Enhanced Malware Detection Using Deep Learning: A Comprehensive Framework for Feature Extraction and Classification

Abstract

The exponential growth and sophistication of malware necessitate new detection strategies. The rapid evolution of malware makes traditional manual heuristic practices ineffective in perceiving new malware variants. Machine learning systems have proven essential for automating the dynamic and static analysis, as they cluster similar malware behaviors and classify new infections based on their similarity to approved malware families. This research validates that deep learning networks can accomplish higher accuracy than customary machine learning approaches. Deep learning has multiple neural network layers, which allows it to better automatically ascertain and classify malware variants. It offers a framework for removing multiple signature sets, including parts, opcode, bytecode, and system calls, from malware files. Experimental consequences indicate that the most accurate feature vector is the feature vector generated through system calls. This study concludes that deep learning approaches outperform traditional shallow machine learning systems in terms of malware recognition and classification precision.