of malware evolution in this ongoing battle for digital security.

Introduction
Deep Learning
Model Confidence
Historical Data
Label Data
Conclusion