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Random forest classification method based detection method for malicious application in Android platform

A technology of random forest classification and malicious application, applied in the field of mobile terminal software security, can solve the problems of low detection efficiency and poor effect, and achieve the effect of improving security and efficient detection

Inactive Publication Date: 2016-05-04
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Since it needs to be detected at runtime, the detection efficiency is relatively low and the effect is not good

Method used

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  • Random forest classification method based detection method for malicious application in Android platform
  • Random forest classification method based detection method for malicious application in Android platform
  • Random forest classification method based detection method for malicious application in Android platform

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Embodiment Construction

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the implementation methods and accompanying drawings.

[0020] In order to realize the difficult problem of accurately batch analyzing whether APPs are malicious or benign, the present invention provides a method for detecting malicious applications on the Android platform based on a random forest classification method. see figure 1 , this method mainly includes the following five steps:

[0021] S1: Obtain APP samples, including malicious and benign APP samples;

[0022] S2: Obtain the list of all applicable permissions and API information of the APP, and obtain the permission set and API set;

[0023] S3: extract the static features of the APP sample, including the requested permissions and the called API;

[0024] S4: Construct a sample library based on the static features, permission ...

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Abstract

The invention discloses a random forest classification method based detection method for a malicious application in an Android platform. The method comprises the following steps: obtaining APP samples, wherein the APP samples include malicious and good APP samples; obtaining all applicable permission lists and API information of APPs to obtain a permission set and an API set; extracting static features of the APP samples, wherein the static features include applied permissions and called APIs; based on the static features of the APP samples, the permission set and the API set, constructing a sample library, wherein the sample library comprises table items of APP identifiers, type identifiers for distinguishing the malicious and good APP samples, application identifiers for the permissions in the permission set, and calling identifiers for the APIs in the API set; based on the sample library, constructing decision trees of a random forest to obtain a random forest classifier; and based on the random forest classifier, detecting to-be-detected APPs. By implementing the detection method, the malicious APPs can be efficiently detected and the security of the Android platform can be improved.

Description

technical field [0001] The invention relates to the technical field of mobile terminal software security, in particular to a method for applying a classification algorithm in the field of machine learning to Android malicious application detection. Background technique [0002] In recent years, with the development of intelligent terminal equipment, especially smart phones, people's life has become more and more convenient. Today's smartphones can even perform many functions that previously had to be done on a PC, which is even more attractive for people to use. However, the growing population of smartphone users has also attracted the attention of many malicious application developers. As the smartphone user base has grown, so has the number of malicious apps. Malicious applications have become a major threat to mobile phone security and user privacy. Under such circumstances, it is very necessary to find a method that can accurately detect malicious applications in batc...

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Application Information

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IPC IPC(8): G06F21/56
CPCG06F21/562
Inventor 桂盛霖杨漫游王沐李多航
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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