CN113542232A - Website data safety protection system based on big data - Google Patents
Website data safety protection system based on big data Download PDFInfo
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- CN113542232A CN113542232A CN202110697438.XA CN202110697438A CN113542232A CN 113542232 A CN113542232 A CN 113542232A CN 202110697438 A CN202110697438 A CN 202110697438A CN 113542232 A CN113542232 A CN 113542232A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
- H04L63/083—Network architectures or network communication protocols for network security for authentication of entities using passwords
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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Abstract
The invention discloses a website data security protection system based on big data, belongs to the field of big data, relates to a website data security protection technology, and is used for solving the problem that the existing single data protection mechanism cannot well protect data. The invention is provided with a data acquisition module, a behavior analysis module, a registration login module, a processor, an identity authentication module, a protection prompt module and a data storage module, wherein a user habit model is established through the behavior analysis module according to user habits, comparison with the user habit model is carried out according to website behavior data acquired by the data acquisition module in real time, and when the data is inconsistent with the data stored in the user habit model, different verification modes are adopted for identity authentication so as to ensure the safety of the data.
Description
Technical Field
The invention belongs to the field of big data, relates to a website data safety protection technology, and particularly relates to a website data safety protection system based on big data.
Background
Information security or data security has two opposite meanings: firstly, the safety of data is mainly characterized in that a modern cryptographic algorithm is adopted to carry out active protection on the data, such as data confidentiality, data integrity, bidirectional identity authentication and the like, and secondly, the safety of data protection is mainly characterized in that a modern information storage means is adopted to carry out active protection on the data, such as means of disk arrays, data backup, remote disaster recovery and the like are adopted to ensure the safety of the data, the data safety is an active contained measure, the safety of the data must be based on a reliable cryptographic algorithm and a safety system, and the two types of symmetric algorithms and public key cryptographic systems are mainly adopted.
In the prior art, different persons in a company have different management authorities for confidential documents or websites in the company, and can log in to a website for acquiring or downloading data in a specific time period, an existing website data security protection system does not protect data according to habits of different managers, most of the existing website data security protection system carries out security protection through a single account and a single password, most of the existing computers have settings for automatically saving the password, and therefore the single data protection mechanism is not suitable for the development at the moment.
Therefore, a website data security protection system based on big data is provided.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a website data security protection system based on big data, which is used for solving the problem that the existing single data protection mechanism can not well protect data. The invention is provided with a data acquisition module, a behavior analysis module, a registration login module, a processor, an identity authentication module, a protection prompt module and a data storage module, wherein a user habit model is established through the behavior analysis module according to user habits, comparison with the user habit model is carried out according to website behavior data acquired by the data acquisition module in real time, and when the data is inconsistent with the data stored in the user habit model, different verification modes are adopted for identity authentication so as to ensure the safety of the data.
The purpose of the invention can be realized by the following technical scheme:
a website data security protection system based on big data comprises a data acquisition module, a behavior analysis module, a registration login module, a processor, an identity authentication module, a protection prompt module and a data storage module;
the data acquisition module is used for acquiring website login behavior data of the registered user, and the data acquisition module sends the acquired website login behavior data of the registered user to the data storage module for data storage;
the data storage module is used for storing the website login behavior data of the registered user sent by the data acquisition module and sending the website login behavior data of the registered user to the behavior analysis module for behavior analysis;
the behavior analysis module is used for performing behavior analysis on the website login behavior data stored in the data storage module and establishing a user habit model according to the website login behavior data of the registered user;
the data acquisition module is also used for acquiring website login behavior data of the registered user in real time and sending the website login behavior data acquired in real time to the processor;
and the processor processes the website login behavior data after receiving the website login behavior data sent by the data acquisition module, and performs website data security protection by combining the identity authentication module and the protection prompting module.
Further, the website login behavior data includes browsing duration, modification records, download records, and login records.
Further, the process that the behavior analysis module establishes the user habit model according to the website login behavior data of the registered user comprises the following steps:
the behavior analysis module acquires a user code i of a registered user, and extracts website login behavior data of the registered user with the user code i from the data storage module; respectively demarcating Ttij, Clij, Lxij and Xlij; wherein j represents the number of times of logging in the website by the registered user;
the behavior analysis module collects and sorts out a login time range (Tti0, Tti1), a browsing time range (Cli0, Cli1), a time modification coefficient Xgi and a time download coefficient Xzi of the registered user;
and binding the login time range (Tti0, Tti1), the browsing time range (Cli0, Cli1), the time modification coefficient Xgi and the single-download coefficient Xzi with the registered user i to establish a user habit model.
