Machine-learning supported vulnerability detection in source code

Abstract

The awareness of writing secure code rises with the increasing number of attacks and their resultant damage. But often, software developers are no security experts and vulnerabilities arise unconsciously during the development process. They use static analysis tools for bug detection, which often come with a high false positive rate. The developers, therefore, need a lot of resources to mind about all alarms, if they want to consistently take care of the security of their software project. We want to investigate, if machine learning techniques could point the user to the position of a security weak point in the source code with a higher accuracy than ordinary methods with static analysis. For this purpose, we focus on current machine learning on code approaches for our initial studies to evolve an efficient way for finding security-related software bugs. We will create a configuration interface to discover certain vulnerabilities, categorized in CWEs. We want to create a benchmark tool to compare existing source code representations and machine learning architectures for vulnerability detection and develop a customizable feature model. At the end of this PhD project, we want to have an easy-to-use vulnerability detection tool based on machine learning on code.