Intelligent network security supervision system

Abstract

The invention discloses an intelligent network security supervision system, which belongs to the technical field of security supervision systems, and comprises the following steps of: before acquiring identity information and face information of a loan person, identifying browser information for opening a loan platform by the loan person, and judging the security of the browser information; after the identity information and the face information of the loan person are obtained, a first group of dynamic passwords is generated based on the time sequence and the identity information of the loan person, a second group of dynamic passwords is generated based on the timestamp, the face information and the body movement information, and the first group of dynamic passwords and the second group of dynamic passwords are sent to the security browser; and the loan platform receives the first group of dynamic passwords and the second group of dynamic passwords and distributes the first group of dynamic passwords and the second group of dynamic passwords to a loan person identity information auditing part and a face recognition information auditing part for auditing and rating respectively, and the auditing and rating of the loan person identity information auditing part and the face recognition information auditing part are combined for mean rating.

Description

Technical Field

[0001] This invention relates to a security monitoring system, and more particularly to an intelligent network security monitoring system, belonging to the technical field of security monitoring systems. Background Technology

[0002] Online lending is very common in financial business risk assessment. It is usually used to provide convenience for users to enter the market. However, the existing technology is prone to financial business risks when assessing personal online lending. Firstly, the browser in use may pose a risk of leaking customer information; Secondly, the assessment department of a financial platform is a department where it is easy for insiders to cause problems such as a large number of loans. Therefore, an intelligent network security monitoring system is designed to address the above problems. Summary of the Invention

[0003] The main objective of this invention is to provide an intelligent network security monitoring system.

[0004] The objective of this invention can be achieved by adopting the following technical solution: An intelligent network security monitoring system is applied to a lending platform, including the platform receiving borrower identity information and facial information, verifying the borrower's identity information and facial information, and rating the borrower; Its features are: Before obtaining the borrower's identity information and facial information, it also includes recognizing the browser information of the borrower when opening the loan platform; Determine the security of the browser information; Automatically redirects to a secure browser to receive the borrower's identity and facial information; After obtaining the borrower's identity information and facial information, the system also generates a first set of dynamic passwords based on the time sequence and the borrower's identity information. A second set of dynamic passwords is generated based on timestamps, facial information, and body movement information; The lending platform receives the first and second sets of dynamic passwords and distributes them to the lender's identity information verification department and facial recognition information verification department for review and rating, respectively. The average rating is calculated by combining the ratings from the lender's identity information verification department and the facial recognition information verification department.

[0005] Preferably, determining the security of the browser information includes the following steps: The network security connection identification module has built-in Python's certifi and requests libraries to automatically detect and verify SSL certificates on web pages; Insert the requests library into the network security connection identification module; Using a Python script and the requests library, we can obtain the SSL certificate of a webpage and verify its validity.

[0006] Preferably, automatically redirecting to a secure browser includes the following steps: Install Flask and use the requests library, which was created when checking the validity of certificates; Based on the created Python script, make adjustments and define two routes: one is originally used to check the validity of the SSL certificate, and the other is used to redirect to the security page; Execute the modified Python script in the server terminal to run the Flask application; Flask applications check the validity of SSL certificates. If the certificate is invalid, they automatically redirect to the / safe_page path to switch browsers automatically.

[0007] Preferably, the lender generates a random first set of dynamic passwords based on the input time sequence and the input lender identity information; Specifically as follows: The input time sequence, borrower's ID information, name information, and address information; Combine the time sequence with the borrower's identity information into a single string; Use the lender's identity information and time sequence as input to the key generator; The key is generated using the SHA-1 hash algorithm and HMAC. Create a TOTP generator; Generate the first set of dynamic passwords; Print the first set of dynamic passwords and send it to the loan platform's backend server.

