Abstract
Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.
Highlights
Brain-machine interfaces (BMIs) are a communication technology that link humans and artificial devices through brain signals
We summarize the main contributions of this paper as follows: (i) We introduce an analysis approach combining static and dynamic behavioral characteristics that is capable of depicting Android malware with comprehensive and accuracy, which is able to provide a high-quality behavioral characteristic dataset for Android malware detection
(ii) We provide an explainable analysis for each kind of behavioral characteristics we extracted. is analysis could explicit illustrate each kind of behavioral characteristics playing the role of Android malware detection
Summary
Brain-machine interfaces (BMIs) are a communication technology that link humans and artificial devices through brain signals. Li et al [2], DroidAPIMiner [3], APK Auditor [4], and Drebin [5] focus on extracting static behavioral characteristics from Android app and combine them with well-known classification algorithms to detect Android malicious app This kind of approach cannot detect malware with code obfuscation and cannot capture behavioral characteristics when running the app. Ird, which kind of behavioral characteristics plays important role during Android malware detection is not illustrated in these works To address these problems, we propose a broad and efficient Android malware behavioral characteristics analysis approach under a real-world dataset. We extract 12 dynamic behavioral characteristics including network traffic and system call for analysis and we not need modify OS code to track data flow during Android app running
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