Malware Detection Method Based on Feature Fusion

Abstract In recent years, as cyberattacks continue to escalate, malware has become increasingly diverse and complex, posing significant security threats to enterprises, government agencies, and individual users. Malware developers often employ techniques such as feature obfuscation and behavior hiding, rendering traditional detection methods less effective. To address this challenge, this study proposes a malware detection method based on feature fusion and a multi-feature detection framework. The method extracts frequency features and semantic information from opcodes and readable characters, and byte transition probabilities from byte sequences, thereby constructing a comprehensive feature vector. A two-layer detection framework that combines deep learning with traditional machine learning is designed, effectively integrating different feature types and overcoming the limitations of single-feature approaches. Experimental results demonstrate that the proposed method significantly outperforms traditional algorithms in terms of detection accuracy and generalization capability, greatly enhancing the detection of complex malware families. Notably, it excels in handling packed code, obfuscation techniques, and imbalanced data, offering an efficient solution for malware detection.