A method for arbitrary computer browser fingerprinting

Abstract

The application discloses a kind of arbitrary computer browser fingerprint simulation methods, it is embedded target browser by JS acquisition script, using dynamic scanning mechanism collects full quantity fingerprint data and records dynamic change timing, secondly, the unstructured raw data is carried out JSON standardization processing and multidimensional check, generate the uniform format fingerprint file with timing mark, then realize the safe anonymous transmission of fingerprint file by high anonymous proxy transmission channel and asymmetric encryption algorithm, guarantee data integrity, subsequently based on containerized operating environment constructs target fingerprint browser, replaces original fingerprint information by priority and timing mark through the fingerprint injection module integrated in kernel, finally, consistency check and dynamic correction are carried out, the application supports multi-browser kernel adaptation, multiple targets parallel simulation and traffic confusion, through full quantity data acquisition, timing injection and dynamic optimization, realize the high simulation degree of browser fingerprint simulation.

Description

Technical Field

[0001] This invention relates to the field of network security technology, and more specifically, to a method for simulating fingerprints in any computer browser. Background Technology

[0002] In the digital age, browser fingerprints, which are a set of device features composed of various unique identifiers such as hardware model, system version, plugin information, Canvas rendering features, and User-Agent string, have become a core technical means for websites to identify users and distinguish device terminals. With its stability and uniqueness, browser fingerprints are widely used in scenarios such as user behavior analysis, account security verification, and anti-crawler protection, providing support for the precision and security of network services.

[0003] However, existing browser fingerprint modification technologies have significant limitations in practical applications such as legitimate and compliant device masquerading testing, network security verification, and multi-scenario compatibility testing. On one hand, existing technologies lack standardized data processing and transmission mechanisms, resulting in chaotic fingerprint data formats, difficulty in ensuring integrity, and the risk of data loss or tampering during transmission, leading to discrepancies between the simulated fingerprint and the target browser fingerprint. On the other hand, existing simulation technologies do not fully consider the dynamic changes in fingerprints and their correlation with the browser's runtime environment. They simply replace static fingerprint parameters, ignoring the coordinated matching of dynamic factors such as network IP, operation timing, and hardware interface characteristics. This makes simulated fingerprints easily detectable by websites through multi-dimensional cross-verification, failing to meet the requirements for highly realistic device masquerading and struggling to cope with diverse simulation needs in complex network environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method for simulating fingerprints in any computer browser, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for simulating fingerprints in any computer browser, comprising the following steps: S1. After developing a JS collection script and embedding it into the target browser, collect the full amount of fingerprint data through a dynamic scanning mechanism, and record the dynamic change sequence of the fingerprint data. S2. Standardize the unstructured raw data using a JSON format converter, classify and organize it according to the preset field structure and fill in the missing fields, use data validation algorithms to verify the legality and format consistency of the fields, and generate a unified format JSON fingerprint file with time-series markers. S3. Construct a highly anonymous proxy transmission channel. Use a secure data transmission tool to encrypt the JSON fingerprint file with an asymmetric encryption algorithm. Verify data integrity in real time during transmission. If data loss or corruption is detected, trigger retransmission. At the same time, hide the characteristics of the transmission link. S4. Build a target fingerprint browser based on a containerized runtime environment, simulate the hardware, network and software configuration environment of the target browser, receive JSON fingerprint files through the fingerprint injection module integrated in its kernel, replace the original fingerprint information of the browser one by one according to the time sequence mark, and update the dynamic features of the browser runtime synchronously. S5. Perform consistency verification between the replaced fingerprint information and the target browser fingerprint. If there is a discrepancy, correct it dynamically. After correction, persistently save the fingerprint information to the browser configuration file and generate a fingerprint simulation log.

[0006] Preferably, in step S1, the full fingerprint data includes at least five of the following: browser size, network IP, proxy IP, browser font, browser language, browser opening time, time zone information, User-Agent string, screen resolution, and browser kernel type. The dynamic change sequence includes the update timestamp, change frequency, and associated feature change records of each fingerprint data.

[0007] Preferably, the JS collection script adapts to mainstream browser kernels through kernel recognition algorithms, enabling real-time capture of static fingerprints and periodic sampling of dynamic fingerprints.

