CN115529155A - Network user behavior analysis system - Google Patents
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Abstract
The invention discloses a network user behavior analysis system which comprises a modeling module, a forwarding behavior analysis module and a user behavior analysis module, wherein the modeling module is used for establishing a social network model, the forwarding behavior analysis module is used for analyzing the forwarding behavior of a user, and the user behavior analysis module is used for analyzing the user behavior according to the forwarding behavior of the user. The invention can realize multi-level identity recognition and can realize the distribution authority management of the first layer interface module, the second layer interface module, the third layer interface module and the fourth layer interface module at the same time, and the safety coefficient is high.
Description
Technical Field
The invention relates to the technical field of behavior analysis systems, in particular to a network user behavior analysis system.
Background
With the continuous development of internet technology, more and more information resources select networks as a carrier for propagation, and users can access information in each web page through a web browser, in particular, for a social network site or a shopping network site, there are specific registered users, in order to manage the registered users, for example, to identify whether the registered users are malicious users or analyze interest and preference of the users, a website server generally needs to analyze behaviors of the registered users, and as the number of registered users increases, the amount of analysis data also significantly increases, and the processing workload and time also significantly increases.
At present, most of the commonly used identity recognition systems adopt a password recognition mode, namely a character string with the length of 5-8 generally consists of numbers, letters, special characters, control characters and the like, but the recognition mode has the problems of low safety factor and easy imposition of people.
Disclosure of Invention
The invention aims to: in order to solve the problems, a network user behavior analysis system is provided.
In order to achieve the purpose, the invention adopts the following technical scheme:
a network user behavior analysis system comprises a modeling module, a forwarding behavior analysis module and a user behavior analysis module, wherein the modeling module is used for establishing a social network model, the forwarding behavior analysis module is used for analyzing forwarding behaviors of users, the user behavior analysis module is used for analyzing user behaviors according to the forwarding behaviors of the users, and the modeling module adopts the following modes to establish the social network model: representing the social network model as a binary group E = (U, B), wherein U represents a user node set, B represents an edge set, and if a user U and a user v in the user node set concern each other, an edge (U, v) exists between the user U and the user v, and the user U and the user v are adjacent nodes; the forwarding behavior analysis module comprises a forwarding probability calculation unit, a forwarding index determination unit and a forwarding behavior analysis unit, wherein the forwarding probability calculation unit is used for calculating the probability that the published information of the user is forwarded, the forwarding index determination unit is used for determining the forwarding index of the user according to the probability that the published information of the user is forwarded, and the forwarding behavior analysis unit is used for analyzing the forwarding of the user behavior according to the forwarding index.
Preferably, the calculating the probability of the forwarded information of the user specifically comprises: 1. representing all adjacent node sets of the user u by L (u), regarding the user u and adjacent nodes v belonging to L (u), if a user w exists, making w belonging to L (u) and w belonging to L (v), then the user v is close adjacent nodes of the user u, representing all close adjacent node sets by La (u), if no user w exists, making w belonging to L (u) and w belonging to L (v), then the user v is loose adjacent nodes of the user u, and representing all loose adjacent node sets by Lb (u); 2. calculating the probability that the user is forwarded by the adjacent nodes: in the formula, pu (L (u)) represents the probability that the user u is forwarded by the adjacent node, m (u) represents the number of the user u issuing messages, ru (v) represents the number of the user v forwarding the user u issuing messages, tu (v) represents the number of the user v forwarding the user u issuing messages in the set time limit, | L (u) | represents the number of the adjacent node of the user u; calculating the probability that the user is forwarded by the close adjacent node: in the formula, pu (La (u)) represents the probability that the user u is forwarded by its close neighboring node, | La (u) | represents the number of close neighboring nodes of the user u; calculating the probability that the user is forwarded by the loose adjacent nodes: in the formula, pu (Lb (u)) represents the probability that user u is forwarded by its loose neighbor node, and | Lb (u) | represents the number of loose neighbor nodes that user u has.
Preferably, the forwarding index determining unit includes a first forwarding index determining subunit, a second forwarding index determining subunit and a forwarding index determining subunit, where the first forwarding index determining subunit is configured to determine a first forwarding index of the user, the second forwarding index determining subunit is configured to determine a second forwarding index of the user, and the forwarding index determining subunit is configured to determine the forwarding index of the user according to the first forwarding index and the second forwarding index.
