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An Android malware detection method based on depth learning

A malware and deep learning technology, applied in the field of computer and information science, which can solve the problems of low detection accuracy, difficulty in adapting to emerging software, and limited detection scope, and achieve high detection accuracy, easy adaptation, and enhanced features. learning effect

Active Publication Date: 2019-01-25
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to solve the problems of low detection accuracy, limited detection scope, and difficulty in adapting to emerging software in conventional Android malware detection methods, and propose a malware detection method based on deep learning

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  • An Android malware detection method based on depth learning
  • An Android malware detection method based on depth learning
  • An Android malware detection method based on depth learning

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Embodiment Construction

[0025] In order to better illustrate the purpose and advantages of the present invention, the implementation of the method of the present invention will be further described in detail below in conjunction with examples.

[0026] The specific process is:

[0027] Step 1, get Android positive and negative sample files, and then preprocess the files

[0028] In step 1.1, a total of 24,552 malicious Android software libraries were obtained from http: / / amd.arguslab.org / behaviors, and 21,000 normal software libraries were obtained from the Android market.

[0029] Step 1.2, for each application software, extract the AndroidManifest.xml file, res file, classes.dex file and other files of the Android application software through the Andguard tool for subsequent analysis.

[0030] Step 1.3, decompile the class.dex file through the Andguard tool, and then extract the Dalvik opcode of each Smalli file.

[0031] Step 2, feature extraction is performed on the Android application file.

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Abstract

The invention relates to an Android malware detection method based on depth learning, belonging to the field of computer and information science and technology. The invention firstly extracts featuresof Android application software, and then extracts relevant security features by decompressing and decompressing Android application files. The extracted features include three aspects: file structure feature, security experience feature and N-Gram statistic characteristic. Then the extracted features are numerically processed to construct feature vectors. Finally, a DNN (Deep Neural Network) model is constructed based on the above extracted features. The new Android software is classified and identified by the constructed model. This method combines the analysis of instruction set and has the function of anti-malware confusion. At the same time, malware detection based on depth model can enhance the feature learning, can express the abundant information of big data, and can adapt to theevolving malware more easily.

Description

technical field [0001] The invention relates to a deep learning-based Android malware detection method, which belongs to the technical field of computer and information science. Background technique [0002] With the continuous development of the mobile Internet, smart terminals have become an important part of everyone's life. Android, as the most widely used mobile operating system, has led to the proliferation of malware due to its open and flexible ecological environment. How to effectively detect Android malware is a research topic with great value. The current mainstream Android malicious code detection methods are roughly divided into static detection methods and dynamic detection methods. [0003] 1. Dynamic detection method [0004] The so-called dynamic detection and analysis refers to the method of extracting features for detection and analysis after the detected program is running. The dynamic detection method is mainly to analyze the software by running the ...

Claims

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Application Information

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IPC IPC(8): G06F21/56G06K9/62G06N3/04G06N3/08
CPCG06F21/563G06N3/04G06N3/08G06F18/24
Inventor 罗森林张寒青潘丽敏
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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