[0008] Preferably, the data verification algorithm includes field type verification, data range verification, logical correlation verification, and outlier removal. By establishing a fingerprint data association rule base, cross-validation is performed on interrelated fingerprint fields, automatically filling in reasonable missing values ​​and removing abnormal data that is logically contradictory or exceeds the normal range.

[0009] Preferably, the containerized runtime environment uses virtualization technology to simulate the target browser's operating system version, display resolution, language settings, time zone configuration, network protocol stack features, and hardware interface features.

[0010] Preferably, the fingerprint injection module supports three modes: real-time injection, batch injection, and time-series injection. In the time-series injection mode, the fingerprint information is replaced step by step according to the dynamic change pattern of the target browser fingerprint based on the time sequence marker in the JSON fingerprint file.

[0011] Preferably, the fingerprint injection module has a built-in field priority parser that divides the fingerprint field into three priority levels according to its impact on browser identity recognition. The User-Agent string, screen resolution, and browser kernel type are set as the first priority, the network IP, proxy IP, and time zone information are set as the second priority, and the browser font, browser opening time, and language settings are set as the third priority.

[0012] Preferably, the fingerprint simulation log includes fingerprint acquisition source, data processing details, transmission verification record, injection mode selection, consistency verification result, dynamic correction trajectory, and traffic transmission characteristic information.

[0013] Preferably, in step S5, the latest fingerprint data of the target browser is periodically captured and compared with the replaced fingerprint information for updating. At the same time, the fingerprint simulation parameters are automatically adjusted based on the verification results and detection features returned by the website.

[0014] Preferably, a parallel simulation module and a traffic obfuscation module that support multi-target browser fingerprints are constructed. The parallel simulation module allocates an independent browser session handle and network channel to each simulation task, and the traffic obfuscation module mixes and transmits the network traffic generated by each simulation task with the real user traffic.

[0015] Compared with the prior art, the beneficial effects of the present invention are: the present invention achieves accurate and high-fidelity simulation of multi-browser kernel fingerprints through full fingerprint collection, standardized processing, encrypted transmission, containerized environment simulation and time-series injection optimization, supports multi-target parallelism and flexible expansion, thereby improving the simulation realism and concealment, and effectively solving the problems of incomplete simulation and easy detection in the prior art. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a method for simulating fingerprints in any computer browser according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 The embodiments of the present invention provide a method for simulating fingerprints in any computer browser. Through full fingerprint collection, standardized processing, encrypted transmission, containerized environment simulation, and time-series injection optimization, it achieves accurate and highly realistic simulation of fingerprints from multiple browser kernels, and supports multi-target parallelism and flexible expansion.

[0019] One method for simulating fingerprints in any computer browser specifically includes the following steps: S1. After developing a JS collection script and embedding it into the target browser, it collects full fingerprint data through a dynamic scanning mechanism, while recording the dynamic changes in fingerprint data. The JS collection script supports dynamic adaptation to Chrome, Firefox, Edge, Safari, and Opera browser engines. It automatically matches the corresponding collection strategy through the engine recognition algorithm, captures static fingerprint features in real time, and periodically samples dynamic fingerprint features. The engine recognition algorithm identifies the target browser engine type by detecting the browser's engine identifier, API support features, and DOM structure differences. The JS collection script optimizes the collection logic for the fingerprint feature storage location and calling interface differences of different engines to ensure the complete capture of full fingerprint data and avoid fingerprint loss or collection errors due to engine adaptation issues.

[0020] In S1, the full fingerprint data includes at least five of the following: browser size, network IP, proxy IP, browser font, browser language, browser opening time, time zone information, User-Agent string, screen resolution, and browser kernel type. The dynamic change sequence includes the update timestamp, change frequency, and related feature change records of each fingerprint data.

[0021] By collecting at least five core fingerprint data types, key dimensions such as browser software configuration, network environment, and hardware compatibility can be covered. This avoids insufficient simulation accuracy due to missing fingerprint dimensions, ensuring the similarity between the simulated fingerprint and the real browser fingerprint. Recording the update timestamps, change frequencies, and associated feature change records of each fingerprint data type allows for the reconstruction of the dynamic change patterns of the fingerprint, avoiding the problem of static fingerprint simulation being easily detected. By capturing fingerprint-related changes, such as the association between User-Agent changes and browser kernel type, and the matching of time zone and opening time, the logical consistency of the simulated fingerprint can be guaranteed, improving the high degree of simulation and reducing the risk of being identified as a simulated fingerprint by the server.

