Software Vulnerability Detection Tool Using Machine Learning Algorithms

Abstract

Software vulnerabilities pose a critical threat to the security and integrity of computer systems, necessitating advanced methods for their detection and mitigation. This paper presents a novel approach to software vulnerability detection leveraging machine learning (ML) algorithms. The proposed Software Vulnerability Detection Tool utilizes supervised learning techniques to analyze code snippets and identify potential vulnerabilities based on learned patterns and features. The methodology encompasses data collection, preprocessing, feature extraction, model training, and deployment within the software development lifecycle. Various ML algorithms, including logistic regression, decision trees, random forests, support vector machines, and deep learning models, are explored for their effectiveness in vulnerability detection. Hyperparameter tuning, cross-validation, and ensemble learning techniques are employed to optimize model performance and ensure robustness. The tool provides real-time feedback to developers, empowering them to address security issues proactively during code development. Continuous monitoring and feedback mechanisms enable the tool to adapt to evolving threats and code patterns. Integration with popular integrated development environments facilitates usability and adoption among developers. Through its proactive approach to vulnerability detection, the tool enhances the security posture of software systems and accelerates the code review process.