Preferably, the first forwarding index is obtained by using the following formula: in the formula, DYu represents a first forwarding index of a user u; the second forwarding index is obtained by the following method: 1. for user u and its close neighbors v and w, affinity is defined to reflect the degree of affinity between close neighbors: in the formula, tu (v, w) represents the intimacy between nodes v and w, rw (v) represents the number of users v forwarding users w publishing messages, and rv (w) represents the number of users w forwarding users v publishing messages; 2. calculating the activity of the user: hu = a (u) x m (u) where Hu represents the activity of user u and a (u) represents the number of messages published by user u on average per day; 3. calculating a second forwarding index: wherein DEu represents a second forwarding index for user u; the forwarding index is determined using the following equation: in the formula, ZFu represents a forwarding index of the user u, and the user behavior forwarding is analyzed according to the forwarding index, specifically: the larger the forwarding index of the user is, the higher the probability that the user is forwarded is, for the users with the same forwarding index and the larger the second forwarding index is, the higher the probability that the user is forwarded is, and for the users with the same second forwarding index and the larger the first forwarding index is, the higher the probability that the user is forwarded is; the analyzing the user behavior according to the user forwarding behavior specifically comprises: the higher the probability that a user is forwarded, the greater the impact of the user in the network.
The utility model provides a network user behavior analysis system, still including login system, user platform system, central processing unit and alarm system, login system is including password input module, people's face shooting module, fingerprint punching card module and iris shooting module, password input module is used for user input character password, people's face shooting module is used for shooting photo collection user face information and carries out follow-up discernment, fingerprint punching card module is used for gathering user's fingerprint and carries out follow-up recognition of punching card, iris shooting module is used for shooting collection user eyeball iris information and carries out follow-up discernment, central processing unit is including password checking module, face identification module, fingerprint identification module and iris identification module, the password is checked the module and is used for checking the user through the character password of password input module input, face identification module is used for discerning the user and shoots the user face information of gathering through people's face shooting module, fingerprint identification module is used for discerning the user's fingerprint of beating card module collection through the fingerprint, iris identification module is used for discerning the user's eyeball information of shooting the collection through iris.
Preferably, the connection end of the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module is provided with a data comparison module, the connection end of the data comparison module is provided with a database, a data feedback module and an interface authority module, the data comparison module is used for comparing user information identified by the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module by combining the database and the password, and distinguishing authenticity, the interface authority module comprises a first layer interface module, a second layer interface module, a third layer interface module and a fourth layer interface module, and the first layer interface module, the second layer interface module, the third layer interface module and the fourth layer interface module are respectively connected with the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module in sequence.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention can realize multi-level identity recognition and can realize the distribution authority management of the first layer interface module, the second layer interface module, the third layer interface module and the fourth layer interface module at the same time, and the safety coefficient is high.
2. According to the method and the device, user behavior analysis of the social network is achieved, specifically, the social network is modeled based on the mutual attention relationship, a large number of junk users in the social network can be effectively eliminated, and accuracy and credibility of follow-up analysis are improved.
Drawings
FIG. 1 illustrates a schematic structural composition provided in accordance with an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a forwarding behavior analysis module provided according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system architecture provided in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a login system provided in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a central processing unit system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-5, the present invention provides a technical solution:
a network user behavior analysis system comprises a modeling module, a forwarding behavior analysis module and a user behavior analysis module, wherein the modeling module is used for establishing a social network model, the forwarding behavior analysis module is used for analyzing the forwarding behavior of a user, the user behavior analysis module is used for analyzing the user behavior according to the forwarding behavior of the user, and the modeling module adopts the following mode to establish the social network model: representing the social network model as a binary group E = (U, B), wherein U represents a user node set, B represents an edge set, if a user U and a user v in the user node set concern each other, an edge (U, v) exists between the user U and the user v, and the user U and the user v are adjacent nodes; the forwarding behavior analysis module comprises a forwarding probability calculation unit, a forwarding index determination unit and a forwarding behavior analysis unit, wherein the forwarding probability calculation unit is used for calculating the probability of the user published information being forwarded, the forwarding index determination unit is used for determining the forwarding index of the user according to the probability of the user published information being forwarded, and the forwarding behavior analysis unit is used for analyzing the user behavior forwarding according to the forwarding index.
Calculating the probability of forwarding the information published by the user, specifically: 1. representing all adjacent node sets of the user u by L (u), regarding the user u and adjacent nodes v belonging to L (u), if a user w exists, making w belonging to L (u) and w belonging to L (v), then the user v is close adjacent nodes of the user u, representing all close adjacent node sets by La (u), if no user w exists, making w belonging to L (u) and w belonging to L (v), then the user v is loose adjacent nodes of the user u, and representing all loose adjacent node sets by Lb (u); 2. calculating the probability that the user is forwarded by the adjacent nodes: in the formula, pu (L (u)) represents the probability that the user u is forwarded by the adjacent node, m (u) represents the number of messages issued by the user u, ru (v) represents the number of messages issued by the user u forwarded by the user v, tu (v) represents the number of messages issued by the user u forwarded by the user v within a set time limit, and L (u) | represents the number of adjacent nodes of the user u; calculating the probability that the user is forwarded by its close neighbor node: in the formula, pu (La (u)) represents the probability that the user u is forwarded by its close neighboring node, | La (u) | represents the number of close neighboring nodes of the user u; calculating the probability that the user is forwarded by the loose adjacent nodes: in the formula, pu (Lb (u)) represents the probability that user u is forwarded by its loose neighbor node, and | Lb (u) | represents the number of loose neighbor nodes that user u has.
