Software Vulnerability Detection Tool Using Machine Learning Algorithms

Abstract

With the rapid growth of software applications, security vulnerabilities have become a major concern in modern software development. Traditional vulnerability detection techniques rely heavily on manual code reviews and rule-based tools, which are often timeconsuming and unable to detect complex security flaws efficiently. To address thisissue, this research proposes a Software Vulnerability Detection Tool using Machine Learning algorithms that automatically identifies vulnerable and non-vulnerable code patterns from software datasets. The proposed system utilizes machine learning techniques to analyze code features and classify them based on the presence of vulnerabilities. An ensemble learning approach is implemented by combining multiple machine learning algorithms to improve the accuracy and reliability of vulnerability detection. The system allows users to upload datasets, train models, and visualize performance metrics such as accuracy, precision, recall, and F1-score through a web interface. Experimental results demonstrate that the ensemble classifier achieves high performance in detecting vulnerabilities, with improved prediction accuracy compared to individual algorithms. The developed tool provides an efficient and automated solution for identifying potential security weaknesses in software code, thereby assisting developers in improving software security and reducing the risk of cyber attacks. Software security has become a major concern due to the increasing number of cyber threats and vulnerabilities present in modern software applications. The experimental results demonstrate that the proposed approach provides efficient and reliable detection of software vulnerabilities, helping developers identify potential security risks early in the development process and improve overall software security.