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Malicious software detection method and device based on deep learning

A malware and deep learning technology, applied in computer security devices, computer components, instruments, etc., can solve the problems of discrete and difficult processing of raw data, low detection accuracy of traditional malware, etc., and achieve the effect of improving the training process

Active Publication Date: 2020-02-07
ZHEJIANG UNIV OF TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the disadvantages of discrete and difficult processing of malware raw data and low accuracy of traditional malware detection, the present invention provides a malware image format detection method based on deep learning and its device with high precision and improved original sample processing method

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  • Malicious software detection method and device based on deep learning
  • Malicious software detection method and device based on deep learning
  • Malicious software detection method and device based on deep learning

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Embodiment Construction

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] In the first aspect, the embodiment of the present invention provides a method for detecting malware image formats based on deep learning, please refer to figure 1 , including the following steps:

[0060] 1) Obtain a malware sample data set; specifically include:

[0061] 1.1) Obtained 9 malware family sample data sets, a total of 10868 malware samples, and the data is saved in the assembly language file type with the suffix ".asm";

[0062] 1.2) Considering the difference in the number of samples of each category and for the convenience of subsequent work, the data sets of each category are divided into a proportion of about 80% of the training set and about 20% of the test set. The training set has a total of 8694 samples, and the test set A total of 2174 samples;

[0063] 2) Convert to malware image format; refer to figure 2 , including:

[0064] 2.1) ...

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Abstract

The invention discloses a malicious software image format detection method based on deep learning. The method comprises the following steps: 1) constructing a malicious software sample data set; 2) converting into a malicious software image format; 3) constructing a convolutional neural network classifier; and 4) training the classifier according to the sample data set to realize classification ofmalicious software samples. The invention further provides a device for implementing the malicious software image format detection method based on deep learning. The method has good applicability andprecision, malicious software can be effectively detected, and a good detection effect is obtained.

Description

technical field [0001] The invention belongs to the technical field of network space security, and designs a method and a device for detecting malware image formats based on deep learning. Background technique [0002] With the rapid development of computers and the Internet, communication between people has become more and more convenient, and network information exchange and intelligent applications play a vital role in people's daily life. According to statistics, as of June 2016, the number of people using the Internet in the world has reached more than 3.6 billion, accounting for more than half of users for the first time. At the same time, the development of the network is accompanied by many network security problems, and malicious software is one of the important influencing factors. Malware (Malicious Software, Malware) refers to a software program that purposely enables attackers to damage computers, servers, clients, or computer networks. Representative types of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06K9/62
CPCG06F21/563G06F2221/033G06F18/24G06F18/214
Inventor 陈晋音邹健飞袁俊坤
Owner ZHEJIANG UNIV OF TECH