Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection

Abstract

The explosive growth and increasing sophistication of Android malware call for new defensive techniques to protect mobile users against novel threats. To address this challenge, in this paper, we propose and develop an intelligent system named Dr.Droid to jointly model malware propagation and evolution for their detection at the first attempt. In Dr.Droid, we first exploit higher-level semantic and social relations within the ecosystem (e.g., app-market, app-developer, market-developer relations etc.) to characterize app propagation patterns; and then we present a structured heterogeneous graph to model the complex relations among different types of entities. To capture malware evolution, we further consider the temporal dependence and introduce a heterogeneous temporal graph to jointly model malware propagation and evolution by considering heterogeneous spatial dependencies with temporal dimensions. Afterwards, we propose a novel heterogeneous temporal graph transformer framework (denoted as HTGT) to integrate both spatial and temporal dependencies while preserving the heterogeneity to learn node representations for malware detection. Specifically, in our proposed HTGT, to preserve the heterogeneity, we devise a heterogeneous spatial transformer to derive heterogeneous attentions over each node and edge to learn dedicated representations for different types of entities and relations; to model temporal dependencies, we design a temporal transformer into the HTGT to attentively aggregate its historical sequences of a given node (e.g., app); the two transformers work in an iterative manner for representation learning. Promising experimental results based on the large-scale sample collections from anti-malware industry demonstrate the performance of Dr.Droid, by comparison with state-of-the-art baselines and popular mobile security products.