[0008] Preferably, the SHA-1 hash algorithm uses the following method: Import the hashlib module, which provides cryptographic hash functions; Define the data to be hashed as a one-byte string containing the text "Some data to hash". Create a SHA-1 hash object sha1; The hashlib.sha1() function is used to create new SHA-1 hash objects; Update the hash object sha1 with the data using the update() method; The data is divided into blocks and added to the hash object step by step for hash calculation; Call the hexdigest() method to get the hexadecimal representation of the hash value; Returns a string containing the hexadecimal representation of the hash value; Print the hexadecimal representation of the hash value.

[0009] Preferably, the second set of dynamic passwords is generated based on timestamps, facial information, and body movement information as follows: When a user registers, their facial image is captured using a facial recognition device; The captured facial images are processed to extract features and generate a unique biometric template; At the same time, the system generates a key for the subsequent generation of a second set of dynamic passwords; Once a key is generated, it is transmitted to the second dynamic password generation module. The second dynamic password generation module combines the timestamp with the user's facial expressions and body movements, and uses a predefined algorithm to generate the second dynamic password.

[0010] Preferably, the lending platform receives the first and second sets of dynamic passwords and distributes them to the lender's identity information verification department and facial recognition information verification department for review and rating, as follows: During the assessment and review process, the first set of dynamic passwords, generated based on the time sequence, the borrower's ID information, name information, and address information, is entered to decrypt the borrower's identity information. The loan amount is rated based on the borrower's credit rating, bank credit history, and other information retrieved and analyzed according to the borrower's identity information. During the evaluation and review process, a second set of dynamic passwords, generated based on timestamps, user facial expressions, and body movements, is used to decrypt the facial capture information. Then, based on this information, the borrower's credit rating, bank credit history, and other information are retrieved and analyzed to rate the loan amount.

[0011] Preferably, the following systems are also included; The network security connection identification module is used to identify the security of the browser used to log in to the loan platform and automatically redirect to a secure browser; The dynamic password generation module is used to generate two different sets of dynamic passwords from the information collected by the personal information input module and the facial recognition module; The dynamic password distribution module distributes dynamic passwords to the personal information verification module and the facial recognition information verification module.

[0012] Beneficial technical effects of the present invention: The present invention provides an intelligent network security monitoring system, which, before obtaining the borrower's identity information and facial information, also includes identifying the browser information of the borrower who opened the loan platform, determining the security of the browser information, and automatically redirecting to a secure browser to receive the borrower's identity information and facial information; After obtaining the borrower's identity and facial information, the process includes generating a first dynamic password based on time sequence and the borrower's identity information, and generating a second dynamic password based on timestamps, facial information, and body language information. The lending platform receives the first and second dynamic passwords and distributes them to the borrower's identity information review department and the facial recognition information review department for review and rating respectively. The review ratings from the borrower's identity information review department and the facial recognition information review department are combined to obtain an average rating. Therefore, by conducting separate assessments, the problem of inflating loan amounts due to internal independent assessments is avoided. In addition, the transmission of dynamic passwords also prevents data leakage and theft during transmission. Attached Figure Description

[0013] Figure 1 This is a system diagram of a preferred embodiment of an intelligent network security monitoring system according to the present invention.

[0014] Figure 2 This is a flowchart of a preferred embodiment of an intelligent network security monitoring system according to the present invention. Detailed Implementation

[0015] To enable those skilled in the art to understand the technical solution of the present invention more clearly, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0016] like Figure 1-2 As shown, after a borrower opens the loan platform, the present invention provides a built-in network security connection identification module for the loan platform. The network security connection identification module is used to automatically identify the browser used by the borrower to open the loan platform and determine whether the browser is a secure browser. Specifically, this involves checking the URL address to see if the platform's URL address (also known as the domain name) starts with "https: / / ". If it does, it means that the website uses the SSL protocol for encrypted transmission, which ensures that a secure browser is used when making a loan, providing a basic guarantee against data leakage. During the identification process, this invention adopts an automated identification method. The network security connection identification module has built-in Python's certifi and requests libraries to automatically detect and verify SSL certificates on web pages. Insert the requests library into the network security connection identification module; A Python script using the requests library is used to obtain the SSL certificate of a webpage and verify its validity. Specifically as follows: Use the Session object from the requests library to create a request session and set a timeout. Use the GET method to retrieve the webpage content of the specified URL, and use the peercert property to obtain the SSL certificate; Use the verify_ssl_certificate function to verify the validity of the certificate. If the certificate is valid, output "SSL certificate valid"; otherwise, output "SSL certificate invalid".

