CN116933016A - Automatic driving information safety test method and system based on vehicle-road cooperation - Google Patents
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Abstract
The invention discloses an automatic driving information safety test method and system based on vehicle-road cooperation, comprising the steps of simulating an attack scene, obtaining communication data, preprocessing the communication data, extracting communication characteristics of the communication data, screening according to the correlation degree of the communication characteristics to obtain abnormal characteristics, classifying the communication data according to the abnormal characteristics to obtain abnormal classification data, carrying out abnormal recognition on the abnormal classification data to obtain abnormal data, constructing an information safety test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information safety test model, and outputting an information safety test result. The method not only can improve the precision of the automatic driving information safety test method, but also has better interpretability, and can be directly applied to an automatic driving information safety test system based on vehicle-road cooperation.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to an automatic driving information safety test method and system based on vehicle-road cooperation.
Background
The information security test technology is widely applied in the field of automatic driving, and can help managers of the automatic driving system with the cooperation of the vehicle and the road to timely and efficiently acquire the result of the automatic driving information security test with the cooperation of the vehicle and the road, thereby realizing intelligent acquisition of the automatic driving information security test with the cooperation of the vehicle and the road. At present, the automatic driving information safety test has the characteristics of huge user information quantity, various data types, high information density and the like, and the information safety test method has more uncertain factors, so that the automatic driving information safety test method for vehicle-road coordination has larger uncertainty. Although some automatic driving information safety testing methods and systems based on vehicle-road coordination have been invented, the problem of uncertainty of the automatic driving information safety testing methods based on vehicle-road coordination cannot be effectively solved.
Disclosure of Invention
The invention aims to provide an automatic driving information safety test method and system based on vehicle-road cooperation.
In order to achieve the above purpose, the invention is implemented according to the following technical scheme:
the invention comprises the following steps:
a, simulating an attack scene, acquiring communication data, and preprocessing the communication data;
b, extracting communication characteristics of the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
classifying the communication data according to the abnormal characteristics to obtain abnormal classification data, and performing abnormal recognition on the abnormal classification data to obtain abnormal data;
and D, constructing an information security test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information security test model, and outputting an information security test result.
Further, the method of preprocessing in step a includes removing duplicate data, deleting missing values and outliers, data conversion and data reduction.
Further, the method for extracting the communication characteristics of the communication data comprises the following steps:
the dimensionality of the initial spatial communication data is randomly planned, an initial dimensionality is provided for data conversion and processing, the communication data is converted into a training test data set, and a covariance matrix of the subspace communication data is calculated:
wherein the mean vector of all training test data sets in the subspace isThe ith communication data is +.>The transpose of the difference between the communication data and the mean vector is +.>The number of the communication data is n;
performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, arranging the eigenvectors in descending order according to the corresponding eigenvalues, selecting the eigenvectors to form a component analysis conversion matrix, and performing principal component analysis conversion on communication data:
wherein the test data after conversion is Q, the principal component analysis conversion matrix is D, and the transpose of the principal component analysis conversion matrix isAccording to the magnitude of the characteristic value, selecting the characteristic vector as a main component, and projecting communication data onto the selected main component to obtain the initial characteristic of the communication data;
extracting trend features and mean features according to the low-frequency signals after wavelet decomposition of the initial features:
wherein the low frequency signal isTrend is characterized by->Mean value specialThe sign is->The mean value of the low frequency signal is +.>And (3) setting the time as E and the time length as E, and establishing a regression model:
wherein the scale parameter isMinimum error is->Wavelet variance is calculated:
wherein the domain is b and the wavelet coefficient isExtracting high-frequency signal characteristics according to wavelet coefficients:
wherein the high frequency signal of the ith layer at time e isThe mean value of the high-frequency signal of the i-th layer is +.>High-frequency signal of the i-th layer +.>Characterized by->Outputting the high-frequency signal characteristics as onAnd (5) information characteristics.
Further, the method for classifying the communication data according to the abnormal characteristics to obtain abnormal classification data comprises the following steps:
inputting the abnormal features into a classification algorithm, and calculating the distance between the features and the hyperplane:
wherein the ith anomaly featureDistance to the hyperplane is +.>The hyperplane is A, B, C, vector coordinates of the anomaly feature +.>Slope m, selecting a kernel function:
wherein the ith anomaly is characterized byThe j-th abnormality is +>The mapping function of the abnormal feature is +.>The transpose of the mapping function is +.>High-dimensional mapping is performed by using a kernel function, and the maximum interval is calculated:
wherein the x-axis position of the jth anomaly isThe j-axis position of the j-th abnormal feature is +.>The number of the abnormal features is r, a hyperplane with the maximum interval is obtained according to the maximum interval, and communication data are selected according to the hyperplane to be classified, so that abnormal classification data are obtained.
