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Method and system to classify Android malicious applications based on behavior characteristics

A malicious application and classification method technology, applied in the Android malicious application classification method and system field, can solve the problems of low feasibility, serious time consumption, and increased dynamic analysis time consumption, so as to reduce time and efficiency and improve accuracy.

Active Publication Date: 2018-05-29
SOUTH CHINA NORMAL UNIVERSITY +1
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AI Technical Summary

Problems solved by technology

Taking Anubis as an example, it can ensure program coverage in a coarse-grained manner, but the time consumption is very serious
For a large number of APP-targeted demands, the feasibility is relatively low
At the same time, with the growth of malicious types and the increase of checkpoints, the consumption of dynamic analysis time will also be increased

Method used

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  • Method and system to classify Android malicious applications based on behavior characteristics
  • Method and system to classify Android malicious applications based on behavior characteristics
  • Method and system to classify Android malicious applications based on behavior characteristics

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Embodiment 1

[0097] Aiming at the problem that the existing technology cannot minimize the classification time while ensuring the correct rate of classification of malicious applications, the present invention proposes a new method and system for classifying Android malicious applications based on behavioral characteristics, which ensures correct classification of malicious applications In the case of high rate, the time consumption of malicious application detection is greatly reduced.

[0098] The entire Android malicious application detection or classification process of the present invention will be described in detail below from the two aspects of nomenclature explanation and specific implementation process.

[0099] (1) Name Explanation:

[0100] The proper nouns that the present invention relates to are as follows:

[0101] SVM (Support Vector Machine): support vector. In the field of machine learning, support vector machine (SVM) is a supervised learning model, usually used for p...

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Abstract

The invention discloses a method and system to classify Android malicious applications based on behavior characteristics. The method comprises: decompiling Android application samples that are input to obtain decompiled source files; subjecting the decompiled source files to syntactic analysis and characteristic extraction to obtain static characteristics and behavior characteristics of Android malicious applications; using a support vector machine classifier to classify Android applications to be classified according to the acquired static characteristics and behavior characteristics, therebydividing the Android applications to be classified into malicious Android applications or non-malicious Android applications. The system comprises a decompiling module, a syntactic analysis and characteristic extraction module and a classifying module. Behavior characteristics that mean something are introduced, and malicious Android applications are classified through the comprehensive application of the static analysis method, the syntactic analysis method and the support vector machine classifying method; shorter classifying time and improved efficiency are provided for malicious Android applications, and classifying accuracy is improved for malicious Android applications. The method and system are widely applicable to the field of data mining.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method and system for classifying Android malicious applications based on behavioral characteristics. Background technique [0002] In recent years, with the rapid development of mobile hardware devices and software, mobile phone platforms have rapidly become popular in people's lives. The rapid development of the mobile Internet has led to a sharp increase in the number of smartphones, and the number of mobile phone users, Android application levels, and market size have all shown explosive growth trends. The rapid growth of mobile Internet users such as mobile phones and ipads has promoted market development. According to the statistics of iResearch, an authoritative third-party consulting platform in China, in 2012, the total number of Internet users in China was 560 million, the number of Internet users on mobile terminals was 420 million, and the penetration rate of Internet us...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06F8/53G06K9/62
CPCG06F8/53G06F21/563G06F2221/033G06F18/2411
Inventor 赵淦森陈梓豪梁轼文刘全凤朱健飞吴杰超任雪琦王欣明张奇支杨晋吉叶卫东温赞亮黄永聪
Owner SOUTH CHINA NORMAL UNIVERSITY
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