Source code vulnerability identification using machine learning models

Software vulnerabilities remain a major challenge in modern software development, frequently leading to security breaches, unauthorized access, service disruption, and financial losses. Although traditional vulnerability detection methods such as static and dynamic analysis are widely used, they often generate excessive false positives and struggle to capture complex patterns within source code. Recent advances in machine learning provide an opportunity to improve vulnerability detection by automatically learning relationships that are difficult to identify using rule-based approaches.
This study presents a machine learning framework for automated source code vulnerability identification using benchmark datasets obtained from the Software Assurance Reference Dataset (SARD) and the National Vulnerability Database (NVD). The framework includes source code preprocessing, feature extraction, model training, hyperparameter optimization, and performance evaluation. Four models Decision Tree, Random Forest, Support Vector Machine, and Long Short-Term Memory (LSTM) were implemented and evaluated using accuracy, precision, recall, and F1-score.
Experimental results show that LSTM achieved the best overall performance with an accuracy of 93.4%, followed by Random Forest at 91.2%. These findings indicate that models capable of learning contextual and sequential information are particularly effective for vulnerability detection. The proposed framework demonstrates how machine learning can support secure software development by reducing manual analysis effort and enabling scalable vulnerability assessment. Its potential application extends to continuous security monitoring and integration within modern software development pipelines.