[0022] S2. Standardize the unstructured raw data using a JSON format converter, classify and organize it according to the preset field structure and fill in the missing fields, use data validation algorithms to verify the legality and format consistency of the fields, and generate a unified format JSON fingerprint file with time-series markers. The JSON format converter classifies and organizes the unstructured raw data according to the above preset field structure and stores different types of fingerprint data into the corresponding fields.

[0023] The preset field structure is divided according to the type and characteristics of fingerprint data, including hardware feature fields, system parameter fields, software configuration fields, network feature fields, and timing information fields. The hardware feature fields cover screen resolution, browser size, and hardware interface characteristics. The system parameter fields include operating system version, time zone information, and language settings. The software configuration fields include browser kernel type, browser font, User-Agent string, and plugin list. The network feature fields include network IP, proxy IP, and network protocol stack characteristics. The timing information fields store the update timestamp, change frequency, and related feature change records of each fingerprint data.

[0024] In S2, the data verification algorithm includes field type verification, data range verification, logical correlation verification, and outlier removal. By establishing a fingerprint data association rule base, cross-validation is performed on interrelated fingerprint fields, automatically filling in reasonable missing values ​​and removing abnormal data that is logically contradictory or exceeds the normal range.

[0025] Field type validation ensures that the format of each fingerprint field is compliant, such as the format of the IP address and the numerical type of the timestamp, to avoid subsequent injection failures due to incorrect formatting. Data range validation filters out abnormal data that exceeds the normal range, such as screen resolution exceeding common sizes or time zone information that does not conform to regional logic, to prevent abnormal data from affecting simulation accuracy. By establishing a fingerprint data association rule base, such as the association between time zone and geographical location, and the matching between User-Agent and browser kernel, cross-validation is achieved to ensure that the logic of associated fields is consistent. Reasonable missing values ​​are automatically filled to ensure the integrity of fingerprint data. Logically contradictory data, such as data corresponding to the Safari kernel of the Windows system, is removed to avoid logical vulnerabilities in the simulated fingerprint, laying a data foundation for subsequent high-fidelity simulations.

[0026] S3. Construct a highly anonymous proxy transmission channel. Use a secure data transmission tool to encrypt the JSON fingerprint file with an asymmetric encryption algorithm. Verify data integrity in real time during transmission. If data loss or corruption is detected, trigger retransmission. At the same time, hide the characteristics of the transmission link.

[0027] The high-anonymity proxy transmission channel employs a multi-layer proxy forwarding mechanism, hiding the transmission link through multiple proxy nodes to prevent the transmission path from being traced. The proxy nodes are distributed in different regions and are changed regularly to further enhance anonymity. The secure data transmission tool uses an asymmetric encryption algorithm; in this embodiment, the RSA algorithm is selected to encrypt the JSON fingerprint file. Before encryption, the JSON fingerprint file is hashed to generate a file digest, which is then transmitted along with the encrypted file.

[0028] During transmission, data integrity is verified in real time. After receiving the data, the receiving end first decrypts the data, then recalculates the file digest and compares it with the transmitted file digest. If they match, the data is complete; if they do not match, the data is considered lost or corrupted, triggering a retransmission mechanism. The receiving end sends a retransmission request to the sending end, and upon receiving the request, it retransmits the encrypted JSON fingerprint file to ensure the reliability of data transmission.

[0029] S4. Build a target fingerprint browser based on a containerized runtime environment, simulate the target browser's hardware, network and software configuration environment, receive JSON fingerprint files through the fingerprint injection module integrated in its kernel, replace the browser's original fingerprint information one by one according to the time sequence mark, and synchronously update the browser's runtime dynamic features.

[0030] The fingerprint injection module has a built-in field priority parser, which divides the fingerprint fields into three priority levels according to their impact on browser identity recognition. The User-Agent string, screen resolution, and browser kernel type are set to the first priority level; network IP, proxy IP, and time zone information are set to the second priority level; and browser font, browser opening time, and language settings are set to the third priority level. During the injection process, the replacement operation is performed field by field in priority order. After the first priority field is replaced, the effective status is immediately verified through the kernel callback mechanism. If it fails, the retry logic is triggered.

