CN111552964A - Malicious software classification method based on static analysis - Google Patents
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Abstract
The invention belongs to the technical field of computer security, and particularly relates to a malicious software classification method based on static analysis. The invention relates to a method for classifying malicious software samples, which comprises the steps of converting malicious software into binary files, generating gray level images, training the gray level images by adopting a convolutional neural network model with a spatial pyramid pooling layer to obtain a static classifier, and classifying the malicious software samples by the static classifier to belong families. The invention can classify the malicious software by taking the gray level image as the characteristic, thereby effectively reducing the information loss caused by the image preprocessing stage. According to the method, the malicious software is classified by analyzing the outline characteristics of the malicious software, so that the professional can be helped to reduce the cost for identifying the malicious software.
Description
Technical Field
The invention belongs to the technical field of computer security, and particularly relates to a malicious software classification method based on static analysis.
Background
Along with the rapid development of the internet industry, the dependence of people on various kinds of software is enhanced, which brings great convenience to attack and spread of malicious software. Because of the endless proliferation of various automation tools, malware is discovered by people much less rapidly than is derived on the internet. For example, 15,714,700 malicious objects were detected by the 2017 Kaspersky laboratory. In quarter 1 of 2018 the McAfee laboratory detected 790 ten thousand malicious files per day, an increase of 450 million over quarter 4 of 2017. Although malware is being derived more and more rapidly, the vast majority of malware has evolved through the polymorphism and deformation of known malware. Therefore, the homologous relationship in the sample is found to have very important functions on attack tissue tracing, operation environment restoration and attack prevention.
Due to the continuous improvement of the technology of the malicious software and the wider and wider use population of the application software, the spread range of the malicious software is continuously increased in the process of executing certain operations, but most of the malicious software is evolved from the known malicious software. While considerable research has been done by the relevant personnel today, malware continues to flood. The dynamic analysis method has high accuracy but poor efficiency, and generates excessive classification cost in the analysis process. Compared with a dynamic analysis method, the static analysis method has higher classification accuracy and better efficiency than the dynamic analysis method. Therefore, it is a very important topic to research a malware classification method based on static analysis. The method has very important scientific theoretical value and practical application significance in researching the malicious software classification technology with wide application range and strong practicability to improve the safety of the computer system.
Disclosure of Invention
The invention aims to provide a malware classification method based on static analysis, which classifies malware by analyzing outline characteristics of the malware and helps professionals to reduce the cost of identifying the malware.
The purpose of the invention is realized by the following technical scheme: the method comprises the following steps:
step 1: inputting a software data set to be classified, and dividing the software data set to be classified into a training set and a testing set;
step 2: converting the software sample in the training set into a binary file, wherein the conversion method specifically comprises the following steps: the Exe of the Windows executable file to be analyzed is converted into a binary stream file in a bytes format;
and step 3: partitioning the binary file with every 8-bit byte and converting every 8-bit byte into a gray value, the conversion scheme mapping byte values from 0 to 255, where 0 represents black and 255 represents white; secondly, converting the gray values into a two-dimensional gray matrix in a sequential arrangement mode, and determining the width and the height of the two-dimensional gray matrix according to the size of a file so as to visualize the two-dimensional gray matrix into a gray image;
and 4, step 4: training a convolutional neural network model by using the generated gray level image to generate a static classifier; the convolutional neural network model comprises an input layer, a convolutional layer, a maximum pooling layer, a spatial pyramid pooling layer and an output layer; processing the gray scale image using a small window convolution filter; convolution layers all use a convolution kernel of 3 × 3, and the step size is set to 1; performing 1-pixel edge filling on the input feature map in the convolutional layer; using a 2 multiplied by 2 sliding window when the pooling is maximum, and setting the step length to be 2; the last pooling layer adopts 3 layers of space pyramid pooling, and features of any dimension are input and then uniformly output; the convolutional neural network uses dropout with probability of 0.5 after each pooling layer to prevent the overfitting phenomenon; initializing and batch normalizing by using a Leaky ReLU activation function and uniformly distributed weights;
and 5: and inputting the test set of the software data set to be classified into a static classifier, judging the family to which the malicious software belongs according to the classification result of the classifier, and finishing classification of the malicious software.
The invention has the beneficial effects that:
the invention takes the gray level image as the characteristic and uses the convolution neural network with the spatial pyramid pooling layer for classification, thereby effectively reducing the information loss caused in the image preprocessing stage. The method has the advantages of lower time cost, high detection speed and high detection efficiency in the field of malicious software detection.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flowchart of the method for generating a malware grayscale image according to the present invention.
FIG. 3 is a diagram of a convolutional neural network structure in the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention provides a malicious software classification system based on static analysis, and belongs to the field of computer security. The invention relates to a method for classifying malicious software samples, which comprises the steps of converting malicious software into binary files, generating gray level images, training the gray level images by adopting a convolutional neural network model with a spatial pyramid pooling layer to obtain a static classifier, and classifying the malicious software samples by the static classifier to belong families. The invention can classify the malicious software by taking the gray level image as the characteristic, thereby effectively reducing the information loss caused by the image preprocessing stage. The invention aims to classify the malicious software by analyzing the outline characteristics of the malicious software and help professionals to reduce the cost of identifying the malicious software.