Further, the time modification coefficient Xgi is calculated as: acquiring the time for a registered user to log in a website and modify the website in a week, and respectively calculating the ratio of the times of modifying data of the user from Monday to Sunday to the total modification times;
the time download coefficient Xzi is calculated by obtaining the time for the registered user to log in the website and download in one week, and calculating the ratio of the download times of the user from Monday to Sunday to the total download times.
Further, the processing procedure of the processor for the website login behavior data comprises the following steps:
the method comprises the following steps: when the data acquisition module acquires that the registered user carries out website login, acquiring the identity of the registered user, acquiring the login time of the registered user carrying out website login, and sending the login time to the processor;
step two: the processor marks the login time as Tti, the processor acquires a user habit model and compares the login time with a login time range (Tti0, Tti1), when the current login time is not in the login time range, the processor sends a prompt signal to the protection prompt module, the protection prompt module carries out risk prompt on a display screen, and a registered user can click to remove the risk prompt through a mouse;
step three: the data acquisition module acquires browsing time of a registered user in real time, synchronously sends the browsing time to the processor, and the processor marks the browsing time as Cli and compares the browsing time with the Cli;
step four: when the browsing duration exceeds the browsing duration range, namely Cli is greater than Cli1, the processor sends a first protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal performs system popup after receiving the first protection signal; after the identity authentication module receives the first protection signal, the identity authentication module pops up an account and password verification by combining with the protection prompting module, and a registered user browses a website by re-inputting an account and a password;
step five: when a registered user carries out modification or downloading operation, the processor acquires a time modification coefficient or a time downloading coefficient of the current working day, when the time modification coefficient or the time downloading coefficient is larger than a time downloading coefficient threshold or a time modification coefficient threshold, the processor sends a second protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal carries out system popup after receiving the second protection signal; after the identity authentication module receives the second protection signal, the identity authentication module pops up the account number, the password and the secret protection question verification in combination with the protection prompting module, and the registered user modifies or downloads website data by re-inputting the account number and the password and answering the secret protection question.
Further, when the user makes an account password input error or a password protection question is answered three times incorrectly, the processor automatically closes the current website page.
Further, the privacy problem is set by the user at the time of registration.
Compared with the prior art, the invention has the beneficial effects that:
1. the behavior analysis module acquires a user code i of a registered user, and extracts website login behavior data of the registered user with the user code i from the data storage module; respectively demarcating Ttij, Clij, Lxij and Xlij; the behavior analysis module collects and sorts out a login time range (Tti0, Tti1), a browsing time range (Cli0, Cli1), a time modification coefficient Xgi and a time download coefficient Xzi of the registered user; and binding the login time range (Tti0, Tti1), the browsing time range (Cli0, Cli1), the time modification coefficient Xgi and the single-download coefficient Xzi with the registered user i to establish a user habit model. The concept based on big data user habits provides a reference for the safety protection of data.
2. When the data acquisition module acquires that the registered user carries out website login, acquiring the identity of the registered user, acquiring the login time of the registered user carrying out website login, and sending the login time to the processor; the processor marks the login time as Tti, the processor acquires a user habit model and compares the login time with a login time range (Tti0, Tti1), when the current login time is not in the login time range, the processor sends a prompt signal to the protection prompt module, the protection prompt module carries out risk prompt on a display screen, and a registered user can click to remove the risk prompt through a mouse; the data acquisition module acquires browsing time of a registered user in real time, synchronously sends the browsing time to the processor, and the processor marks the browsing time as Cli and compares the browsing time with the Cli; when the browsing duration exceeds the browsing duration range, namely Cli is greater than Cli1, the processor sends a first protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal performs system popup after receiving the first protection signal; according to the comparison with the user habit model, different verification modes are adopted for reading data, modifying data and downloading data, and website data safety protection is better completed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a website data security protection system based on big data according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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.
