Towards a Block-Level Conformer-Based Python Vulnerability Detection

Software vulnerabilities pose a significant threat to computer systems because they can jeopardize the integrity of both software and hardware. The existing tools for detecting vulnerabilities are inadequate. Machine learning algorithms may struggle to interpret enormous datasets because of their limited ability to understand intricate linkages within high-dimensional data. Traditional procedures, on the other hand, take a long time and require a lot of manual labor. Furthermore, earlier deep-learning approaches failed to acquire adequate feature data. Self-attention mechanisms can process information across large distances, but they do not collect structural data. This work addresses the critical problem of inadequate vulnerability detection in software systems. We propose a novel method that combines self-attention with convolutional networks to enhance the detection of software vulnerabilities by capturing both localized, position-specific features and global, content-driven interactions. Our contribution lies in the integration of these methodologies to improve the precision and F1 score of vulnerability detection systems, achieving unprecedented results on complex Python datasets. In addition, we improve the self-attention approaches by changing the denominator to address the issue of excessive attention heads creating irrelevant disturbances. We assessed the effectiveness of this strategy using six complex Python vulnerability datasets obtained from GitHub. Our rigorous study and comparison of data with previous studies resulted in the most precise outcomes and F1 score (99%) ever attained by machine learning systems.