PATTERN RECOGNITION METHODS FOR ANALYZING AND PREVENTING WEB-BASED MALWARE ATTACKS

Abstract

Web-based malware attacks represent one of the most dangerous cybersecurity threats affecting modern digital infrastructures. Traditional security systems often struggle to identify sophisticated malware because attackers use encryption, obfuscation, and polymorphic techniques to avoid detection. This study investigates the application of pattern recognition methods for analyzing and preventing web-based malware attacks. The proposed framework integrates machine learning algorithms and intelligent behavioral analysis techniques to recognize suspicious activities within web environments. Pattern recognition technologies analyze malware signatures, user behavior, network traffic anomalies, and abnormal request patterns to classify malicious activities accurately. The study demonstrates that intelligent cybersecurity systems can significantly improve malware detection accuracy and reduce system vulnerabilities. Experimental observations confirm that pattern recognition methods provide adaptive and scalable protection against evolving web-based cyber threats.