Pattern-based methods for vulnerability discovery

Abstract

Discovering and eliminating critical vulnerabilities in program code is a key requirement for the secure operation of software systems. This task rests primarily on the shoulders of experienced code analysts who inspect programs in-depth to identify weaknesses. As software systems grow in complexity, while the amount of security critical code increases, supplying these analysts with effective methods to assist in their work becomes even more crucial. Unfortunately, exact methods for automated software analysis are rarely of help in practice, as they do not scale to the complexity of contemporary software projects, and are not designed to benefit from the analyst's domain knowledge. To address this problem, we present pattern-based vulnerability discovery, a novel approach of devising assistant methods for vulnerability discovery that are build with a high focus on practical requirements. The approach combines techniques of static analysis, machine learning, and graph mining to lend imprecise but highly effective methods that allow analysts to benefit from the machine's pattern recognition abilities without sacrificing the strengths of manual analysis.