Off the beaten path: machine learning for offensive security

Abstract

Machine learning has been widely used for defensive security. Numerous approaches have been devised that make use of learning techniques for detecting attacks and malicious software. By contrast, only very few research has studied how machine learning can be applied for offensive security. In this talk, we will explore this research direction and show how learning methods can be used for discovering vulnerabilities in software, finding information leaks in protected data, or supporting network reconnaissance. We discuss advantages and challenges of learning for offensive security as well as identify directions for future research.