CN116027903A - Computer network security analysis system and method based on big data - Google Patents
Computer network security analysis system and method based on big data Download PDFInfo
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Abstract
The invention discloses a computer network security analysis system and a computer network security analysis method based on big data, which belong to the field of network security analysis, wherein the network security analysis system comprises a data acquisition module, a database, a data analysis module and a mouse driving module; the data acquisition module is used for acquiring basic data information, computer information used by a user and webpage sensitive word information, the database is used for encrypting and storing the acquired information and analysis results, the data analysis module is used for analyzing and processing the acquired data information, and the mouse driving module is used for automatically controlling a mouse cursor to operate according to the analysis results. The invention inputs the basic sensitive word information of the database, analyzes the state of the user using the computer, the sensitive word information of the page and the click position information, establishes an operation learning model, and automatically drives the mouse cursor to operate when the user is not using the computer, thereby reducing the safety problem of the computer network and improving the experience and the working efficiency of the user using the computer.
Description
Technical Field
The invention relates to the field of network security analysis, in particular to a computer network security analysis system and method based on big data.
Background
With the continuous development of computers, computers bring convenience to people in many aspects of life, and the computers are not opened in science, education or offices. In life, people can communicate through a computer, can check news without going out, can watch favorite films at any time, can use learning shopping and the like. The internal circuit of the computer is composed, and various arithmetic operations can be accurately completed at high speed. The operation speed of the computer system reaches trillion times per second at present, and the microcomputer can reach more than billions times per second, so that a great deal of complex scientific calculation problems are solved. The computer not only can perform accurate calculation, but also has a logic operation function, and can compare and judge information. The computer can save the data, the program, the intermediate result and the final result of the operation, and can automatically execute the next instruction according to the judged result so as to be called by the user at any time. The memory inside the computer has memory characteristic and can store great amount of information including various data information and program for processing the data. Because the computer has memory capacity and logic judgment capacity, people can incorporate the pre-programmed program group into the computer memory, and under the control of the program, the computer can work continuously and automatically without human intervention.
However, when people use a computer, a large number of advertising bullets often appear, requiring frequent user clicks to close. Sometimes, when the user does not watch the computer for a period of time and watches the computer interface again, the interface of the computer has a lot of advertisements, the use of the computer by the user is interfered, sometimes, the user can jump to the advertisement interface by touching the advertisement link by mistake, the use feeling of the user is influenced, and meanwhile, the network security of the computer is also influenced.
It is therefore necessary to monitor how the user is using the computer and how the advertisement on the interface is automatically turned off when the user is not using the computer. Thus, there is a need for a system and method for computer network security analysis based on big data.
Disclosure of Invention
The invention aims to provide a computer network security analysis system and a computer network security analysis method based on big data, which are used for inputting basic sensitive word information of a database, acquiring the state of a user using a computer through camera equipment, acquiring sensitive word information of a page and click position information of the page of the user, establishing an operation learning model, and automatically driving a mouse cursor to operate when the user is not using the computer so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the computer network security analysis system based on big data comprises a data acquisition module, a database, a data analysis module and a mouse driving module;
the data acquisition module is connected with the database, the database is connected with the data analysis module, and the data analysis module is connected with the mouse driving module; the data acquisition module is used for acquiring basic data information, computer state information used by a user and webpage sensitive word information, the database is used for encrypting and storing acquired information and analysis results, the data analysis module is used for analyzing and processing the acquired data information, and the mouse driving module is used for automatically controlling a mouse cursor to operate according to the analysis results.
Further, the data acquisition module comprises a basic information acquisition unit, an image acquisition unit and a webpage acquisition unit, wherein the basic information acquisition unit is used for inputting basic sensitive word information such as welfare, advertisements, answering and rewards, the image acquisition unit is used for acquiring head posture information of a user when the computer is used through a camera device, such as a camera or a safety camera of the computer, and the webpage acquisition unit is used for acquiring the sensitive word information of a page used by the computer and the position information of the page clicked by the user in real time.
