CN112257076A - Vulnerability detection method based on random detection algorithm and information aggregation - Google Patents
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Abstract
A vulnerability detection method based on a random detection algorithm and information aggregation comprises the steps of establishing an information collection; decompiling; establishing a binary training set; establishing a vulnerability detection model; judging abnormal points by the vulnerability detection model; and (5) processing the vulnerability. According to the method, through the information aggregation module, network data flow information in each module of the software is collected, screened, organized and integrated for many times to obtain a binary training set with high purity, then a vulnerability detection model is established under the action of the radial basis function neural network and the recurrent neural network, the detection capability of the model on possible security vulnerabilities in the binary software is continuously improved through machine learning, meanwhile, the random forest algorithm is adopted, the accuracy of extraction of functions and corresponding feature vectors of a binary program to be detected is improved, and the purpose of rapid and accurate vulnerability detection is achieved.
Description
Technical Field
The invention relates to the field of vulnerability detection, in particular to a vulnerability detection method based on a random detection algorithm and information aggregation.
Background
With the rapid development of computer information technology, network space security is increasingly important, because network events caused by software bugs are in a high-incidence trend, great threat is caused to network space security, and a bug analysis technology becomes a research hotspot in the field of network security.
The vulnerability detection is an important branch of vulnerability analysis technology, and mostly starts from the existing vulnerability, similar vulnerabilities with homologous relations are searched, and even undisclosed vulnerabilities are found according to vulnerability code characteristics. However, when the existing vulnerability detection method is used for feature extraction, the number of samples is large and complicated, so that the working efficiency of a vulnerability detection model is low, and the accuracy and pertinence of vulnerability detection are further influenced.
In order to solve the above problems, the present application provides a vulnerability detection method based on a random detection algorithm and information aggregation.
Disclosure of Invention
Objects of the invention
The invention provides a vulnerability detection method based on a random detection algorithm and information aggregation, which comprises the steps of collecting, screening, organizing and integrating network data stream information in each module of software for multiple times through an information aggregation module to obtain a binary training set with high purity, establishing a vulnerability detection model by matching with the action of a radial basis function neural network and a recurrent neural network, continuously improving the detection capability of the model on possible security vulnerabilities in binary software through machine learning, and simultaneously improving the accuracy of the function of a binary program to be detected and the corresponding feature vector extraction by adopting a random forest algorithm to achieve the purpose of rapid and accurate vulnerability detection.
(II) technical scheme
In order to solve the problems, the invention provides a vulnerability detection method based on a random detection algorithm and information aggregation, which comprises the following steps:
s1, establishing an information aggregation module, and acquiring, screening, organizing and integrating network data flow information in each module of the software to obtain an information collection consisting of java/C + + codes;
s2, performing decompiling by taking byte codes as the minimum unit in the information collection to obtain operation codes corresponding to the byte codes one by one;
s3, extracting the lightweight static features of the operation codes, and simultaneously, collecting, screening, organizing and integrating all extracted feature information again by the information aggregation module to establish a binary training set;
s4, extracting functions and corresponding feature vectors in the binary training set, and linking and matching the functions and the corresponding feature vectors with a random algorithm to obtain a vulnerability detection model;
s5, continuously acquiring new dynamic features and static features by the information aggregation module, and perfecting the vulnerability detection model;
s6, the vulnerability detection model carries out vulnerability detection on the binary program, the extracted function of the binary program to be detected and the corresponding characteristic vector are input into the vulnerability detection model, the vulnerability detection model is compared with the function of the binary program with known vulnerabilities and the corresponding characteristic vector, the similarity is analyzed, and abnormal points are judged;
s7, fitting the abnormal points of the binary program to be detected to T decision trees, and calculating the depth h of the leaf node of the sample on each decision treet(x) Therefore, the average height h (x) can be calculated to obtain the abnormal probability, and the calculation formula is as follows:
the expression of (c) (m) is:
and S8, sending the judgment result to the vulnerability processing module.
Preferably, a radial basis function neural network and a recurrent neural network are adopted to perfect the vulnerability detection model.
Preferably, the radial basis function neural network is provided with a first input layer, a hidden layer and a second output layer; the first input layer and the second output layer are positioned at the upper end and the lower end of the hidden layer.
