Similarity-Based Hybrid Malware Detection Model Using API Calls

Abstract

This study presents a novel Similarity-Based Hybrid API Malware Detection Model (HAPI-MDM) aiming to enhance the accuracy of malware detection by leveraging the combined strengths of static and dynamic analysis of API calls. Faced with the pervasive challenge of obfuscation techniques used by malware authors, the conventional detection models often struggle to maintain robust performance. Our proposed model addresses this issue by deploying a two-stage learning approach where the XGBoost algorithm acts as a feature extractor feeding into an Artificial Neural Network (ANN). The key innovation of HAPI-MDM is the similarity-based feature, which further enhances the detection accuracy of the dynamic analysis, ensuring reliable detection even in the presence of obfuscation. The model was evaluated using seven machine learning techniques with 10 K-fold cross-validation. Experimental results demonstrated HAPI-MDM’s superior performance, achieving an overall accuracy of 97.91% and the lowest false-positive and false-negative rates compared to related works. The findings suggest that integrating dynamic and static API-based features and utilizing a similarity-based feature significantly improves malware detection performance, thereby offering an effective tool to fortify cybersecurity measures against escalating malware threats.

Keywords

• malware detection
• API calls correlation
• feature similarity
• static and dynamic analysis