As shown in fig. 1, a website data security protection system based on big data includes a data acquisition module, a behavior analysis module, a registration and login module, a processor, an identity authentication module, a protection prompt module, and a data storage module;
the data acquisition module is used for acquiring website login behavior data of the registered user, wherein the website login behavior data comprises browsing duration, modification records, downloading records, login records and the like, and the data acquisition module is used for sending the acquired website login behavior data of the registered user to the data storage module for data storage;
the data storage module is used for storing the website login behavior data of the registered user sent by the data acquisition module and sending the website login behavior data of the registered user to the behavior analysis module for behavior analysis;
the behavior analysis module is used for performing behavior analysis on the website login behavior data stored in the data storage module and establishing a user habit model according to the website login behavior data of the registered user, and specifically, the behavior analysis module analyzes the website login behavior data of the registered user and comprises the following steps:
the method comprises the following steps: the behavior analysis module acquires a user code i of a registered user, extracts website login behavior data of the registered user with the user code i from the data storage module, and calibrates login time, browsing duration, modification records and download records in website login behaviors;
step two: respectively marking the login time, the browsing duration, the modification record and the download record as Ttij, Clij, Lxij and Xlij; j represents the number of times that the registered user logs in the website, namely j equals 1 when logging in for the first time, j equals 2 when logging in for the second time, j is a positive integer, and j equals 1, 2 … … m;
step three: when the registered user downloads data, Xlij is 1, and when the registered user does not download data, Xlij is 0;
step four: the behavior analysis module respectively collects and collates the website login behavior data stored in the data storage module, and calculates a login time range (Tti0, Tti1), a browsing time range (Cli0, Cli1), a time modification coefficient Xgi and a time download coefficient Xzi of the registered user;
step five: and binding the login time range (Tti0, Tti1), the browsing time range (Cli0, Cli1), the time modification coefficient Xgi and the single-download coefficient Xzi with the registered user i to establish a user habit model.
It should be noted that the time modification coefficient Xgi is calculated by obtaining the time for the registered user to log in the website and modify the website in one week, and calculating the ratio of the number of times of modifying data by the user from monday to sunday to the total number of modifications, where the monday to sunday correspond to different time modification coefficients;
the time downloading coefficient Xzi is calculated by obtaining the time for the registered user to log in the website and download in one week, and calculating the ratio of the times of downloading data of the user from Monday to Sunday to the total downloading times, wherein the time from Monday to Sunday corresponds to different time downloading coefficients;
it should be noted that, the behavior analysis module also sets a time download coefficient threshold and a time modification coefficient threshold.
The registration login module is used for inputting personal information by a user to perform login registration, wherein the personal information comprises a user name, a user age, a user position and a user working age; the data storage module stores the personal information of the user who successfully registers, marks the user who successfully registers as the registered user, and marks the registered user as i according to the time sequence, wherein i is a positive integer and is 1, 2 … … n.
It should be noted that the login module is further configured to automatically generate a login account and a login password according to personal information of a registered user, where the login password is registered for self-modification;
the data acquisition module is also used for acquiring website login behavior data of the registered user in real time and sending the website login behavior data acquired in real time to the processor;
the processor processes the website login behavior data after receiving the website login behavior data sent by the data acquisition module, and the specific processing process comprises the following steps:
the method comprises the following steps: when the data acquisition module acquires that the registered user carries out website login, acquiring the identity of the registered user, acquiring the login time of the registered user carrying out website login, and sending the login time to the processor;
step two: the processor marks the login time as Tti, the processor acquires a user habit model and compares the login time with a login time range (Tti0, Tti1), when the current login time is not in the login time range, the processor sends a prompt signal to the protection prompt module, the protection prompt module carries out risk prompt on a display screen, and a registered user can click to remove the risk prompt through a mouse;
step three: the data acquisition module acquires browsing time of a registered user in real time, synchronously sends the browsing time to the processor, and the processor marks the browsing time as Cli and compares the browsing time with the Cli;
step four: when the browsing duration exceeds the browsing duration range, namely Cli is greater than Cli1, the processor sends a first protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal performs system popup after receiving the first protection signal; after the identity authentication module receives the first protection signal, the identity authentication module pops up an account and password verification by combining with the protection prompting module, and a registered user browses a website by re-inputting an account and a password;
step five: when a registered user carries out modification or downloading operation, the processor acquires a time modification coefficient or a time downloading coefficient of the current working day, when the time modification coefficient or the time downloading coefficient is larger than a time downloading coefficient threshold or a time modification coefficient threshold, the processor sends a second protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal carries out system popup after receiving the second protection signal; after the identity authentication module receives the second protection signal, the identity authentication module pops up the account number, the password and the secret protection question verification in combination with the protection prompting module, and the registered user modifies or downloads website data by re-inputting the account number and the password and answering the secret protection question.
It should be noted that, when the user makes an account password input error or answers a password protection question three times incorrectly, the processor automatically closes the current website page.
The secret protection problem is set by the user during registration.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: the data acquisition module is used for acquiring website login behavior data of the registered user, and the data acquisition module transmits the acquired website login behavior data of the registered user to the data storage module for data storage; the data storage module is used for storing the website login behavior data of the registered user sent by the data acquisition module and sending the website login behavior data of the registered user to the behavior analysis module for behavior analysis; the behavior analysis module is used for performing behavior analysis on the website login behavior data stored in the data storage module and establishing a user habit model according to the website login behavior data of the registered user; the data acquisition module is also used for acquiring website login behavior data of the registered user in real time and sending the website login behavior data acquired in real time to the processor; and after receiving the website login behavior data sent by the data acquisition module, the processor processes the website login behavior data and performs website data security protection by combining the identity authentication module and the protection prompting module.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
It will also be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order. Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.