Further, the database includes an encryption unit and a storage unit, the encryption unit encrypts data in the whole process by using an AES encryption algorithm, which is one of a plurality of symmetric encryption algorithms, its english full name being Advanced Encryption Standard, translated into an advanced encryption standard, which is used to replace the previous DES encryption algorithm. The AES encryption algorithm uses a block cipher system, each block data has a length of 128 bits and 16 bytes, the key length can be 128 bits and 16 bytes, 192 bits or 256 bits, there are four encryption modes, we generally use CBC mode that requires an initial vector IV, the length of the initial vector is also 128 bits and 16 bytes, and the storage unit is used to store the collected information and the analysis result.
Further, the data analysis module comprises a user analysis unit and a webpage analysis unit, the user analysis unit is used for analyzing the head gesture of the user through image information acquired in real time when the user uses the computer, so that the user can conveniently analyze the state of the user when using the computer, such as checking files, thinking problems in work and the like, the user state can be effectively known through analyzing the head gesture of the user, the precision requirement on the camera equipment is not high, the acquisition process can be simply and quickly realized through the existing camera equipment, the webpage analysis unit is used for analyzing the sensitive word information acquired in real time when the computer uses the page, an operation learning model is established, the position information of the page clicked by the user is analyzed and learned, the computer has memory storage capacity, the system can automatically learn and simulate the behavior of the user to click to close the advertisement by using a mouse according to the historical data manually closed by the user, the computer can continuously and automatically work under program control, and human intervention is not needed.
Furthermore, the mouse driving module is used for automatically driving the mouse cursor to operate when the computer is in a working state according to an analysis result, but the user does not operate the computer within a set time, for example, the user can automatically click a closing button or cross numbers to close the appeared advertisements, the existing advertisement interception plug-in units can only filter the advertisements of specific websites, the advertisement can not be completely filtered for some systems, the user is required to manually close the advertisement, and the automatic driving of the mouse cursor is utilized to close the advertisement, so that the problem of insufficient advertisement closing can be effectively avoided, the use experience of the user when the computer is used is greatly improved, and the working efficiency of the user is improved.
The computer network security analysis system and method based on big data comprises the following steps:
s1, inputting basic sensitive word information, acquiring head posture information of a user when the user uses a computer through camera equipment, and acquiring sensitive word information of a page and page click position information;
s2, analyzing the head gesture of the user, and identifying the state of the user when the user uses the computer;
s3, establishing an operation learning model according to the position information of the clicked page by the user, and analyzing the sensitive word information of the page when the user is not using the computer;
and S4, driving a mouse cursor to automatically operate according to the analysis result.
Further, in step S2, when the user is not in front of the computer, step S3 is directly performed; when the user is in front of the computer, analyzing according to the acquired head posture image of the user, establishing a three-dimensional vertical coordinate system, and measuring the distance by using an image ranging technology;
the rotation angle around the x axis is alpha, and represents the pitching degree of the human head; by l 0 Representing the projection length of nose on face image when the face view is in front, l 1 Representing the projected length of the nose at angle alpha on the image, using (x 0 ，y 0 ，z 0 ) And (x) 1 ，y 1 ，z 1 ) Representing the spatial coordinates of the nose feature points in the two cases respectively, using d 1 Representing the spatial distance between the outer intersection of the eyes and the nose feature point, f is the focal length of the camera, then:
α＝arcsinM；
wherein L is 1 Represents the average length of the nose, L 2 Representing the average length of the eyes, ω being the projected length of the eyes under the frontal image of the face. Setting a threshold alpha to the rotation angle Threshold value Setting a threshold t for the rotation angle maintaining time t Threshold value When alpha is greater than or equal to alpha Threshold value And t is greater than or equal to t Threshold value When it is determined that the user has not used the computer for time t. The head pitching degree of the user is analyzed, whether the user is looking at the computer can be effectively known, when the information containing the sensitive words appears on the interface, whether the user needs to drive the mouse cursor to assist in operation processing can be rapidly judged, the situation that advertisements are not cleaned when the user does not use the computer for a long time although the user is in front of the computer is avoided, and the cleaning efficiency of the system is improved.
