Android application threat degree evaluation model establishing method, evaluation method and system

Abstract

Embodiments of the present invention provide an Android application threat degree evaluation model establishing method, evaluation method and system. The model establishing method comprises: extracting feature data, including authority information and additional feature information of training samples, and constructing feature vectors according to the state of the feature data; using a clusteringalgorithm to cluster a feature vector set of the training samples according to the authority information, and dividing the feature vector set into different feature vector clusters to obtain a clustering algorithm model; using a data dimension reduction algorithm to perform feature selection on feature vectors in the feature vector clusters, and obtaining a corresponding feature selection dictionary; classifying feature vectors in the feature vector clusters by using multiple preset machine learning algorithms; obtaining a corresponding machine learning algorithm according to a classificationresult to obtain a classification algorithm model; and establishing an Android application threat degree evaluation model according to the preprocessing algorithm, the clustering algorithm model, thefeature selection dictionary and the classification algorithm model. According to the embodiments of the present invention, the classification accuracy and universality of the application threat degree evaluation are improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a method for establishing an Android application threat assessment model, an assessment method and a system. Background technique [0002] In recent years, with the development of smart terminal devices, especially smart phones, applications on smart terminals have become more and more abundant, which greatly facilitates people's lives. With the explosive growth of smartphone users, the number of malicious apps is also growing. Malicious apps have become a major threat to users' property, security, and privacy. [0003] In terms of detection of malicious applications, in the paper Quantitative Security Risk Assessment of Android Permission and Applications, the Bayesian formula is used to generate the probability P (A is malicious|pi) that a sample A under a certain permission may be malicious. The weighted sum of all Ps is the threat degree of t...

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Problems solved by technology

[0005] In order to solve the problems of low accuracy and non-universal applicability of the existing Android application threat evaluation, the embodiment of the present invention provides a method for establishing an Android application threat evaluation model, an evaluation method and a system

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