Abstract :

With The Increasing Complexity Of Software Systems, Software Vulnerabilities Have Become A Major Concern, Leading To Security Breaches, Data Loss, And Financial Damage. Traditional Vulnerability Detection Methods, Such As Manual Code Reviews And Signature-based Scanning, Are Often Timeconsuming, Error-prone, And Unable To Detect Unknown Or Zero-day Vulnerabilities. The Proposed Project Focuses On Developing A Software Vulnerability Detection Tool Using Machine Learning (ML) Algorithms To Automate And Enhance The Process Of Identifying Security Flaws In Software Code. The System Leverages Static Code Analysis And Feature Extraction To Transform Source Code Into A Format Suitable For ML Models. Algorithms Such As Random Forest, Support Vector Machine (SVM), And Neural Networks Are Employed To Classify Code Segments As Vulnerable Or Safe. By Training The Models On Datasets Of Known Vulnerabilities, The System Can Learn Patterns Indicative Of Security Weaknesses And Predict Potential Threats In New, Unseen Code. This Approach Provides Faster, More Accurate, And Scalable Vulnerability Detection Compared To Traditional Methods. Additionally, The Tool Can Assist Developers In Early Identification Of Security Risks, Allowing For Proactive Remediation During The Software Development Lifecycle. The Integration Of Machine Learning Ensures Adaptability To Evolving Coding Practices And Emerging Threats, Making The System A Robust Solution For Enhancing Software Security. Keywords: Software Vulnerability, Machine Learning, Static Code Analysis, Security Flaws, Automated Detection, Neural Networks, SVM, Random Forest.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

212-216

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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