The forwarding index determining unit comprises a first forwarding index determining subunit, a second forwarding index determining subunit and a forwarding index determining subunit, wherein the first forwarding index determining subunit is used for determining a first forwarding index of a user, the second forwarding index determining subunit is used for determining a second forwarding index of the user, and the forwarding index determining subunit is used for determining the forwarding index of the user according to the first forwarding index and the second forwarding index.
The first forwarding index is obtained using the following equation: where DYu represents the first forwarding index for user u; the second forwarding index is obtained in the following way: 1. for user u and its close neighbor nodes v and w, affinity is defined to reflect the degree of affinity between close neighbor nodes: in the formula, tu (v, w) represents the intimacy between nodes v and w, rw (v) represents the number of users v forwarding users w publishing messages, and rv (w) represents the number of users w forwarding users v publishing messages; 2. calculating the activity of the user: hu = a (u) × m (u) where Hu denotes the activity of user u and a (u) denotes the number of published messages of user u on average per day; 3. calculating a second forwarding index: wherein DEu represents a second forwarding index for user u; the forwarding index is determined using the following equation: in the formula, ZFu represents the forwarding index of the user u, and the user behavior forwarding is analyzed according to the forwarding index, specifically: the larger the forwarding index of the user is, the higher the probability that the user is forwarded is, for the users with the same forwarding index and the larger the second forwarding index is, the higher the probability that the user is forwarded is, and for the users with the same second forwarding index and the larger the first forwarding index is, the higher the probability that the user is forwarded is; analyzing the user behavior according to the user forwarding behavior, specifically: the higher the probability that a user is forwarded, the greater the user's influence on the network.
The utility model provides a network user behavior analysis system, still including the login system, the user platform system, central processing unit and alarm system, the login system is including password input module, the face shoots the module, fingerprint is beaten card module and iris and is shot the module, password input module is used for the user to input the character password, the face is shot the module and is used for shooting the picture and gather user face information and carry out follow-up discernment, fingerprint is beaten card module and is used for gathering user's fingerprint and carry out follow-up recognition of punching the card, central processing unit is including password check module, face identification module, fingerprint identification module and iris identification module, password check module is used for checking the character password that the user was input through password input module, face identification module is used for discerning the user face information that the user was shot the collection through face shooting module, fingerprint identification module is used for discerning the user's fingerprint of punching the card module collection through the fingerprint, iris identification module is used for discerning the user's eyeball iris information of shooting the collection through iris.
The interface authority module comprises a first layer interface module, a second layer interface module, a third layer interface module and a fourth layer interface module, wherein the first layer interface module, the second layer interface module, the third layer interface module and the fourth layer interface module are respectively connected with the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module in sequence.
The previous description of the embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A network user behavior analysis system is characterized by comprising a modeling module, a forwarding behavior analysis module and a user behavior analysis module, wherein the modeling module is used for establishing a social network model, the forwarding behavior analysis module is used for analyzing forwarding behaviors of users, the user behavior analysis module is used for analyzing user behaviors according to the forwarding behaviors of the users, and the modeling module adopts the following method to establish the social network model: representing the social network model as a binary group E = (U, B), wherein U represents a user node set, B represents an edge set, and if a user U and a user v in the user node set concern each other, an edge (U, v) exists between the user U and the user v, and the user U and the user v are adjacent nodes; the forwarding behavior analysis module comprises a forwarding probability calculation unit, a forwarding index determination unit and a forwarding behavior analysis unit, wherein the forwarding probability calculation unit is used for calculating the probability that the published information of the user is forwarded, the forwarding index determination unit is used for determining the forwarding index of the user according to the probability that the published information of the user is forwarded, and the forwarding behavior analysis unit is used for analyzing the forwarding of the user behavior according to the forwarding index.