[0031] The containerized runtime environment uses virtualization technology to simulate the target browser's operating system version, display resolution, language settings, time zone configuration, network protocol stack characteristics, and hardware interface characteristics.

[0032] Furthermore, the fingerprint injection module supports three modes: real-time injection, batch injection, and time-series injection. The time-series injection mode replaces the fingerprint information step by step according to the time sequence markers in the JSON fingerprint file and the dynamic change pattern of the target browser fingerprint.

[0033] Furthermore, three injection modes are used to adapt to different simulation scenarios. In the real-time injection mode, after receiving the JSON fingerprint file, the core fields, such as User-Agent and screen resolution, are set to first-level priority, the plugin list is set to second-level priority, and the rest are set to third-level priority through the field priority parser. The fields are parsed one by one in this order. After each field is parsed, the browser kernel feature replacement interface is called immediately to write it to the corresponding storage address. The effective status is verified through the kernel callback mechanism. If it fails, it is retried, thus ensuring that it takes effect without delay and adapting to the interactive scenario of dynamic fingerprint detection on the website.

[0034] The batch injection mode first parses all fingerprint data in full, categorizes and organizes it into task subsets according to hardware characteristics, system parameters, software configuration, and network characteristics, and starts a batch transaction processing mechanism to lock kernel fingerprint modification permissions to prevent the browser from automatically updating fingerprints during the injection process. Then, it replaces all fields of the same category in order of category. After all injections are completed, permissions are released and a full consistency check is performed. If there are any failed fields, the entire batch is rolled back and re-injected to ensure that there are no logical contradictions in the fingerprints. It is suitable for batch deployment in environments with a high proportion of static fingerprints.

[0035] The time-series injection mode extracts the update timestamps, change frequencies, and associated feature change records of each fingerprint field in the JSON fingerprint file separately, constructs a dynamic change timeline to recreate the fingerprint evolution trajectory of the target browser, sets injection nodes and intervals according to the timeline, such as replacing network IP every 30 minutes and loading plugin status 5 seconds after browser startup, first injects the initial basic fingerprint, and then replaces dynamic features step by step, while setting trigger conditions for associated fields. After the preceding field takes effect, the injection of associated fields is automatically triggered, deeply restoring the natural change process of the fingerprint and adapting to long-running browser session simulation scenarios.

[0036] S5. Perform consistency verification between the replaced fingerprint information and the target browser fingerprint. If there is a discrepancy, correct it dynamically. After correction, persistently save the fingerprint information to the browser configuration file and generate a fingerprint simulation log. The fingerprint simulation log includes the fingerprint acquisition source, data processing details, transmission verification record, injection mode selection, consistency verification result, dynamic correction trajectory, and traffic transmission characteristic information.

[0037] In S5, a configurable periodic capture cycle can be set from 1 to 24 hours. It periodically captures the latest fingerprint data from the target browser, compares it with the replaced fingerprint information, and updates the data. Simultaneously, based on the verification results and detection characteristics returned by the website, it automatically adjusts the fingerprint simulation parameters. Regularly capturing the latest fingerprint data from the target browser and comparing it with the current simulated fingerprint allows for timely correction of simulation deviations caused by target fingerprint updates, ensuring the timeliness of the simulated fingerprint. At the same time, it automatically adjusts corresponding parameters, such as optimizing the User-Agent string and adjusting the matching degree of time zone and IP. If verification passes, the current parameters are retained and the strategy is solidified, thereby continuously improving the accuracy and anti-detection capability of fingerprint simulation.

[0038] In this embodiment, S1-S5 all support parallel simulation and traffic obfuscation of multi-target browser fingerprints. By assigning an independent browser session handle and network channel to each simulation task, the network traffic generated by each simulation task is mixed with the real user traffic for transmission. By assigning an independent browser session handle and network channel to each simulation task, parallel simulation of multi-target browser fingerprints can be achieved, such as simulating the fingerprints of Chrome and Firefox browsers at the same time. Moreover, each task is isolated from the others to avoid session conflicts or fingerprint obfuscation, thereby improving simulation efficiency and flexibility.

