Fast Reliable Malware Detection Using the KNN Algorithm

Cyber security has become a critical concern in the modern digital era, where malicious software continuously evolves to exploit system vulnerabilities. Traditional signature-based malware detection techniques are no longer effective against advanced polymorphic and zero-day attacks, as they rely on predefined signatures that fail to adapt to new threats. This study introduces a machine learning-based malware detection framework that leverages the K-Nearest Neighbors (KNN) algorithm for efficient and adaptive classification. The proposed system extracts distinctive static features such as entropy, file size, and opcode frequency from executable files to capture the behavioral and structural characteristics of malware. By applying feature normalization and optimized distance-based classification, the KNN model effectively distinguishes between benign and malicious files with high precision. Experimental evaluations demonstrate improved detection accuracy, robustness, and scalability compared to conventional approaches. The findings validate KNN as a lightweight yet powerful method for enhancing automated malware detection, offering both interpretability and adaptability for future cyber security systems.