[0017] When the certificate is valid, the browser can be used normally; when the certificate is invalid, it will automatically redirect to other websites with valid SSL certificates. On the server side, Python and the Flask framework are used to detect whether an SSL certificate is valid. Install Flask and use the requests library, which was created when checking the validity of certificates; Further adjustments were made to the created Python script, defining two routes: one originally used to check the validity of the SSL certificate, and the other used to redirect to the security page; Execute the modified Python script in the terminal to run the Flask application; Flask applications can check the validity of SSL certificates and automatically redirect to the / safe_page path if the certificate is invalid, thus enabling automatic browser switching.

[0018] Once the switch is complete, borrowers access the lending platform through a secure browser, reducing the risk of data leakage caused by the browser.

[0019] When applying for a loan through a loan platform, borrowers need to fill in specific borrower identity information and undergo facial recognition. The loan platform then reviews the data obtained from the borrower's identity information and facial recognition, and disburses the loan amount after the review is completed.

[0020] During this process, the present invention generates a dynamic password and sends it to the lending platform after the borrower's identity information is entered. Since borrowers enter different specific borrower identity information and in different time sequences, the lending platform generates a random key based on the order of the input and the borrower's identity information. Specifically as follows: The input time sequence, borrower's ID information, name information, and address information; Time order = str(datetime.datetime.now().strftime("%Y%m%d")) # Current date, in YYYYMMDD format; ID card number = "ID card number"; name="name"; address="home address"; # Combine the time sequence with the borrower's identity information into a single string: String = f"{Time Sequence}-{ID Number}-{Name}-{Address}"; #Use the lender's identity information and time sequence as input to the key generator; #Use SHA-1 hash algorithm and HMAC to generate the key; key = base64.b64encode(hmac.new(message string.encode('utf-8'),hashlib.sha1).digest()); # Create a TOTP generator (time-based dynamic password generator); totp=pyotp.TOTP(key, interval=30) # totp generator, the interval parameter specifies the time interval (default is 30 seconds); # Generate a dynamic password print(totp.now()); # Print the current dynamic password; The above method is used to generate a key based on the input time sequence, the borrower's ID information, name information, and address information, and then send it to the loan platform's backend server.

[0021] The SHA-1 hash algorithm uses the following method: Import the hashlib module, which provides various cryptographic hash functions; Define the data to be hashed, which is a byte string containing the text "Some data tohash"; Create a SHA-1 hash object sha1; The hashlib.sha1() function is used to create a new SHA-1 hash object; Update the hash object sha1 with the data using the update() method; This method divides the data into blocks and adds them step by step to the hash object for hash calculation; Call the hexdigest() method to get the hexadecimal representation of the hash value; This method returns a string containing the hexadecimal representation of the hash value; Print the hexadecimal representation of the hash value.

