Anakin: explainable android malware detection with graph neural networks

Abstract

Android OS is today the most used Operating System for mobile devices. However, it is susceptible to several malware attacks that may seriously compromise the privacy and security of individuals and organizations. This paper proposes an approach based on a static analysis of decompiled Android PacKages (APKs) to extract critical APIs and detect Android malware. The main contributions lie in the adoption of a graph-based data engineering schema to represent APIs taken from the Function Call Graphs of decompiled APKs and the formulation of a graph-based deep learning approach for explainable malware detection. In particular, the proposed approach, named ANAKIN, implements a Graph Neural Network (GNN) for binary classification (malware versus goodware), and integrates GNNExplainer algorithm to disclose how specific API classes and control-flow edges between API calls influence malware alerts. The proposed approach was evaluated by considering 26,527 Android APKs. The results of an extensive and in-depth evaluation show that the presented GNN model achieves higher accuracy than deep neural models trained with traditional API call sequence representations and publicly available related methods. On the other hand, it produces decision explanations that yield interesting insights into the malicious patterns of APKs and support root cause analysis of missed malware alarms.