Further, in step S3, sensitive word information of the page is analyzed, a database is called, and similarity of the sensitive word information is calculated;
mapping the text monitored by the page by using a word mapping algorithm into a numerical vector space, establishing a coordinate system, processing page sensitive word information to obtain a vector set A, processing sensitive word information recorded in a database to obtain a vector set B, setting beta as the similarity of the vector set A and the vector set B, and then
Setting a threshold beta for similarity Threshold value When beta is greater than or equal to beta Threshold value When the content is displayed, the sensitive words of the page content are similar to the sensitive words stored in the database, otherwise, the sensitive words of the page content are dissimilar to the sensitive words stored in the database;
when the sensitive words of the page content are similar to the sensitive words stored in the database, the click position (x i ，y i ) Form the set p= { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N is the corresponding computer page image i Form the set n= { N 1 ，N 2 ，…，N n Obtaining a corresponding sensitive word position vector set C, wherein n represents that n times of acquisition are carried out, storing acquired data into a database, and establishing an operation learning model;
calculating the similarity gamma between the sensitive word position information in the current picture and the sensitive word position information in the database through the following formula:
q is a sensitive word position vector set of the current page;
setting a threshold gamma for similarity Threshold value When gamma is greater than or equal to gamma Threshold value When the position of the sensitive word representing the current page is similar to the position of the sensitive word storing the image in the database, the mouse cursor is driven to click the corresponding position, otherwise, the positions are dissimilar, and the mouse cursor cannot be driven. According to the historical data of the advertisement manually closed when a user uses the computer, the analysis data are matched with the historical data, so that the operation can be performed more accurately, the accuracy of the click operation of the driving mouse cursor is improved, the phenomenon that the advertisement is not closed or the advertisement is clicked due to the occurrence of the dislocation of the point is avoided, the operation learning model can be optimized continuously, and the accuracy of the system driving the mouse cursor can be higher along with the increase of the historical data.
Further, in step S4, according to the analysis result, when the user is not using the computer and the sensitive word appears on the page, the mouse cursor is automatically driven to process the sensitive word, for example, the user automatically clicks the close button or crosses to close the appearing advertisement. According to the analysis result, the sensitive word appearance interface can be processed more accurately, and along with the increase of the historical data, the accuracy of the system can be increased continuously, the use feeling of a user on a computer can be improved better, and the working efficiency of the user is improved.
Compared with the prior art, the invention has the following beneficial effects:
the invention inputs basic sensitive word information of a database, acquires the state of a user using a computer through the camera equipment, and acquires whether the user is using the computer or not by analyzing the head gesture of the user when the user is in front of the computer. And analyzing sensitive word information of the collected page and click position information of the user page, establishing an operation learning model, and when the user is not using the computer, such as looking down at data, operating the advertisement with the sensitive word by automatically driving a mouse cursor, such as clicking to close or crossing, without manually driving the mouse to close the advertisement when the user uses the computer again. The invention can effectively improve the use experience of the user on the computer, avoid the user from entering the advertisement interface caused by false touch, reduce the safety problem of the computer network and improve the efficiency and the working efficiency of the user on the computer.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the modular composition of the big data based computer network security analysis system of the present invention;
FIG. 2 is a schematic diagram of the steps of the big data based computer network security analysis method of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the computer network security analysis system based on big data comprises a data acquisition module, a database, a data analysis module and a mouse driving module;
the data acquisition module is connected with the database, the database is connected with the data analysis module, and the data analysis module is connected with the mouse driving module;
the data acquisition module is used for acquiring basic data information, user using computer state information and webpage sensitive word information. The data acquisition module comprises a basic information acquisition unit, an image acquisition unit and a webpage acquisition unit, wherein the basic information acquisition unit is used for inputting basic sensitive word information such as welfare, advertisements, answering and rewards, the image acquisition unit is used for acquiring head posture information of a user when the computer is used through a camera device, such as a camera or a safety camera of the computer, and the webpage acquisition unit is used for acquiring the sensitive word information of a page used by the computer and the position information of the page clicked by the user in real time.
The database is used for encrypting and storing the acquired information and the analysis result. The database comprises an encryption unit and a storage unit, wherein the encryption unit encrypts data in the whole process by adopting an AES encryption algorithm, which is one of a plurality of symmetrical encryption algorithms, the English name of which is Advanced Encryption Standard, and the translation of which is an advanced encryption standard which is used for replacing the prior DES encryption algorithm. The AES encryption algorithm uses a block cipher system, each block data has a length of 128 bits and 16 bytes, the key length can be 128 bits and 16 bytes, 192 bits or 256 bits, there are four encryption modes, we generally use CBC mode that requires an initial vector IV, the length of the initial vector is also 128 bits and 16 bytes, and the storage unit is used to store the collected information and the analysis result.
