AUTOMATED DETECTION OF WEB APPLICATION VULNERABILITIES USING MACHINE LEARNING ALGORITHMS

Abstract

The rapid growth of web applications has revolutionized digital communication, e-commerce, online banking, healthcare services, education platforms, and enterprise operations. As organizations increasingly rely on web-based systems for delivering services and managing sensitive information, web application security has become a critical concern. Cybercriminals continuously exploit vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Remote Code Execution (RCE), and authentication flaws to gain unauthorized access to systems and data. Traditional vulnerability assessment techniques, including manual code reviews and signature-based detection tools, often struggle to identify emerging threats and zero-day vulnerabilities due to the growing complexity of modern web applications. Consequently, there is an increasing need for intelligent and automated security solutions capable of detecting vulnerabilities efficiently and accurately. Machine Learning (ML) has emerged as a promising technology for enhancing cybersecurity by enabling systems to learn from historical data and identify malicious patterns automatically. ML algorithms can analyze large volumes of web application logs, source code characteristics, network traffic, and user behavior data to detect vulnerabilities and suspicious activities. Unlike conventional rule-based systems, ML-based approaches can adapt to evolving attack techniques and improve detection performance over time. Various classification algorithms such as Decision Trees, Random Forests, Support Vector Machines (SVM), Naïve Bayes, and Neural Networks have demonstrated significant potential in vulnerability detection and threat analysis. This study proposes an automated machine learning framework for detecting web application vulnerabilities. The framework integrates data acquisition, feature extraction, preprocessing, machine learning classification, vulnerability analysis, and automated alert generation modules. The proposed system aims to improve vulnerability detection accuracy while reducing manual effort and response time. Performance evaluation is conducted using standard cybersecurity metrics including accuracy, precision, recall, F1-score, and response time. The findings are expected to demonstrate that machine learning algorithms significantly enhance the efficiency and effectiveness of web application vulnerability detection. The proposed framework contributes to the development of intelligent cybersecurity systems capable of supporting secure software development and proactive threat management. Furthermore, the research provides valuable insights for cybersecurity professionals, software developers, researchers, and organizations seeking advanced solutions for protecting web applications against increasingly sophisticated cyber threats.