A large-scale Android malware automatic detection system and method

An automatic detection and malware technology, applied in the direction of platform integrity maintenance, etc., can solve the problems of identifying malware, improving the overall learning accuracy rate that cannot be coordinated, and Android security software cannot be granted the highest authority of the system, so as to achieve accurate identification Effect

Inactive Publication Date: 2018-12-11
中共中央办公厅电子科技学院
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AI Technical Summary

Problems solved by technology

[0003] With the continuous disclosure of various system vulnerabilities, the existing Android smartphones are like a leaky ship. Even though the mobile phone security software can alleviate some security risks, the loopholes in the system have not been effectively repaired, and the Android security software cannot Has been granted the highest authority of the system, so Android system security issues have always been very difficult
Most of the proposed solutions are based on permission features to detect malware. However,...

Method used

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  • A large-scale Android malware automatic detection system and method
  • A large-scale Android malware automatic detection system and method
  • A large-scale Android malware automatic detection system and method

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Embodiment Construction

[0039] The solution of the present invention is realized through the following scheme: the user submits the Android executable file to be detected locally, and the server is responsible for analyzing the submitted sample, and then detects on the model that the system has learned, obtains the result, and returns Analyze the results to the user, and automatically classify whether the submitted software collection is malicious software.

[0040] 1. the realization process of the present invention is:

[0041] (1) Collect security software and malware libraries. Malware obtains a fixed number of samples from Google play, together with known malware data sets, to form the initial sample set of the scheme.

[0042] (2) Use software analysis tools such as Androguard and Droidbox to collect the characteristics of each software from dynamic and static perspectives to form a table of Android software characteristics.

[0043] (3) Process the receipt of the form, specify a threshold, an...

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Abstract

The invention relates to a large-scale malicious Android software automatic detection system and method based on multi-class features and machine learning. The method includes: collecting security andmalware libraries, using software analysis tools such as Androguard and Droidbox, collecting the characteristics of each software from both dynamic and static angles, processing the receipts of the forms, defining the thresholds in different angles, when a certain feature does not meet the threshold requirements, deleting the feature, and utilizing a support vector machine, a neural network, an ensemble learning algorithm and other algorithms for detection, and obtaining the optimal results of detection results are obtained, so as to realize the automatic detection of large-scale malware. Theinvention can be used for detecting and identifying large-scale malicious mobile phone software and protecting the privacy and safety of users.

Description

technical field [0001] The invention relates to the technical field of mobile terminal security, in particular to a large-scale malicious software automatic detection system and method. Background technique [0002] With the popularity of smartphones, malware is also growing rapidly. For mobile devices with the Android operating system, its open source nature makes it a prime target for malware developers. [0003] With the continuous disclosure of various system vulnerabilities, the existing Android smartphones are like a leaky ship. Even though the mobile phone security software can alleviate some security risks, the loopholes in the system have not been effectively repaired, and the Android security software cannot It is granted the highest authority of the system, so Android system security issues have always been very difficult. The traditional way of detecting whether software is malicious mainly relies on manually analyzing the code, but with the increasing number o...

Claims

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Application Information

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IPC IPC(8): G06F21/56
CPCG06F21/56
Inventor 钱榕唐瑶王志强池亚平汪永好张健毅陈颖赵绪营张艳硕马平川
Owner 中共中央办公厅电子科技学院
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