Android application maliciousness and malicious race detection model construction method and application

Abstract

The invention belongs to the field of mobile malicious application detection. The invention particularly relates to an Android application maliciousness and malicious race detection model constructionmethod and application. For different obfuscation techniques, operation code features of different granularities are extracted; each characteristic weight under each granularity is calculated; the operation code feature which is most beneficial to enhancing the detection effect of the original sample set and reducing the difference of samples before and after confusion is selected from various operation code features; an anti-confusion feature set with various granularities is constructed to resist mainstream obfuscation techniques, an operation code feature sequence of an application sampleis extracted from the anti-confusion feature sets with various granularities, the operation code feature sequence is converted into a grey-scale map, a deep learning network model is trained according to different label values based on the grey-scale map, malicious classification and malicious family classification of the Android application are realized, and detection function integration is realized on mobile terminal equipment. According to the method, while application maliciousness and malicious family detection are supported, interference of an Android application confusion technology on a detection result is effectively resisted, and the detection reliability is high.

Description

technical field [0001] The invention belongs to the field of mobile malicious application detection, and more specifically relates to a method and application for building an Android application maliciousness and malicious race detection model. Background technique [0002] With the continuous development of the Android application market and the continuous and rapid growth of the number of Android applications, many third-party application markets have emerged. However, Android applications contain Android users' personal privacy data, which attracts some attackers to spread malicious applications to users' mobile smartphones. Mobile phones, in order to steal the user's privacy and property, a serious threat to the privacy and data security of Android users. [0003] A special report on Android malware released by security vendor 360 shows that in 2019 alone, 360 Security Brain intercepted about 1.809 million mobile malicious applications, and blocked about 950 million Andr...

AI Technical Summary

Problems solved by technology

[0008] The present invention provides a method and application for constructing an Android application maliciousness and malicious race detection model, which are used to solve the problem that the existing Android application maliciousness detection method cannot effectively resist the Android application code obfuscation technology

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