Malicious Code Detection Based on Code Semantic Features

Abstract

With the development of smart phones, malicious applications for the Android platform have increased dramatically. The existing Android malicious code analysis methods majorly focus on detection based on signatures, inter-component communication, and other configuration information features. Such methods ignore the effect of the semantic features of the malicious code. Even a few such studies that exist are based on the statistical features of the code for malicious code detection. To address these shortcomings, we (1) use the code semantic structure features to reflect deep semantic information, (2) propose a preprocessing method of APK files to generate graphics that reflect the code semantic features, and (3) introduce the advanced graphical semantics for a graph convolutional network (GCN) model to automatically identify and learn semantics and extract features for malicious code detection. Experiments on a dataset confirm that the proposed method can achieve 95.8% detection accuracy. Compared with the existing methods that adopt configuration information features or statistical features of codes, our method shows higher accuracy.