[0022] During the loan application process, borrowers also need to have their facial information collected through facial recognition. The dynamic password is established based on facial information during facial recognition as follows: When a user registers, their facial image is captured using a facial recognition device; The captured facial images are processed to extract features and generate a unique biometric template; At the same time, the system will generate a key for subsequent dynamic password generation; Once a key is generated, it is transmitted to the dynamic password generation module. The dynamic password generation module combines the timestamp with the user's facial expressions and body movements to generate a one-time password (OTP) using a predefined algorithm (such as HMAC-SHA1 or TOTP). After obtaining two sets of dynamic passwords, the loan platform's backend distributes them to the group responsible for the borrower's identity information and the facial recognition group. To avoid information leakage issues, since it is easy for one department to control all the borrower's information, which could lead to subjective bias and inaccurate assessment of the borrower's loan amount, and since both passwords are dynamically generated and used only once, the two departments conduct separate assessments and reviews during the evaluation process. During the assessment and review process, a dynamic password generated based on the time sequence, the borrower's ID card information, name information, and address information is entered to decrypt the borrower's identity information. Then, based on the borrower's identity information, the borrower's credit rating and bank credit record information are retrieved and analyzed to rate the loan amount. During the assessment and review process, a dynamic password generated based on timestamps, user facial expressions, and body movements is used to decrypt facial image information. Then, based on this information, the borrower's credit rating, bank credit history, and other information are retrieved and analyzed to rate the loan amount. The credit rating is determined by combining the credit rating based on time sequence, borrower's ID information, name information, and address information with the credit rating based on the user's facial expressions and body language information. The final rating is generated by averaging these factors to determine the loan amount.

[0023] During the assessment and review process, the loan rating assessment is based on the borrower's credit history, debt status, debt-to-income ratio, and past default records.

[0024] The assessment results are based on the borrower's overall risk level and are classified into different risk levels, which are divided into nine levels: AAA, AA, A, BBB, BB, B, CCC, CC, and C. Once the rating system is established, the information is imported into the borrower information update module. This module allows for regular updates to borrower information, enabling periodic rating assessments and enhancing the effectiveness of oversight.

[0025] Specifically, the following approach is used to assess the borrower's credit history, debt status, debt-to-income ratio, and past default records: Credit history assessment: Collecting a borrower's credit report, including credit history, account activity, and payment records.

[0026] Analyze borrowers' credit scores, such as FICO scores or other credit scoring systems.

[0027] Check for negative credit records such as late payments, debts, or bankruptcy.

[0028] Assess the number and types of credit accounts and their impact on borrowers' creditworthiness.

[0029] Debt assessment: Calculate the borrower's total debt burden, including credit card debt, student loans, and other loans.

[0030] Analyze the debt-to-income ratio, such as the debt-to-income ratio (DTI), to determine whether a borrower has the ability to repay new debt.

[0031] Assess the debt structure and maturity date, and understand the debt repayment plan and potential liquidity risks.

[0032] Debt-to-income ratio assessment: Calculate the borrower's debt-to-income ratio and compare it with industry standards and internal bank guidelines.

[0033] Consider the borrower's stable income and potential future income sources.

[0034] Analyze the borrower’s spending patterns, including daily living expenses, savings, and other financial commitments.

[0035] Past default record assessment: Review the borrower's history of defaults, including records of late payments, debt arrears, or defaulted loans.

[0036] Understand the reasons and background of the default, and assess whether it is a systemic problem or an isolated event.

[0037] Consider the long-term impact of default on the borrower’s current financial and creditworthiness.

[0038] Comprehensive Risk Assessment: Taking all the above information into account, assess the borrower's overall credit risk.

[0039] Credit risk can be quantified using risk scoring models and categorized into corresponding credit risk levels.

[0040] Based on the assessment results, appropriate credit strategies are developed, such as setting interest rates, determining loan amounts, requiring collateral, or developing specific repayment plans.

[0041] The above description is merely a further embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. An intelligent network security monitoring system applied to a lending platform, comprising the platform receiving borrower identity information and facial information, verifying the borrower's identity information and facial information, and rating the borrower; Its features are: Before obtaining the borrower's identity information and facial information, it also includes recognizing the browser information of the borrower when opening the loan platform; Determine the security of the browser information; Automatically redirects to a secure browser to receive the borrower's identity and facial information; After obtaining the borrower's identity information and facial information, the system also generates a first set of dynamic passwords based on the time sequence and the borrower's identity information. A second set of dynamic passwords is generated based on timestamps, facial information, and body movement information; The lending platform receives the first and second sets of dynamic passwords and distributes them to the lender's identity information verification department and facial recognition information verification department for review and rating, respectively. The average rating is calculated by combining the ratings from the lender's identity information verification department and the facial recognition information verification department.