The data analysis module is used for analyzing and processing the collected data information. The data analysis module comprises a user analysis unit and a webpage analysis unit, wherein the user analysis unit is used for analyzing the head gesture of a user through image information acquired in real time when the user uses a computer, so that the user can conveniently analyze the state of the user when using the computer, such as checking files, thinking problems in work and the like, the user state can be effectively known through analyzing the head gesture of the user, the precision requirement on the camera equipment is low, the acquisition process can be simply and quickly realized through the existing camera equipment, the webpage analysis unit is used for analyzing the sensitive word information acquired in real time when the computer uses the page, establishing an operation learning model, analyzing and learning the position information of the page clicked by the user, the computer has memory storage capacity, the system can automatically learn according to historical data manually closed by the user through establishing the operation learning model, simulate the behavior of the user when the user clicks to close the advertisement by using a mouse, and the computer can continuously and automatically work without human intervention under program control.
And the mouse driving module is used for automatically controlling the mouse cursor to operate according to the analysis result. The mouse driving module is used for automatically driving a mouse cursor to operate when the computer is in a working state according to an analysis result, but a user does not operate the computer within a set time, such as automatically clicking a closing button or crossing numbers to close the appeared advertisements, most of the existing advertisement interception plug-ins can only filter the advertisements of specific websites, and the advertisement can not be completely filtered for some systems, so that the user is required to manually close the advertisements, and the problem of insufficient advertisement closing can be effectively avoided by automatically driving the mouse cursor, the use experience of the user when the computer is used is greatly improved, and the working efficiency of the user is improved.
The computer network security analysis system and method based on big data comprises the following steps:
s1, inputting basic sensitive word information, acquiring head posture information of a user when the user uses a computer through camera equipment, and acquiring sensitive word information of a page and page click position information;
s2, analyzing the head gesture of the user, and identifying the state of the user when the user uses the computer;
in step S2, when the user is not in front of the computer, directly performing step S3; when the user is in front of the computer, analyzing according to the acquired head posture image of the user, establishing a three-dimensional vertical coordinate system, and measuring the distance by using an image ranging technology;
the rotation angle around the x axis is alpha, and represents the pitching degree of the human head; by l 0 Representing the projection length of nose on face image when the face view is in front, l 1 Representing the projected length of the nose at angle alpha on the image, using (x 0 ，y 0 ，z 0 ) And (x) 1 ，y 1 ，z 1 ) Representing the spatial coordinates of the nose feature points in the two cases respectively, using d 1 Representing the spatial distance between the outer intersection of the eyes and the nose feature point, f is the focal length of the camera, then:
α＝arcsinM；
wherein L is 1 Represents the average length of the nose, L 2 Representing the average length of the eyes, ω being the projected length of the eyes under the frontal image of the face. Setting a threshold alpha to the rotation angle Threshold value Setting a threshold t for the rotation angle maintaining time t Threshold value When alpha is greater than or equal to alpha Threshold value And t is greater than or equal to t Threshold value When it is determined that the user has not used the computer for time t. The head pitching degree of the user is analyzed, whether the user is looking at the computer can be effectively known, when the information containing the sensitive words appears on the interface, whether the user needs to drive the mouse cursor to assist in operation processing can be rapidly judged, the situation that advertisements are not cleaned when the user does not use the computer for a long time although the user is in front of the computer is avoided, and the cleaning efficiency of the system is improved.