(1) The classification system converts the malicious software sample into a binary file for processing in a static analysis mode.
(2) The classification system divides binary files by taking each 8 bytes as a block, converts a string of gray value streams into gray values of 0-255 and stores the gray values in a one-dimensional gray value array, converts one-dimensional vectors into a two-dimensional gray value matrix, and then generates gray level images.
(3) The classification system trains a convolutional neural network model to generate a static classifier using the generated grayscale image. The convolutional neural network model comprises an input layer, a convolutional layer, a maximum pooling layer, a spatial pyramid pooling layer and an output layer.
(4) And the classification system inputs the malware samples in the test set into a static classifier, and judges the family to which the malware belongs according to the classification result of the classifier.
Converting the malware into a binary file in the classification system further comprises:
preprocessing the sample data of the malicious software, and converting the Windows executable file to be analyzed to the binary stream file in the bytes format.
The generating of the gray scale image in the classification system further comprises:
the classification system treats malware as a binary file, partitions the binary file every 8-bit byte, and converts every 8-bit byte to a gray value, the conversion scheme mapping byte values from 0 (black) to 255 (white). And then converting the gray values into a two-dimensional gray matrix in a sequential arrangement mode, and determining the width and the height of the two-dimensional gray matrix according to the size of the malicious code file so as to visualize the two-dimensional gray matrix into a gray image.
The convolutional neural network involved in the classification system further comprises:
the classification system processes the gray scale image through a convolutional neural network using a small window convolution filter. The convolutional layers all use a 3 x 3 convolutional kernel with the step size set to 1. The input feature map is then edge-filled for 1 pixel in the convolutional layer. A 2 x 2 sliding window is used for maximum pooling, with the step size set to 2. And the last pooling layer adopts 3 layers of space pyramid pooling, and the features of any dimension are input and then uniformly output.
The convolutional neural network optimization further comprises:
the convolutional neural network uses dropout with a probability of 0.5 after each pooling layer to prevent the overfitting phenomenon. Then initialized with the Leaky ReLU activation function, uniformly distributed weights and batch normalization.
The static classifier classification involved in the classification system further comprises:
and classifying each malicious software sample by the convolutional neural network model, namely obtaining a classification result of the static classifier.
The invention provides a malware classification system based on static analysis, which can classify malware by taking a gray image as a feature. The invention aims to classify the malicious software by analyzing the outline characteristics of the malicious software and help professionals to reduce the cost of identifying the malicious software.
Compared with the prior art, the invention has the advantages that:
1. the invention provides a malicious software classification system based on static analysis, which takes a gray level image as a feature and uses a convolutional neural network with a spatial pyramid pooling layer for classification, thereby effectively reducing information loss caused in an image preprocessing stage.
2. The invention provides a malicious software classification system based on static analysis, which has lower time cost in the field of malicious software detection.
3. The invention provides a malicious software classification system based on static analysis, which has the advantages of high detection speed and high detection efficiency.
Fig. 1 is a flowchart of a malware classification system based on static analysis according to the present invention. The present invention includes the following four aspects.
(1) The classification system converts the malicious software sample into a binary file for processing in a static analysis mode.
Preprocessing the sample data of the malicious software, and converting the Windows executable file to be analyzed to the binary stream file in the bytes format.
(2) The classification system divides binary files by taking each 8 bytes as a block, converts a string of gray value streams into gray values of 0-255 and stores the gray values in a one-dimensional gray value array, converts the one-dimensional vectors into a two-dimensional gray value matrix, and then generates a gray image.
In conjunction with the grayscale chart generation flow diagram of FIG. 2, the classification system treats malware as a binary file, partitions the binary file by every 8-bit byte, and converts every 8-bit byte to a grayscale value, with the transformation scheme mapping byte values from 0 (black) to 255 (white). And then converting the gray values into a two-dimensional gray matrix in a sequential arrangement mode, and determining the width and the height of the two-dimensional gray matrix according to the size of the malicious code file so as to visualize the two-dimensional gray matrix into a gray image.
(3) The classification system trains a convolutional neural network model to generate a static classifier using the generated grayscale image. The convolutional neural network model comprises an input layer, a convolutional layer, a maximum pooling layer, a spatial pyramid pooling layer and an output layer.
In connection with fig. 3, the classification system processes the gray scale image through a convolutional neural network using a small window convolution filter. The convolutional layers all use a 3 x 3 convolutional kernel with the step size set to 1. The input feature map is then edge-filled for 1 pixel in the convolutional layer. A 2 x 2 sliding window is used for maximum pooling, with the step size set to 2. And the last pooling layer adopts 3 layers of space pyramid pooling, and the features of any dimension are input and then uniformly output.
(4) And the classification system inputs the malware samples in the test set into a static classifier, and judges the family to which the malware belongs according to the classification result of the classifier.
