Comparative Evaluation of Static and Dynamic Malware Analysis for Android Using AI Techniques

Abstract

With the rapid proliferation of Android devices, the volume and sophistication of Android-based malware have increased significantly. Traditional signature-based detection systems are insufficient to combat evolving threats. This paper presents a comparative evaluation of two core malware detection approaches: static and dynamic analysis. Emphasis is placed on the integration of artificial intelligence (AI) techniques such as machine learning and deep learning within these analysis methods. We explore their advantages, limitations, performance metrics, and practical applicability, culminating in a comprehensive comparative assessment to inform researchers and security practitioners.