Improving Security Detection with Machine Learning: A Scalable Solution for Real-Time Vulnerability Analysis

The sophistication of web applications has increased the attack surface of vulnerabilities, such as XSS and SQLi. Conventional detection methods include manual review and signature-based systems, which are inadequate to handle zero-day and emerging threats. The present study discusses a new framework of machine learning for vulnerability detection with improved accuracy and scalability. This minimizes the issue of false positives and false negatives at high rates during detection processes. The system will use a web application performing vulnerability real-time analysis; such analysis will be carried out on the usage of a preprocessed dataset, labeled with a preprocessed feature extracted by using Term Frequency-Inverse Document Frequency (TF-IDF). There will be several machine learning models, that will be used, trained, and optimized to decide which one is the best for detection. Our proposed solution can process large datasets efficiently, keeping in mind computational performance versus detection precision, with an interface designed to provide user-friendly dynamic vulnerability analysis. The experimental results indicate substantial detection accuracy improvement, therefore this framework is a real transformation step in web application security.