[0039] Furthermore, mixing network traffic generated by simulated tasks with real user traffic during transmission can mask the characteristics of simulated traffic, making it difficult for servers to distinguish between simulated and real traffic.

[0040] In summary, the method provided in this embodiment achieves accurate and highly realistic simulation of multi-browser kernel fingerprints through full fingerprint collection, standardized processing, encrypted transmission, containerized environment simulation, and time-series injection optimization. It supports multi-target parallelism and flexible expansion, thereby improving the realism and concealment of the simulation and effectively solving the problems of incomplete simulation and easy detection in existing technologies.

[0041] All parts not described in this invention are the same as or can be implemented using existing technology. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for simulating fingerprints in any computer browser, characterized in that, Includes the following steps: S1. After developing a JS collection script and embedding it into the target browser, collect the full amount of fingerprint data through a dynamic scanning mechanism, and record the dynamic change sequence of the fingerprint data. S2. Standardize the unstructured raw data using a JSON format converter, classify and organize it according to the preset field structure and fill in the missing fields, use data validation algorithms to verify the legality and format consistency of the fields, and generate a unified format JSON fingerprint file with time-series markers. S3. Construct a highly anonymous proxy transmission channel. Use a secure data transmission tool to encrypt the JSON fingerprint file with an asymmetric encryption algorithm. Verify data integrity in real time during transmission. If data loss or corruption is detected, trigger retransmission. At the same time, hide the characteristics of the transmission link. S4. Build a target fingerprint browser based on a containerized runtime environment, simulate the hardware, network and software configuration environment of the target browser, receive JSON fingerprint files through the fingerprint injection module integrated in its kernel, replace the original fingerprint information of the browser one by one according to the time sequence mark, and update the dynamic features of the browser runtime synchronously. S5. Perform consistency verification between the replaced fingerprint information and the target browser fingerprint. If there is a discrepancy, correct it dynamically. After correction, persistently save the fingerprint information to the browser configuration file and generate a fingerprint simulation log.

2. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: In step S1, the full fingerprint data includes at least five of the following: browser size, network IP, proxy IP, browser font, browser language, browser opening time, time zone information, User-Agent string, screen resolution, and browser kernel type. The dynamic change sequence includes the update timestamp, change frequency, and related feature change records of each fingerprint data.

3. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The JS collection script adapts to mainstream browser kernels through kernel recognition algorithms, enabling real-time capture of static fingerprints and periodic sampling of dynamic fingerprints.

4. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The data verification algorithm includes field type verification, data range verification, logical correlation verification, and outlier removal. By establishing a fingerprint data association rule base, cross-validation is performed on interrelated fingerprint fields, automatically filling in reasonable missing values ​​and removing abnormal data that is logically contradictory or exceeds the normal range.

5. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The containerized runtime environment uses virtualization technology to simulate the target browser's operating system version, display resolution, language settings, time zone configuration, network protocol stack characteristics, and hardware interface characteristics.

6. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The fingerprint injection module supports three modes: real-time injection, batch injection, and time-series injection. The time-series injection mode replaces the fingerprint information step by step according to the time sequence markers in the JSON fingerprint file and the dynamic change pattern of the target browser fingerprint.

7. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: In step S5, the latest fingerprint data of the target browser is periodically captured and compared with the replaced fingerprint information for updating. At the same time, the fingerprint simulation parameters are automatically adjusted based on the verification results and detection features returned by the website.

8. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The fingerprint injection module has a built-in field priority parser, which divides the fingerprint field into three priority levels according to its impact on browser identity recognition. The User-Agent string, screen resolution, and browser kernel type are set as the first priority, network IP, proxy IP, and time zone information are set as the second priority, and browser font, browser opening time, and language settings are set as the third priority.

9. The method for simulating fingerprints in any computer browser according to claim 1, characterized in that: The fingerprint simulation log includes fingerprint acquisition source, data processing details, transmission verification records, injection mode selection, consistency verification results, dynamic correction trajectory, and traffic transmission characteristic information.

10. The method for fingerprint simulation in any computer browser according to any one of claims 1-9, characterized in that: A parallel simulation module and a traffic obfuscation module that support multi-target browser fingerprinting are constructed. The parallel simulation module allocates an independent browser session handle and network channel for each simulation task, and the traffic obfuscation module mixes and transmits the network traffic generated by each simulation task with the real user traffic.

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