A mobile application malware detection method and system for a power enterprise

A malicious software and mobile application technology, applied in data processing applications, computer components, electrical digital data processing, etc., can solve the problems that support vector machines are not suitable for classification learning, and achieve the effect of reducing learning time and improving learning efficiency

Pending Publication Date: 2019-04-26
GLOBAL ENERGY INTERCONNECTION RES INST CO LTD +2
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to its own bottleneck problem, the support vector machine is not suitable for the classification learning of a large number of samples.

Method used

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  • A mobile application malware detection method and system for a power enterprise
  • A mobile application malware detection method and system for a power enterprise
  • A mobile application malware detection method and system for a power enterprise

Examples

Experimental program
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Effect test

Embodiment 1

[0071] The invention provides a mobile application malicious software detection method for electric power enterprises. The detection method firstly decompiles the APP sample to obtain the source code of the application, and then uses the static scanning method to extract the feature vectors of the normal application and the malicious application, and constructs the normal and malicious feature sample library. Finally, the machine learning algorithm support vector machine is used to train and learn the feature library, and the SVM classification model for identifying malicious applications is obtained. If there are new samples to be trained, the incremental learning method can be used to quickly learn the features of the new samples without re-learning all the sample features, and finally obtain the iteratively updated SVM classification model.

[0072] The flow chart of mobile application malware detection in electric power enterprises is attached figure 1 As shown, it mainly...

Embodiment 2

[0113] A mobile application malware detection system for electric power enterprises, including:

[0114] Obtaining module: used to obtain the software to be tested, and decompile the software to be tested to obtain the source code of the software to be tested;

[0115] Determining module: used to extract the feature vector of the source code, and input the feature vector of the source code to a pre-built SVM classification model for comparison, and determine whether the software to be detected is malicious software;

[0116] The SVM classification model includes: an SVM classifier, and the SVM classifier is iteratively updated based on a double weight increment method.

[0117] The determination module includes: a model building submodule, a model updating submodule and a determination submodule:

[0118] The model building module is used for: performing feature extraction and constructing an SVM classifier based on a large amount of normal application software and malicious ...

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PUM

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Abstract

The mobile application malicious software detection method for the power enterprise is characterized by comprising the steps that to-be-detected software is acquired, and decompilation is conducted onthe to-be-detected software to obtain a source code of the to-be-detected software; Extracting a feature vector of the source code, inputting the feature vector of the source code into a pre-constructed SVM classification model for comparison, and determining whether the to-be-detected software is malicious software or not; Wherein the SVM classification model comprises an SVM classifier, and theSVM classifier performs iterative updating based on a double weight increment method. According to the technical scheme, the problem that a support vector machine is not suitable for classified learning of a large number of samples is solved, the SVM incremental learning algorithm based on the double-weight function is provided for learning and classifying the application samples, and the methodcan reduce the learning time to the maximum extent and improve the learning efficiency on the premise that it is guaranteed that the application classification precision is not reduced.

Description

technical field [0001] The invention relates to the fields of electric power information security and mobile Internet security, in particular to a mobile application malware detection method and system for electric power enterprises. Background technique [0002] In recent years, with the continuous improvement of the application level of information technology in the electric power industry and the rapid development of business, smart grid construction, lean management and customer service improvement have put forward increasingly urgent requirements for the security of mobile applications. Important content of information and communication construction. However, the current internal and external security situation in the electric power industry is severe. Various types of network attack technologies continue to evolve, and various incidents occur from time to time. serious challenge. In addition, traditional viruses, Trojan horses and malware and other attack technologie...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06F8/53G06K9/62G06Q50/06
CPCG06F8/53G06F21/563G06Q50/06G06F2221/033G06F18/2411
Inventor 李勇马媛媛张涛陈牧戴造建邵志鹏石聪聪陈璐李尼格席泽生
Owner GLOBAL ENERGY INTERCONNECTION RES INST CO LTD
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