Enhancing Software Security through Machine Learning-Based Vulnerability Detection

Abstract

 Software security remains one of the most critical concerns in the development of digital systems due to the increasing frequency and sophistication of cyberattacks. Traditional methods of vulnerability detection, such as manual code review, static code analysis, and dynamic testing, are often limited by their complexity, high cost, and slow pace, especially in modern software systems with massive codebases. Machine learning (ML) offers a promising alternative, leveraging automation to identify vulnerabilities faster and more accurately.

This paper proposes a novel framework that employs ML models to automate the detection of security vulnerabilities in software applications, enhancing both the efficiency and accuracy of the vulnerability detection process. The approach focuses on using supervised learning, unsupervised learning, and deep learning models to classify and detect common security flaws such as buffer overflows, SQL injection, cross-site scripting (XSS), and more.

We present a detailed methodology involving data preprocessing, feature extraction, and model training on a large dataset of known vulnerabilities. The study utilizes models such as decision trees, support vector machines (SVM), and deep neural networks (DNN), evaluating their performance based on precision, recall, accuracy, and F1- score. Our results indicate that ML-based vulnerability detection surpasses traditional methods in both speed and accuracy. Furthermore, we explore how natural language analysis to further refine vulnerability detection, particularly for non-traditional coding errors.

In comparison to static and dynamic analysis techniques, ML models exhibit significantly reduced false positives and false negatives, demonstrating their potential for integration into automated software development workflows. This paper concludes by discussing the broader implications of adopting ML in cybersecurity and suggesting future research directions in this evolving field.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.164-171