Behavior Intention Derivation of Android Malware Using Ontology Inference

Abstract

Previous researches on Android malware mainly focus on malware detection, and malware's evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.