Android malicious act detection method based on Bayesian network

A technology of Bayesian network and detection method, which is applied in the field of Android malicious behavior detection, and can solve problems such as user threats

Active Publication Date: 2016-07-06
HARBIN ENG UNIV
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AI Technical Summary

Problems solved by technology

[0009] Due to the current security situation of the Android platform and the increasing number of viruses, which have brought serious threats to users, it is necessary to develop a safe and efficient detection method

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  • Android malicious act detection method based on Bayesian network
  • Android malicious act detection method based on Bayesian network
  • Android malicious act detection method based on Bayesian network

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with accompanying drawing

[0042] The invention uses a static analysis method to detect the virus program on the Android platform. The static analysis method mainly obtains the static behavior characteristics of the APK through decompilation technology, and does not actually run the application program. The present invention extracts the characteristics of Android software training samples through static analysis, and then uses data mining technology to dig out the potential relationship between various information, and automatically discovers the laws existing in the sample data by analyzing the sample data, and utilizes the learned predict the unknown data. Machine learning is an important branch of data mining. The purpose of machine learning is to build a model with a better virus detection program based on existing sample data. The purpose of the present invention is to establish a goo...

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Abstract

The invention discloses an Android malicious act detection method based on the Bayesian network.The method specifically comprises the steps of 1, conducting static feature extraction on an Android application training sample; 2, conducting feature processing, and calculating the correlation degree between feature and category with the chi-squared statistic feature selection approach; 3, establishing an Android software malicious act detection model based on the Bayesian network classification algorithm; 4, inputting an APK extraction feature to be detected into a well trained Bayesian network virus detection model, and calculating the posterior probability of the category of the feature; 5, comparing the two data obtained from the step 4 representing the posterior probability that the APK extraction feature to be detected belongs to the virus category and representing the posterior probability that the APK extraction feature to be detected belongs to the normal category respectively, and taking the category with larger posterior probability as the classification result of the application.The method can effectively detect Android malice applications and reduce the learning time of the Bayesian network to a certain degree.

Description

technical field [0001] The invention belongs to the field of Android malicious behavior detection, in particular to a Bayesian network-based Android malicious behavior detection method. Background technique [0002] In recent years, mobile terminal technology has developed very rapidly, and its functions have become more and more powerful. Mobile smart terminal devices have played an increasingly important role in people's lives, and the number of various mobile smart terminal devices has also shown explosive growth. The smart operating systems mainly used in mobile terminals now include Android, IOS, WindowsPhone, and the like. Since Android is an open source system, the Android system has quickly become the pre-installed system of major mobile phone manufacturers since its launch. But at the same time, the number of malicious software in the Android system is also showing a rapid growth trend, among which malicious deduction, privacy theft, and resource consumption are th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56
CPCG06F21/562
Inventor 张国印曲家兴王玲李晓光夏松竹
Owner HARBIN ENG UNIV
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