Preferably, the weight between adjacent neurons is initially 1, varying with the degree of training.
Preferably, the recurrent neural network cuts the time series data in the training set to obtain a time series matrix, and then returns the time series matrix to the vulnerability detection model.
Preferably, in S3, the information aggregation module removes the operation codes in the information collection whose content repetition rate is higher than the repetition threshold, and counts the number of operation code samples.
Preferably, in S3, the statistics of the information aggregation module are purified by using the kini index.
Preferably, the information aggregation module converts the codes in the information collection into XML texts.
Preferably, each node of the XML text is taken as a minimum unit during feature information collection, and syntax, semantics and function behaviors are taken as collection targets, and the feature information is divided into a basic information feature, a binary operator feature, a code complexity feature and a path feature.
Preferably, the preprocessing module is configured to preprocess the binary program to be detected, and extract the code function and the corresponding feature vector.
Preferably, in S8, a simulation attack module is provided to simulate an attack on each detected vulnerability and verify the risk level of the vulnerability.
The technical scheme of the invention has the following beneficial technical effects:
according to the method, through the information aggregation module, network data flow information in each module of the software is collected, screened, organized and integrated for many times to obtain a binary training set with high purity, then a vulnerability detection model is established under the action of the radial basis function neural network and the recurrent neural network, the detection capability of the model on possible security vulnerabilities in the binary software is continuously improved through machine learning, meanwhile, the random forest algorithm is adopted, the accuracy of extraction of functions and corresponding feature vectors of a binary program to be detected is improved, and the purpose of rapid and accurate vulnerability detection is achieved.
Drawings
Fig. 1 is a flowchart of a vulnerability detection method based on a random detection algorithm and information aggregation according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, the vulnerability detection method based on the random detection algorithm and the information aggregation provided by the present invention includes the following steps:
s1, establishing an information aggregation module, and acquiring, screening, organizing and integrating network data flow information in each module of the software to obtain an information collection consisting of java/C + + codes;
s2, performing decompiling by taking byte codes as the minimum unit in the information collection to obtain operation codes corresponding to the byte codes one by one;
s3, extracting the lightweight static features of the operation codes, and simultaneously, collecting, screening, organizing and integrating all extracted feature information again by the information aggregation module to establish a binary training set;
s4, extracting functions and corresponding feature vectors in the binary training set, and linking and matching the functions and the corresponding feature vectors with a random algorithm to obtain a vulnerability detection model;
s5, continuously acquiring new dynamic features and static features by the information aggregation module, and perfecting the vulnerability detection model;
s6, the vulnerability detection model carries out vulnerability detection on the binary program, the extracted function of the binary program to be detected and the corresponding characteristic vector are input into the vulnerability detection model, the vulnerability detection model is compared with the function of the binary program with known vulnerabilities and the corresponding characteristic vector, the similarity is analyzed, and abnormal points are judged;
s7, fitting the abnormal points of the binary program to be detected to T decision trees, and calculating the depth h of the leaf node of the sample on each decision treet(x) Therefore, the average height h (x) can be calculated to obtain the abnormal probability, and the calculation formula is as follows:
the expression of (c) (m) is:
and S8, sending the judgment result to the vulnerability processing module.
In an optional embodiment, the vulnerability detection model is perfected by adopting a radial basis function neural network and a recurrent neural network.
In an alternative embodiment, the radial basis function neural network is provided with a first input layer, a hidden layer and a second output layer; the first input layer and the second output layer are positioned at the upper end and the lower end of the hidden layer.
In an alternative embodiment, the weight between adjacent neurons starts at 1 and varies with the degree of training.
In an optional embodiment, the recurrent neural network cuts the time series data in the training set to obtain a time series matrix, and then returns the time series matrix to the vulnerability detection model.
In an alternative embodiment, in S3, the information aggregation module removes the operation codes in the information collection whose content repetition rate is higher than the repetition threshold, and counts the number of operation code samples.
In an alternative embodiment, in S3, the statistics of the information aggregation module are purified using the kini index.
In an alternative embodiment, the information aggregation module translates the code within the collection of information into XML text.
