A Multidimensional Detection Model of Android Malicious Applications Based on Dynamic and Static Analysis

This paper presents an approach utilizing static and dynamic analysis techniques to identify malicious Android applications. We extract static features, such as certificate information, and monitor real-time behavior to capture application properties. Using machine learning, our approach accurately differentiate between benign and malicious applications. We introduce the concept of "Multi-dimensional features", combining static and dynamic features into unique application fingerprints. This enables us to infer application families and target groups of related malware. Tested on a dataset of 8000 applications, our approach demonstrates high detection rates, low false positive and false negative rates. The results highlight the effectiveness of our comprehensive analysis in accurately identifying and mitigating Android malware threats.