Further, the method for obtaining the abnormal data by carrying out the abnormal recognition on the abnormal classification data comprises the following steps:
calculating the distance of the abnormal classification data:
wherein the data is p, the c-th data isThe number of data is s, the c-th data nearest to data p is +.>The data closest to data p is +.>The k distance of the data p is +.>Calculating the data reachable distance:
wherein data p andthe distance of the data o isCalculating the local reachable density of the data p:
wherein the set of all data of the abnormality classification data having a distance from the point p of not more than the k distance of the point p isCalculating local outlier factors of the data:
wherein the local reachable density of data o isJudging whether the data is abnormal or not according to the abnormal proportion threshold value and the local outlier factor, correlating the abnormal characteristics of the corresponding abnormal classified data with the classified data after the abnormal recognition, and outputting the abnormal data.
Further, the method for constructing the information security test model according to the abnormal characteristics and the abnormal classification data comprises a cyclic neural network algorithm, wherein the abnormal classification data is divided into a test set and a training set according to a ratio of 4:1, the test set is input into the information security test model for training, the accuracy and the efficiency are higher than 0.9 and are used as optimization targets, the attention mechanism is increased, the information security test model is optimized, and the training set is input into a trained cyclic neural network algorithm to predict the information security test result.
In a second aspect, an automatic driving information safety test system based on vehicle-road cooperation includes:
and a data acquisition module: the method comprises the steps of simulating an attack scene, acquiring communication data and preprocessing the communication data;
and the feature extraction module is used for: for performing a communication feature extraction on the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
an anomaly identification module: the communication data are classified according to the abnormal characteristics to obtain abnormal classification data, and the abnormal classification data are subjected to abnormal recognition to obtain abnormal data;
and a result output module: and the information safety test module is used for constructing an information safety test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information safety test model and outputting an information safety test result.
The beneficial effects of the invention are as follows:
compared with the prior art, the invention has the following technical effects:
the method can improve the accuracy of the information safety test through the steps of preprocessing, abnormal feature extraction, abnormal data identification and prediction result, thereby realizing the information safety test, the resource and labor cost can be greatly saved, the working efficiency can be improved, the information safety test can be realized for the automatic driving of the vehicle-road cooperation, the information safety test is carried out for the automatic driving of the vehicle-road cooperation in real time, the method has important significance for the information safety test of the automatic driving of the vehicle-road cooperation, and the method can adapt to the information safety test requirements of the automatic driving information safety test systems of different vehicle-road cooperation and the automatic driving information safety test systems of different users based on the vehicle-road cooperation, and has certain universality.
Drawings
Fig. 1 is a flow chart of steps of an automatic driving information safety test method based on vehicle-road cooperation.
Detailed Description
The invention is further described by the following specific examples, which are presented to illustrate, but not to limit, the invention.
The invention discloses an automatic driving information safety test method based on vehicle-road cooperation, which comprises the following steps:
as shown in fig. 1, in this embodiment, the steps include:
a, simulating an attack scene, acquiring communication data, and preprocessing the communication data;
in actual evaluation, taking a certain middle-end automobile as an example, an information security test environment is built, and fake vehicle control instructions, fake road side equipment information, malicious code injection, abnormal network flow and eavesdropping data are performed, wherein the generated communication data are as follows:
the communication data generated by the data 1 and the fake vehicle control instruction are false acceleration, braking or steering commands, and the running state and track of the vehicle are affected;
the communication data generated by the data 2 and the fake vehicle control instruction are false acceleration, braking or steering commands, and the running state and track of the vehicle cannot be influenced;
the communication data generated by the data 3 and the fake road side equipment information is used for transmitting fake road conditions and traffic signals to the road side equipment, so that the running and the track of the vehicle are affected;
the communication data generated by the data 4 and the fake road side equipment information is that fake road conditions and traffic signals are sent to the road side equipment, and the running and the track of the vehicle cannot be influenced;
the data 5 and communication data generated by malicious code injection are the malicious code injection to an operating system or an application program of the vehicle or the road side equipment, and the behavior of the vehicle or the road side equipment is controlled;
the data 6 and communication data generated by malicious code injection are the malicious code injected into an operating system or an application program of the vehicle or the road side equipment, and the behavior of the vehicle or the road side equipment cannot be controlled;