In an optional embodiment, when feature information is collected, each node of an XML text is taken as a minimum unit, and grammar, semantics and function behaviors are taken as collection targets, and the feature information is divided into a basic information feature, a binary operator feature, a code complexity feature and a path feature.
In an optional embodiment, the preprocessing module is configured to preprocess the binary program to be detected, and extract the code function and the corresponding feature vector.
In an optional embodiment, in S8, a simulation attack module is provided to perform attack simulation on each detected vulnerability and verify the risk level of the vulnerability.
According to the method, through the information aggregation module, network data flow information in each module of the software is collected, screened, organized and integrated for many times to obtain a binary training set with high purity, then a vulnerability detection model is established under the action of the radial basis function neural network and the recurrent neural network, the detection capability of the model on possible security vulnerabilities in the binary software is continuously improved through machine learning, meanwhile, the random forest algorithm is adopted, the accuracy of extraction of functions and corresponding feature vectors of a binary program to be detected is improved, and the purpose of rapid and accurate vulnerability detection is achieved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A vulnerability detection method based on a random detection algorithm and information aggregation is characterized by comprising the following steps:
s1, establishing an information aggregation module, and acquiring, screening, organizing and integrating network data flow information in each module of the software to obtain an information collection consisting of java/C + + codes;
s2, performing decompiling by taking byte codes as the minimum unit in the information collection to obtain operation codes corresponding to the byte codes one by one;
s3, extracting the lightweight static features of the operation codes, and simultaneously, collecting, screening, organizing and integrating all extracted feature information again by the information aggregation module to establish a binary training set;
s4, extracting functions and corresponding feature vectors in the binary training set, and linking and matching the functions and the corresponding feature vectors with a random algorithm to obtain a vulnerability detection model;
s5, continuously acquiring new dynamic features and static features by the information aggregation module, and perfecting the vulnerability detection model;
s6, the vulnerability detection model carries out vulnerability detection on the binary program, the extracted function of the binary program to be detected and the corresponding characteristic vector are input into the vulnerability detection model, the vulnerability detection model is compared with the function of the binary program with known vulnerabilities and the corresponding characteristic vector, the similarity is analyzed, and abnormal points are judged;
s7, fitting the abnormal points of the binary program to be detected to T decision trees, and calculating the depth h of the leaf node of the sample on each decision treet(x) Therefore, the average height h (x) can be calculated to obtain the abnormal probability, and the calculation formula is as follows:
the expression of (c) (m) is:
and S8, sending the judgment result to the vulnerability processing module.
2. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 1, characterized in that a radial basis function neural network and a recurrent neural network are adopted to perfect a vulnerability detection model.
3. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 2, characterized in that the radial basis function neural network is provided with a first input layer, a hidden layer and a second output layer; the first input layer and the second output layer are positioned at the upper end and the lower end of the hidden layer; the weight between adjacent neurons starts at 1 and varies with the degree of training.
4. The vulnerability detection method based on the stochastic detection algorithm and the information aggregation as claimed in claim 2, characterized in that the recurrent neural network cuts the time series data in the training set to obtain a time series matrix, and then returns the time series matrix to the vulnerability detection model.
5. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 1, characterized in that in S3, the information aggregation module removes operation codes whose content repetition rate is higher than the repetition threshold in the information collection, and counts the number of operation code samples.
6. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 1, characterized in that in S3, the statistic results of the information aggregation module are purified by using a kini index.
7. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 1, characterized in that the information aggregation module converts codes in the information collection into XML text.
8. The vulnerability detection method based on random detection algorithm and information aggregation according to claim 7, characterized in that each node of XML text is taken as the minimum unit during feature information collection, and grammar, semantics and function behaviors are taken as collection targets, and the feature information is divided into basic information features, binary operator features, code complexity features and path features.
9. The vulnerability detection method based on the random detection algorithm and the information aggregation according to claim 1, characterized in that a preprocessing module is arranged to preprocess the binary program to be detected and extract the code function and the corresponding feature vector.
10. The vulnerability detection method based on the random detection algorithm and the information aggregation as claimed in claim 1, wherein in S8, a simulation attack module is provided to simulate the attack of each detected vulnerability and verify the risk level of the vulnerability.
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