A malware identification method and system based on a convolution neural network
A convolutional neural network and malware technology, applied in the field of malware identification methods and systems based on convolutional neural networks, can solve the problems of a large number of new applications, difficulty in detecting applications, and inability to manually check malicious behaviors, etc. Simple operation, uniform input data, and improved accuracy
Active Publication Date: 2018-12-14
东北大学秦皇岛分校
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Problems solved by technology
The number of new applications is too large to manually check each program for malicious behavior
Malware detection is traditionally based on manual detection of known malware behaviors or codes to manually design malware signatures, a process that makes it difficult to detect a large number of applications
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[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0058] The purpose of the present invention is to provide a malware identification method and system based on convolutional neural network, which has the characteristics of high identification accuracy and easy operation.
[0059] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embod...
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The invention discloses a malware identification method and system based on a convolution neural network. The method comprises the following steps: obtaining the operation code and the authority information of the sample software; converting an opcode to a decimal number; preprocessing the converted operation code; mixing the preprocessed operation code and the permission information; inputting the mixed data into the convolution neural network as characteristic matrix, and training the convolution neural network; judging whether the accuracy of the malicious probability or the non-malicious probability output by the convolution neural network reaches the set value; if so, stopping the training of convolution neural network, and using the trained convolution neural network to identify therecognition software; if not, adjusting the weight parameters in the training process according to the accuracy of malicious probability or non-malicious probability output by the convolutional neuralnetwork, and continuously training the convolutional neural network. The malicious software identification method and system based on the convolution neural network provided by the invention have thecharacteristics of high identification accuracy and simple operation.
Description
technical field [0001] The invention relates to the field of malicious software detection, in particular to a method and system for identifying malicious software based on a convolutional neural network. Background technique [0002] With the development of science and technology, the types and complexity of malware are getting higher and higher, and the identification of malware is becoming more and more difficult, especially in the mobile field platform. Given the rapid growth of mobile devices and mobile app stores. The number of new applications is too large to manually check each program for malicious behavior. Malware detection has traditionally been based on manual detection of known malware behaviors or codes to manually design malware signatures, a process that makes it difficult to detect a large number of applications. And this signature-based static malware detection means that new malware can be engineered to evade existing signatures. Contents of the invent...
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CPCG06F21/562G06F2221/033
Inventor 赵立超史闻博李丹黄涛
Owner 东北大学秦皇岛分校