the data 7 and communication data generated by the abnormal network flow are communication data which are used for transmitting a large amount of or repeated network flow to the vehicle or the road side equipment, and the communication and the normal operation of the vehicle and the road side equipment are affected;
the data 8 and communication data generated by the abnormal network flow are large or repeated network flows sent to the vehicle or the road side equipment, so that the communication and normal operation of the vehicle and the road side equipment cannot be influenced;
the data 9 and communication data generated by the interception data are communication data between the interception vehicle and road side equipment, and the position, the speed, the driving route and the like of the vehicle are obtained;
the data 10 and communication data generated by interception data are communication data between an intercepted vehicle and road side equipment, and the position, the speed, the driving route and the like of the vehicle cannot be acquired;
b, extracting communication characteristics of the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
in practical evaluation, the common characteristics of the communication data are data integrity, data confidentiality, data flow and behavior, data source identity authentication, data content security, value density, timeliness, interactivity, diversity, traceability, the extracted communication characteristics are data integrity, data confidentiality, data flow and behavior, data source identity authentication, data content security, value density, interactivity, diversity, the correlation degree of the data integrity and the data confidentiality, data flow and behavior, data source identity authentication, data content security, value density, interactivity, diversity is 0.12, 0.24, 0.19, 0.17, 0.58, 0.69, 0.74, the correlation degree of the data confidentiality and the data flow and behavior, data source identity authentication, data content security, value density, interactivity, diversity is 0.16, 0.27, 0.13, 0.79, 0.81, 0.65 respectively, the correlation degree of the data flow and the behavior and the data source identity authentication, the data content security, the value density, the interactivity and the diversity is 0.25, 0.11, 0.57, 0.82 and 0.67, the correlation degree of the data flow and the behavior and the data source identity authentication, the value density, the interactivity and the diversity is 0.29, 0.85, 0.64 and 0.87, the correlation degree of the data content security and the value density, the interactivity and the diversity is 0.54, 0.87 and 0.68, the correlation degree of the value density and the interactivity and the diversity is 0.71 and 0.73, the correlation degree of the interactivity and the diversity is 0.77, and abnormal characteristics such as data integrity, data confidentiality, data flow and behavior, the data source identity authentication and the data content security are screened.
Classifying the communication data according to the abnormal characteristics to obtain abnormal classification data, and performing abnormal recognition on the abnormal classification data to obtain abnormal data;
d, constructing an information security test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information security test model, and outputting an information security test result;
in the actual evaluation, the security test results of the data 9 and the data 10 are predicted to be unsafe data confidentiality and unsafe respectively.
In this embodiment, the method of preprocessing in step a includes removing duplicate data, deleting missing values and outliers, data conversion, and data specifications.
In this embodiment, the method for extracting communication characteristics of the communication data includes:
the dimensionality of the initial spatial communication data is randomly planned, an initial dimensionality is provided for data conversion and processing, the communication data is converted into a training test data set, and a covariance matrix of the subspace communication data is calculated:
wherein the mean vector of all training test data sets in the subspace isThe ith communication data is +.>The transpose of the difference between the communication data and the mean vector is +.>The number of the communication data is n;
performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, arranging the eigenvectors in descending order according to the corresponding eigenvalues, selecting the eigenvectors to form a component analysis conversion matrix, and performing principal component analysis conversion on communication data:
wherein test data after conversionThe principal component analysis conversion matrix is Q, D, and the transpose of the principal component analysis conversion matrix isAccording to the magnitude of the characteristic value, selecting the characteristic vector as a main component, and projecting communication data onto the selected main component to obtain the initial characteristic of the communication data;
extracting trend features and mean features according to the low-frequency signals after wavelet decomposition of the initial features:
wherein the low frequency signal isTrend is characterized by->The mean value is characterized by->The mean value of the low frequency signal is +.>And (3) setting the time as E and the time length as E, and establishing a regression model:
wherein the scale parameter isMinimum error is->Wavelet variance is calculated:
wherein the domain is b and the wavelet coefficient isExtracting high-frequency signal characteristics according to wavelet coefficients:
wherein the high frequency signal of the ith layer at time e isThe mean value of the high-frequency signal of the i-th layer is +.>High-frequency signal of the i-th layer +.>Characterized by->Outputting the high-frequency signal characteristic as a communication characteristic;
in practical evaluation, common characteristics of communication data are data integrity, data confidentiality, data flow and behavior, data source identity authentication, data content security, value density, timeliness, interactivity, diversity and traceability, and extracted communication characteristics are data integrity, data confidentiality, data flow and behavior, data source identity authentication, data content security, value density, interactivity and diversity.