Claims (7)
1. A website data security protection system based on big data is characterized by comprising a data acquisition module, a behavior analysis module, a registration and login module, a processor, an identity authentication module, a protection prompt module and a data storage module;
the data acquisition module is used for acquiring website login behavior data of the registered user, and the data acquisition module sends the acquired website login behavior data of the registered user to the data storage module for data storage;
the data storage module is used for storing the website login behavior data of the registered user sent by the data acquisition module and sending the website login behavior data of the registered user to the behavior analysis module for behavior analysis;
the behavior analysis module is used for performing behavior analysis on the website login behavior data stored in the data storage module and establishing a user habit model according to the website login behavior data of the registered user;
the data acquisition module is also used for acquiring website login behavior data of the registered user in real time and sending the website login behavior data acquired in real time to the processor;
and the processor processes the website login behavior data after receiving the website login behavior data sent by the data acquisition module, and performs website data security protection by combining the identity authentication module and the protection prompting module.
2. The big data-based website data security protection system according to claim 1, wherein the website login behavior data includes browsing duration, modification records, download records, and login records.
3. The big data-based website data security protection system according to claim 1, wherein the process of the behavior analysis module building the user habit model according to the website login behavior data of the registered user comprises:
the behavior analysis module acquires a user code i of a registered user, and extracts website login behavior data of the registered user with the user code i from the data storage module; respectively demarcating Ttij, Clij, Lxij and Xlij; wherein j represents the number of times of logging in the website by the registered user;
the behavior analysis module collects and sorts out a login time range (Tti0, Tti1), a browsing time range (Cli0, Cli1), a time modification coefficient Xgi and a time download coefficient Xzi of the registered user;
and binding the login time range (Tti0, Tti1), the browsing time range (Cli0, Cli1), the time modification coefficient Xgi and the single-download coefficient Xzi with the registered user i to establish a user habit model.
4. The big-data-based website data security protection system according to claim 3, wherein the time modification coefficient Xgi is calculated by: acquiring the time for a registered user to log in a website and modify the website in a week, and respectively calculating the ratio of the times of modifying data of the user from Monday to Sunday to the total modification times;
the time download coefficient Xzi is calculated as: and acquiring the time for logging in the website and downloading by the registered user in one week, and respectively calculating the ratio of the data downloading times of the user from Monday to Sunday to the total downloading times.
5. The big data-based website data security protection system according to claim 1, wherein the processing of the website login behavior data by the processor comprises the following steps:
the method comprises the following steps: when the data acquisition module acquires that the registered user carries out website login, acquiring the identity of the registered user, acquiring the login time of the registered user carrying out website login, and sending the login time to the processor;
step two: the processor marks the login time as Tti, the processor acquires a user habit model and compares the login time with a login time range (Tti0, Tti1), when the current login time is not in the login time range, the processor sends a prompt signal to the protection prompt module, the protection prompt module carries out risk prompt on a display screen, and a registered user can click to remove the risk prompt through a mouse;
step three: the data acquisition module acquires browsing time of a registered user in real time, synchronously sends the browsing time to the processor, and the processor marks the browsing time as Cli and compares the browsing time with the Cli;
step four: when the browsing duration exceeds the browsing duration range, namely Cli is greater than Cli1, the processor sends a first protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal performs system popup after receiving the first protection signal; after the identity authentication module receives the first protection signal, the identity authentication module pops up an account and password verification by combining with the protection prompting module, and a registered user browses a website by re-inputting an account and a password;
step five: when a registered user carries out modification or downloading operation, the processor acquires a time modification coefficient or a time downloading coefficient of the current working day, when the time modification coefficient or the time downloading coefficient is larger than a time downloading coefficient threshold or a time modification coefficient threshold, the processor sends a second protection signal to the identity authentication module and the protection prompting module, and the protection prompting signal carries out system popup after receiving the second protection signal; after the identity authentication module receives the second protection signal, the identity authentication module pops up the account number, the password and the secret protection question verification in combination with the protection prompting module, and the registered user modifies or downloads website data by re-inputting the account number and the password and answering the secret protection question.
6. The big-data-based website data security protection system according to claim 5, wherein the processor automatically closes the current website page when the user makes an account password input error or a password security question answer three times an error.
7. The big-data-based website data security system according to claim 5, wherein the privacy problem is set by a user at registration.
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Application publication date: 20211022 |