S3, establishing an operation learning model according to the position information of the clicked page by the user, and analyzing the sensitive word information of the page when the user is not using the computer;
in step S3, analyzing the sensitive word information of the page, calling a database, and calculating the similarity of the sensitive word information;
mapping the text monitored by the page by using a word mapping algorithm into a numerical vector space, establishing a coordinate system, processing page sensitive word information to obtain a vector set A, processing sensitive word information recorded in a database to obtain a vector set B, setting beta as the similarity of the vector set A and the vector set B, and then
Setting a threshold beta for similarity Threshold value When beta is greater than or equal to beta Threshold value When the content is displayed, the sensitive words of the page content are similar to the sensitive words stored in the database, otherwise, the sensitive words of the page content are dissimilar to the sensitive words stored in the database;
when the sensitive words of the page content are similar to the sensitive words stored in the database, the click position (x i ，y i ) Form the set p= { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N is the corresponding computer page image i Form the set n= { N 1 ，N 2 ，…，N n Obtaining a corresponding sensitive word position vector set C, wherein n represents that n times of acquisition are carried out, storing acquired data into a database, and establishing an operation learning model;
calculating the similarity gamma between the sensitive word position information in the current picture and the sensitive word position information in the database through the following formula:
q is a sensitive word position vector set of the current page;
setting a threshold gamma for similarity Threshold value When gamma is greater than or equal to gamma Threshold value When the sensitive word position representing the current page is in the databaseThe positions of the sensitive words of the stored images are similar, the mouse cursor is driven to click the corresponding positions, otherwise, the positions are dissimilar, and the mouse cursor cannot be driven. . According to the historical data of the advertisement manually closed when a user uses the computer, the analysis data are matched with the historical data, so that the operation can be performed more accurately, the accuracy of the click operation of the driving mouse cursor is improved, the phenomenon that the advertisement is not closed or the advertisement is clicked due to the occurrence of the dislocation of the point is avoided, the operation learning model can be optimized continuously, and the accuracy of the system driving the mouse cursor can be higher along with the increase of the historical data.
And S4, driving a mouse cursor to automatically operate according to the analysis result.
In step S4, according to the analysis result, when the user is not using the computer and the sensitive word appears on the page, the mouse cursor is automatically driven to process the sensitive word, for example, the user automatically clicks a close button or crosses to close the appearing advertisement. According to the analysis result, the sensitive word appearance interface can be processed more accurately, and along with the increase of the historical data, the accuracy of the system can be increased continuously, the use feeling of a user on a computer can be improved better, and the working efficiency of the user is improved.
Embodiment case one:
when the user is in front of the computer, the head gesture analysis is carried out on the user, and the rotation angle is set to be a threshold value alpha Threshold value =20°, the rotation angle maintenance time sets a threshold t Threshold value =2 minutes, if calculated
α＝arcsinM＝45°≥α Threshold value If the rotation angle maintaining time is 20 seconds, the next step is not performed, and if the rotation angle maintaining time is 3 minutes, it is determined that the user is not using the computer.
When the user is not using the computer, sensitive word information of the page is analyzed, and a threshold beta is set for similarity Threshold value =0.8, calculate the sensitive word information similarity:
at this timeCalculating the similarity gamma between the sensitive word position information in the current picture and the sensitive word position information in the database: />
Similarity setting threshold gamma Threshold value =0.9, then γ>γ Threshold value The sensitive word position of the current page is similar to the sensitive word position of the stored image in the database, the mouse cursor is driven to click the corresponding position, and the advertisement is closed.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The computer network security analysis system based on big data is characterized in that: the network security analysis system comprises: the device comprises a data acquisition module, a database, a data analysis module and a mouse driving module;
the data acquisition module is connected with the database, the database is connected with the data analysis module, and the data analysis module is connected with the mouse driving module; the data acquisition module is used for acquiring basic data information, computer state information used by a user and webpage sensitive word information, the database is used for encrypting and storing acquired information and analysis results, the data analysis module is used for analyzing and processing the acquired data information, and the mouse driving module is used for automatically controlling a mouse cursor to operate according to the analysis results.
2. The big data based computer network security analysis system of claim 1, wherein: the data acquisition module comprises a basic information acquisition unit, an image acquisition unit and a webpage acquisition unit, wherein the basic information acquisition unit is used for inputting basic sensitive word information, the image acquisition unit is used for acquiring head posture information of a user when the user uses the computer through a camera device, and the webpage acquisition unit is used for acquiring the sensitive word information of the page used by the computer and the position information of the page clicked by the user in real time.
3. The big data based computer network security analysis system of claim 2, wherein: the database comprises an encryption unit and a storage unit, wherein the encryption unit is used for encrypting data in the whole process by adopting an AES encryption algorithm, and the storage unit is used for storing acquired information and analysis results.