And classifying each malicious software sample by the convolutional neural network model, namely obtaining a classification result of the static classifier.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A malware classification method based on static analysis is characterized by comprising the following steps:
step 1: inputting a software data set to be classified, and dividing the software data set to be classified into a training set and a testing set;
step 2: converting the software sample in the training set into a binary file, wherein the conversion method specifically comprises the following steps: the Exe of the Windows executable file to be analyzed is converted into a binary stream file in a bytes format;
and step 3: partitioning the binary file with every 8-bit byte and converting every 8-bit byte into a gray value, the conversion scheme mapping byte values from 0 to 255, where 0 represents black and 255 represents white; secondly, converting the gray values into a two-dimensional gray matrix in a sequential arrangement mode, and determining the width and the height of the two-dimensional gray matrix according to the size of a file so as to visualize the two-dimensional gray matrix into a gray image;
and 4, step 4: training a convolutional neural network model by using the generated gray level image to generate a static classifier; the convolutional neural network model comprises an input layer, a convolutional layer, a maximum pooling layer, a spatial pyramid pooling layer and an output layer; processing the gray scale image using a small window convolution filter; convolution layers all use a convolution kernel of 3 × 3, and the step size is set to 1; performing 1-pixel edge filling on the input feature map in the convolutional layer; using a 2 multiplied by 2 sliding window when the pooling is maximum, and setting the step length to be 2; the last pooling layer adopts 3 layers of space pyramid pooling, and features of any dimension are input and then uniformly output; the convolutional neural network uses dropout with probability of 0.5 after each pooling layer to prevent the overfitting phenomenon; initializing and batch normalizing by using a Leaky ReLU activation function and uniformly distributed weights;
and 5: and inputting the test set of the software data set to be classified into a static classifier, judging the family to which the malicious software belongs according to the classification result of the classifier, and finishing classification of the malicious software.
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Cited By (9)
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| CN112347478A (en) * | 2020-10-13 | 2021-02-09 | 北京天融信网络安全技术有限公司 | Malicious software detection method and device |
| CN112884061A (en) * | 2021-03-10 | 2021-06-01 | 河北师范大学 | Malicious software family classification method based on parameter optimization meta-learning |
| CN113282925A (en) * | 2021-03-30 | 2021-08-20 | 深圳融安网络科技有限公司 | Malicious file detection method and device, terminal equipment and storage medium |
| CN113538288A (en) * | 2021-07-29 | 2021-10-22 | 中移（杭州）信息技术有限公司 | Network anomaly detection method and device and computer readable storage medium |
| CN114611102A (en) * | 2022-02-23 | 2022-06-10 | 西安电子科技大学 | Visual malicious software detection and classification method and system, storage medium and terminal |
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| CN116861431A (en) * | 2023-09-05 | 2023-10-10 | 国网山东省电力公司信息通信公司 | Malicious software classification method and system based on multichannel image and neural network |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112347478A (en) * | 2020-10-13 | 2021-02-09 | 北京天融信网络安全技术有限公司 | Malicious software detection method and device |
| CN112884061A (en) * | 2021-03-10 | 2021-06-01 | 河北师范大学 | Malicious software family classification method based on parameter optimization meta-learning |
| CN113282925B (en) * | 2021-03-30 | 2023-09-05 | 深圳融安网络科技有限公司 | Malicious file detection method, malicious file detection device, terminal equipment and storage medium |
| CN113282925A (en) * | 2021-03-30 | 2021-08-20 | 深圳融安网络科技有限公司 | Malicious file detection method and device, terminal equipment and storage medium |
| CN113538288A (en) * | 2021-07-29 | 2021-10-22 | 中移（杭州）信息技术有限公司 | Network anomaly detection method and device and computer readable storage medium |
| CN114611102A (en) * | 2022-02-23 | 2022-06-10 | 西安电子科技大学 | Visual malicious software detection and classification method and system, storage medium and terminal |
| CN114741697A (en) * | 2022-04-22 | 2022-07-12 | 中国电信股份有限公司 | Malicious code classification method and device, electronic equipment and medium |
| CN114741697B (en) * | 2022-04-22 | 2023-10-13 | 中国电信股份有限公司 | Malicious code classification method and device, electronic equipment and medium |
| CN114926680A (en) * | 2022-05-13 | 2022-08-19 | 山东省计算中心（国家超级计算济南中心） | Malicious software classification method and system based on AlexNet network model |
| CN114926680B (en) * | 2022-05-13 | 2022-11-11 | 山东省计算中心（国家超级计算济南中心） | Malicious software classification method and system based on AlexNet network model |
| CN114913384A (en) * | 2022-06-24 | 2022-08-16 | 河北科技大学 | Target application classification method and device and electronic equipment |
| CN116861431A (en) * | 2023-09-05 | 2023-10-10 | 国网山东省电力公司信息通信公司 | Malicious software classification method and system based on multichannel image and neural network |
| CN116861431B (en) * | 2023-09-05 | 2023-11-21 | 国网山东省电力公司信息通信公司 | Malicious software classification method and system based on multichannel image and neural network |
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