In this embodiment, the method for classifying the communication data according to the abnormal feature to obtain abnormal classification data includes:
inputting the abnormal features into a classification algorithm, and calculating the distance between the features and the hyperplane:
wherein the ith exceptionFeatures (e.g. a character)Distance to the hyperplane is +.>The hyperplane is A, B, C, vector coordinates of the anomaly feature +.>Slope m, selecting a kernel function:
wherein the ith anomaly is characterized byThe j-th abnormality is +>The mapping function of the abnormal feature is +.>The transpose of the mapping function is +.>High-dimensional mapping is performed by using a kernel function, and the maximum interval is calculated:
wherein the x-axis position of the jth anomaly isJ-axis position of jth abnormal featurePut into->The number of the abnormal features is r, a hyperplane with the maximum interval is obtained according to the maximum interval, and communication data are selected according to the hyperplane to be classified to obtain abnormal classification data;
in actual evaluation, data are classified into a complete class, an encryption class, a flow direction and a behavior class according to abnormal characteristics, wherein data 2, data 4, data 6 and data 8 are the complete class, data 10 and data 9 are the encryption class, and data 1, data 3, data 5 and data 7 are the flow direction and the behavior class.
In this embodiment, the method for obtaining the abnormal data by performing the abnormal recognition on the abnormal classification data includes:
inputting the abnormal classification data, and calculating the distance of the abnormal classification data:
wherein the data is p, the c-th data isThe number of data is s, the c-th data nearest to data p is +.>The data closest to data p is +.>The k distance of the data p is +.>Calculating the data reachable distance:
wherein the distance between data p and data o isLocal availability of the calculation data pThe density is reached:
wherein the set of all data of the abnormality classification data having a distance from the point p of not more than the k distance of the point p isCalculating local outlier factors of the data:
wherein the local reachable density of data o isJudging whether the data is abnormal or not according to the abnormal proportion threshold value and the local outlier factor, correlating the abnormal characteristics of the corresponding abnormal classified data with the classified data after the abnormal recognition, and outputting the abnormal data;
in the actual evaluation, the local outliers of data 1, data 2, data 3, data 4, data 5, data 6, data 7, data 9, and data 10 were 2.7, 0.7, 1.8, 0.96, 5.31, 0.39, 3.47, 0.54, 4.67, and 0.73, respectively, and the identified outliers were data 1, data 3, data 5, data 7, and data 9.
Further, the method for constructing the information security test model according to the abnormal characteristics and the abnormal classification data comprises a cyclic neural network algorithm, wherein the abnormal classification data is divided into a test set and a training set according to a ratio of 4:1, the test set is input into the information security test model for training, the accuracy and the efficiency are higher than 0.9 and are used as optimization targets, the attention mechanism is increased, the information security test model is optimized, and the training set is input into a trained cyclic neural network algorithm to predict the information security test result.
In a second aspect, an automatic driving information safety test system based on vehicle-road cooperation includes:
and a data acquisition module: the method comprises the steps of simulating an attack scene, acquiring communication data and preprocessing the communication data;
and the feature extraction module is used for: for performing a communication feature extraction on the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
an anomaly identification module: the communication data are classified according to the abnormal characteristics to obtain abnormal classification data, and the abnormal classification data are subjected to abnormal recognition to obtain abnormal data;
and a result output module: and the information safety test module is used for constructing an information safety test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information safety test model and outputting an information safety test result.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The automatic driving information safety test method based on the vehicle-road cooperation is characterized by comprising the following steps of:
a, simulating an attack scene, acquiring communication data, and preprocessing the communication data;
b, extracting communication characteristics of the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
classifying the communication data according to the abnormal characteristics to obtain abnormal classification data, and performing abnormal recognition on the abnormal classification data to obtain abnormal data;
and D, constructing an information security test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information security test model, and outputting an information security test result.
2. The vehicle-road-collaboration-based automatic driving information security test method according to claim 1, wherein the preprocessing method in step a includes removing duplicate data, deleting missing values and abnormal values, data conversion, and data specifications.
