Detection, Traceability, and Propagation of Mobile Malware Threats

In recent years, the application of smartphones, Android operating systems and mobile applications have become more prevalent worldwide. To study the traceability, propagation, and detection of the threats, we perform research on all aspects of the end-to-end environment. With machine learning based on the mobile malware detection algorithms that integrate the dynamic and static research of the identification algorithm, application software samples are collected to study sentences. Through knowledge labeling and knowledge construction, the association relationship of knowledge is extracted to realize the research of knowledge map construction. Flooding is closely correlated with the complexity of the Android mobile version of the kernel and malicious programs. A static dynamic analysis of the mobile malicious program is carried out, and the social network social diagram is constructed to model the propagation of the mobile malicious program. We extended the approach of deriving common malware behavior through graph clustering. On this basis, Android behavior analysis is performed through our virtual machine execution engine. We extend the family characteristics to the concept of DNA race genes. By studying SMS/MMS, Bluetooth, 5G base station networks, metropolitan area networks, social networks, homogeneous communities, telecommunication networks, and application market ecosystem propagation scenarios, we discovered the law of propagation. In addition, we studied the construction of the mobile Internet big data knowledge graph. Quantitative data for the main family chronology of mobile malware are obtained. We conducted detailed research and comprehensive analysis of Android application package (APK) details and behavior, relationship, resource-centric, and syntactic aspects. Furthermore, we summarized the architecture of mobile malware security analysis. We also discuss encryption of malware traffic discrimination. These precise modeling and quantified research results constitute the architecture of mobile malware analysis.