4. A big data based computer network security analysis system according to claim 3, wherein: the data analysis module comprises a user analysis unit and a webpage analysis unit, wherein the user analysis unit is used for analyzing the head gesture of a user through image information acquired in real time when the user uses a computer, and the webpage analysis unit is used for analyzing sensitive word information acquired in real time when the computer uses a page, establishing an operation learning model and analyzing and learning the position information of the user clicking the page.
5. The big data based computer network security analysis system of claim 4, wherein: the mouse driving module is used for automatically driving a mouse cursor to operate according to the analysis result when the computer is in a working state but the user does not operate the computer within a set time.
6. The computer network security analysis system and method based on big data is characterized in that: the method comprises the following steps:
s1, inputting basic sensitive word information, acquiring head posture information of a user when the user uses a computer through camera equipment, and acquiring sensitive word information of a page and page click position information;
s2, analyzing the head gesture of the user, and identifying the state of the user when the user uses the computer;
s3, establishing an operation learning model according to the position information of the clicked page by the user, and analyzing the sensitive word information of the page when the user is not using the computer;
and S4, driving a mouse cursor to automatically operate according to the analysis result.
7. The big data based computer network security analysis method of claim 6, wherein: in step S2, when the user is not in front of the computer, directly performing step S3; when the user is in front of the computer, analyzing according to the acquired head posture image of the user, establishing a three-dimensional vertical coordinate system, and measuring the distance by using an image ranging technology;
the rotation angle around the x axis is alpha, and represents the pitching degree of the human head; by l 0 Representing the projection length of nose on face image when the face view is in front, l 1 Representing the projected length of the nose at angle alpha on the image, using (x 0 ，y 0 ，z 0 ) And (x) 1 ，y 1 ，z 1 ) Representing the spatial coordinates of the nose feature points in the two cases respectively, using d 1 Representing the distance between the point of intersection outside the eyes and the point of the noseSpatial distance, f is the focal length of the camera, then:
α＝arcsinM；
wherein L is 1 Represents the average length of the nose, L 2 Representing the average length of eyes, wherein omega is the projection length of eyes under the front image of the human face; setting a threshold alpha to the rotation angle Threshold value Setting a threshold t for the rotation angle maintaining time t Threshold value When alpha is greater than or equal to alpha Threshold value And t is greater than or equal to t Threshold value When it is determined that the user has not used the computer for time t.
8. The big data based computer network security analysis method of claim 7, wherein: in step S3, analyzing the sensitive word information of the page, calling a database, and calculating the similarity of the sensitive word information;
mapping the text monitored by the page by using the wordbelting algorithm into a numerical vector space, establishing a coordinate system, processing page sensitive word information to obtain a vector set A, processing sensitive word information recorded in a database to obtain a vector set B, setting beta as the similarity of the vector set A and the vector set B, and then
Setting a threshold beta for similarity Threshold value When beta is greater than or equal to beta Threshold value When the content is displayed, the sensitive words of the page content are similar to the sensitive words stored in the database, otherwise, the sensitive words of the page content are dissimilar to the sensitive words stored in the database;
when sensitive words of page content are stored in databaseWhen the stored sensitive words are similar, the click position (x i ，y i ) Form the set p= { (x) 1 ，y 1 )，(x 2 ，y 2 )，…，(x n ，y n ) N is the corresponding computer page image i Form the set n= { N 1 ，N 2 ，…，N n Obtaining a corresponding sensitive word position vector set C, wherein n represents that n times of acquisition are carried out, storing acquired data into a database, and establishing an operation learning model;
calculating the similarity gamma between the sensitive word position information in the current picture and the sensitive word position information in the database through the following formula:
q is a sensitive word position vector set of the current page;
setting a threshold gamma for similarity Threshold value When gamma is greater than or equal to gamma Threshold value When the position of the sensitive word representing the current page is similar to the position of the sensitive word storing the image in the database, the mouse cursor is driven to click the corresponding position, otherwise, the positions are dissimilar, and the mouse cursor cannot be driven.
9. The big data based computer network security analysis method of claim 8, wherein: in step S4, according to the analysis result, when the user is not using the computer and the sensitive word appears on the page, the mouse cursor is automatically driven to process the sensitive word.
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