2. The intelligent network security monitoring system according to claim 1, characterized in that: Determining the security of a browser's information involves the following steps: The network security connection identification module has built-in Python's certifi and requests libraries to automatically detect and verify SSL certificates on web pages; Insert the requests library into the network security connection identification module; Using a Python script and the requests library, we can obtain the SSL certificate of a webpage and verify its validity.

3. The intelligent network security monitoring system according to claim 2, characterized in that: Automatically redirecting to a secure browser involves the following steps: Install Flask and use the requests library, which is built to check the validity of certificates; Based on the created Python script, make adjustments and define two routes: one is originally used to check the validity of the SSL certificate, and the other is used to redirect to the security page; Execute the modified Python script in the server terminal to run the Flask application; Flask applications check the validity of SSL certificates. If the certificate is invalid, they automatically redirect to the / safe_page path to switch browsers automatically.

4. The intelligent network security monitoring system according to claim 1, characterized in that: The lender generates a random first set of dynamic passwords based on the input time sequence and the input lender identity information; Specifically as follows: The input time sequence, borrower's ID information, name information, and address information; Combine the time sequence with the borrower's identity information into a single string; Use the lender's identity information and time sequence as input to the key generator; The key is generated using the SHA-1 hash algorithm and HMAC. Create a TOTP generator; Generate the first set of dynamic passwords; Print the first set of dynamic passwords and send it to the loan platform's backend server.

5. The intelligent network security monitoring system according to claim 4, characterized in that: The SHA-1 hash algorithm uses the following method: Import the hashlib module, which provides cryptographic hash functions; Define the data to be hashed as a one-byte string containing the text "Some data to hash". Create a SHA-1 hash object sha1; The hashlib.sha1() function is used to create new SHA-1 hash objects; Update the hash object sha1 with the data using the update() method; The data is divided into blocks and added to the hash object step by step for hash calculation; Call the hexdigest() method to get the hexadecimal representation of the hash value; Returns a string containing the hexadecimal representation of the hash value; Print the hexadecimal representation of the hash value.

6. The intelligent network security monitoring system according to claim 1, characterized in that: The second dynamic password is generated based on timestamps, facial information, and body movement information as follows: When a user registers, their facial image is captured using a facial recognition device; The captured facial images are processed to extract features and generate a unique biometric template; At the same time, the system generates a key for the subsequent generation of a second set of dynamic passwords; Once a key is generated, it is transmitted to the second dynamic password generation module. The second dynamic password generation module combines the timestamp with the user's facial expressions and body movements, and uses a predefined algorithm to generate the second dynamic password.

7. The intelligent network security monitoring system according to claim 1, characterized in that: The lending platform receives the first and second sets of dynamic passwords and distributes them to the borrower's identity information verification department and facial recognition information verification department for review and rating, as detailed below: During the assessment and review process, the first set of dynamic passwords, generated based on the time sequence, the borrower's ID information, name information, and address information, is entered to decrypt the borrower's identity information. The loan amount is rated based on the borrower's credit rating, bank credit history, and other information retrieved and analyzed according to the borrower's identity information. During the evaluation and review process, a second set of dynamic passwords, generated based on timestamps, user facial expressions, and body movements, is used to decrypt the facial capture information. Then, based on this information, the borrower's credit rating, bank credit history, and other information are retrieved and analyzed to rate the loan amount.

8. The intelligent network security monitoring system according to claim 1, characterized in that: It also includes the following systems; The network security connection identification module is used to identify the security of the browser used to log in to the loan platform and automatically redirect to a secure browser; The dynamic password generation module is used to generate two different sets of dynamic passwords from the information collected by the personal information input module and the facial recognition module; The dynamic password distribution module distributes dynamic passwords to the personal information verification module and the facial recognition information verification module.

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