2. The system according to claim 1, wherein the calculating the probability of forwarding the published information of the user specifically comprises: 1. representing all the adjacent node sets of the user u by using L (u), wherein for the user u and the adjacent node v of the user u, belonging to L (u), if a user w exists, so that w belongs to L (u) and w belongs to L (v), the user v is the close adjacent node of the user u, representing all the close adjacent node sets by using La (u), if the user w does not exist, so that w belongs to L (u) and w belongs to L (v), the user v is the loose adjacent node of the user u, and representing all the loose adjacent node sets by using Lb (u); 2. calculating the probability that the user is forwarded by the adjacent nodes: in the formula, pu (L (u)) represents the probability that the user u is forwarded by the adjacent node, m (u) represents the number of messages issued by the user u, ru (v) represents the number of messages issued by the user u forwarded by the user v, tu (v) represents the number of messages issued by the user u forwarded by the user v within a set time limit, and L (u) | represents the number of adjacent nodes of the user u; calculating the probability that the user is forwarded by the close adjacent node: in the formula, pu (La (u)) represents the probability that the user u is forwarded by its close neighboring node, | La (u) | represents the number of close neighboring nodes of the user u; calculating the probability that the user is forwarded by the loose adjacent nodes: in the formula, pu (Lb (u)) represents the probability that user u is forwarded by its loose neighbor node, and | Lb (u) | represents the number of loose neighbor nodes that user u has.
3. The system according to claim 2, wherein the forwarding index determining unit comprises a first forwarding index determining subunit, a second forwarding index determining subunit, and a forwarding index determining subunit, the first forwarding index determining subunit is configured to determine a first forwarding index of the user, the second forwarding index determining subunit is configured to determine a second forwarding index of the user, and the forwarding index determining subunit is configured to determine the forwarding index of the user according to the first forwarding index and the second forwarding index.
4. The system according to claim 3, wherein the first forwarding index is obtained by using the following formula: where DYu represents the first forwarding index for user u; the second forwarding index is obtained in the following manner: 1. for user u and its close neighbor nodes v and w, affinity is defined to reflect the degree of affinity between close neighbor nodes: in the formula, tu (v, w) represents the intimacy between nodes v and w, rw (v) represents the number of users v forwarding users v publishing messages, and rv (w) represents the number of users w forwarding users v publishing messages; 2. calculating the activity of the user: hu = a (u) × m (u) where Hu denotes the activity of user u and a (u) denotes the number of published messages of user u on average per day; 3. calculating a second forwarding index: wherein DEu represents a second forwarding index for user u; the forwarding index is determined using the following equation: in the formula, ZFu represents a forwarding index of the user u, and the user behavior forwarding is analyzed according to the forwarding index, specifically: the larger the forwarding index of the user is, the higher the probability that the user is forwarded is, for the users with the same forwarding index and the larger the second forwarding index is, the higher the probability that the user is forwarded is, and for the users with the same second forwarding index and the larger the first forwarding index is, the higher the probability that the user is forwarded is; the analyzing the user behavior according to the user forwarding behavior specifically comprises: the higher the probability that a user is forwarded, the greater the impact of the user in the network.
5. The network user behavior analysis system according to claim 1, further comprising a login system, a user platform system, a central processing unit and an alarm system, wherein the login system comprises a password input module, a face shooting module, a fingerprint card punching module and an iris shooting module, the password input module is used for inputting a character password by a user, the face shooting module is used for shooting a picture to collect face information of the user for subsequent identification, the fingerprint card punching module is used for collecting user fingerprints for subsequent card punching identification, the iris shooting module is used for shooting and collecting eyeball information of the user for subsequent identification, the central processing unit comprises a password checking module, a face recognition module, a fingerprint recognition module and an iris recognition module, the password checking module is used for checking the character password input by the password input module by the user, the face recognition module is used for recognizing the face information of the user shot and collected by the face shooting module, the fingerprint recognition module is used for recognizing the user fingerprints collected by the fingerprint card punching module, and the iris recognition module is used for recognizing the iris information collected by the user shot by the iris shooting module.
6. The system of claim 5, wherein the connection end of the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module is provided with a data comparison module, the connection end of the data comparison module is provided with a database, a data feedback module and an interface permission module, the data comparison module is used for comparing the user information identified by the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module with the database to distinguish authenticity, the interface permission module comprises a first layer interface module, a second layer interface module, a third layer interface module and a fourth layer interface module, and the first layer interface module, the second layer interface module, the third layer interface module and the fourth layer interface module are respectively connected with the password checking module, the face recognition module, the fingerprint recognition module and the iris recognition module in sequence.
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| CN107369099A (en) * | 2017-06-28 | 2017-11-21 | 深圳源广安智能科技有限公司 | A kind of user behavior analysis system towards social networks |
| CN114360132A (en) * | 2022-01-04 | 2022-04-15 | 云南电网有限责任公司信息中心 | Method and system for network security identity recognition |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107369099A (en) * | 2017-06-28 | 2017-11-21 | 深圳源广安智能科技有限公司 | A kind of user behavior analysis system towards social networks |
| CN114360132A (en) * | 2022-01-04 | 2022-04-15 | 云南电网有限责任公司信息中心 | Method and system for network security identity recognition |
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