3. The vehicle-road-collaboration-based automatic driving information safety test method according to claim 1, wherein the method for extracting communication characteristics of the communication data comprises the steps of:
the dimensionality of the initial spatial communication data is randomly planned, an initial dimensionality is provided for data conversion and processing, the communication data is converted into a training test data set, and a covariance matrix of the subspace communication data is calculated:
wherein the mean vector of all training test data sets in the subspace isThe ith communication data is +.>The transpose of the difference between the communication data and the mean vector is +.>The number of the communication data is n;
performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues and corresponding eigenvectors, arranging the eigenvectors in descending order according to the corresponding eigenvalues, selecting the eigenvectors to form a component analysis conversion matrix, and performing principal component analysis conversion on communication data:
wherein the test data after conversion is Q, the principal component analysis conversion matrix is D, and the transpose of the principal component analysis conversion matrix isAccording to the magnitude of the characteristic value, selecting the characteristic vector as a main component, and projecting communication data onto the selected main component to obtain the initial characteristic of the communication data;
extracting trend features and mean features according to the low-frequency signals after wavelet decomposition of the initial features:
wherein the low frequency signal isTrend is characterized by->The mean value is characterized by->The mean value of the low frequency signal is +.>And (3) setting the time as E and the time length as E, and establishing a regression model:
wherein the scale parameter isMinimum error is->Wavelet variance is calculated:
wherein the domain is b and the wavelet coefficient isExtracting high-frequency signal characteristics according to wavelet coefficients:
wherein the high frequency signal of the ith layer at time e isThe mean value of the high-frequency signal of the i-th layer is +.>High-frequency signal of the i-th layer +.>Characterized by->The high frequency signal characteristic is output as a communication characteristic.
4. The vehicle-road-collaboration-based automatic driving information safety test method according to claim 1, wherein the method for classifying the communication data according to the abnormality feature to obtain abnormality classification data comprises:
inputting the abnormal features into a classification algorithm, and calculating the distance between the features and the hyperplane:
wherein the ith anomaly featureDistance to the hyperplane is +.>The hyperplane is A, B, C, the vector coordinates of the abnormal featuresSlope m, selecting a kernel function:
wherein the ith anomaly is characterized byThe j-th abnormality is +>The mapping function of the abnormal feature is +.>The transpose of the mapping function is +.>High-dimensional mapping is performed by using a kernel function, and the maximum interval is calculated:
wherein the x-axis position of the jth anomaly isThe j-axis position of the j-th abnormal feature is +.>The number of the abnormal features is r, a hyperplane with the maximum interval is obtained according to the maximum interval, and communication data are selected according to the hyperplane to be classified, so that abnormal classification data are obtained.
5. The vehicle-road-collaboration-based automatic driving information safety test method according to claim 1, wherein the method for acquiring the abnormal data by performing abnormality identification on the abnormal classification data comprises the following steps:
calculating the distance of the abnormal classification data:
wherein the data is p, the c-th data isThe number of data is s, the c-th data nearest to data p is +.>The data closest to data p is +.>The k distance of the data p is +.>Calculating the data reachable distance:
wherein the distance between data p and data o isCalculating the local reachable density of the data p:
wherein the set of all data of the abnormality classification data having a distance from the point p of not more than the k distance of the point p isCalculating local outlier factors of the data:
wherein the local reachable density of data o isJudging whether the data is abnormal or not according to the abnormal proportion threshold value and the local outlier factor, correlating the abnormal characteristics of the corresponding abnormal classified data with the classified data after the abnormal recognition, and outputting the abnormal data.
6. An automatic driving information safety test system based on vehicle-road cooperation is characterized by comprising:
and a data acquisition module: the method comprises the steps of simulating an attack scene, acquiring communication data and preprocessing the communication data;
and the feature extraction module is used for: for performing a communication feature extraction on the communication data,
and screening and obtaining abnormal characteristics according to the relevance of the communication characteristics:
calculating the correlation between the communication features:
wherein the characteristic is g, and the average value of the characteristic isThe number of features is n, the ith feature is +.>The j-th feature is->The relevance of the ith feature to the jth feature is +.>Screening out communication characteristics with correlation degree between 0 and 0.3 as abnormal characteristics;
an anomaly identification module: the communication data are classified according to the abnormal characteristics to obtain abnormal classification data, and the abnormal classification data are subjected to abnormal recognition to obtain abnormal data;
and a result output module: and the information safety test module is used for constructing an information safety test model according to the abnormal characteristics and the abnormal classification data, inputting the communication data into the information safety test model and outputting an information safety test result.
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