Detecting Software Code Vulnerabilities Using 2D Convolutional Neural Networks with Program Slicing Feature Maps

Throughout the post-PC world, IoT devices and real-time embedded systems have integrated themselves into every aspect of our lives. However, as technology integrates itself, the risk of security vulnerabilities and their impact also grows. Different languages have different susceptibilities to cyberattacks. Programs based on C and C++ are particularly vulnerable to cyberattacks through buffer overflow errors due to their lack of memory management. While security vulnerabilities are well-studied areas, with the increasing popularity of deep learning applications, the attack scenarios have become more sophis-ticated, and traditional techniques may no longer be enough to detect attacks or potential vulnerabilities. Therefore, the application of using deep learning to detect such vulnerabilities has become a trend. The appeal of using deep learning in software security is usually based on graphical models and data, which encourages researchers to use graphical-based deep learning methods like Graph Neural Networks (GNN). In this study, we use a Convolutional Neural Network (CNN) to detect security vulnerabilities due to its scalability and lower computation need. This paper proposes scanning vulnerable C/C++ functions for vulnerabilities using deep learning-based image classification. Our experiments are performed on the NIST SARD dataset. Inspired by existing high-level language vulnerability detection, we propose converting the function into an image through three steps: sentence embedding, a statically created program dependency graph, and social network analysis.We employ sentence embedding to convert each line of code into an equally sized representative vector. Following this, we produce a program dependency graph (PDG) for each function. Each line of code is then analyzed using three metrics: Katz, closeness, and destructibility. We compute the destructibility metric by employing forward-slicing on the PDG to determine the reachability of each line. The combination of the equally sized vectors and the analysis metrics are used to create a representative image of the source code. Finally, the image is classified through a Convolutional Neural Network.