Detection of Software Vulnerabilities using Machine Learning

• Applicant: Professor Dr. Konrad Rieck
• Subject Area: Software Engineering and Programming Languages
• Term: from 2014 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 242913835

The early detection of vulnerabilities in software is a key for securing computer systems. While some types of security flaws can be identified automatically, the majority of vulnerabilities is still discovered by time-consuming manual analysis. This project aims at developing novel methods for vulnerability discovery using machine learning techniques. To this end, structured representations of code, such as parse trees and control flow graphs, are embedded in vector spaces and analyzed using unsupervised learning algorithms for semantic analysis and anomaly detection. This analysis should enable identifying typical programming patterns automatically and detecting potential vulnerabilities as deviations from these patterns.

• DFG Programme: Research Grants