30 results about "Device fingerprint" patented technology

Embodiments of the present application provide a positioning method and device, electronic equipment, storage medium and program product. The positioning method comprises: determining a plurality of target location points based on the similarity between the device fingerprint data of the to-be-positioned device and the device fingerprint data of the location points recorded in the device fingerprint database; the device fingerprint data comprises wireless network signal data and direction; determining a first weight value based on the similarity between the device fingerprint data of the plurality of target location points and the to-be-positioned device fingerprint data; and weighting the plurality of target location points based on the first weight value to obtain the location of the to-be-positioned device. Since the angle towards the wireless access point, i.e. the direction of the device, will affect the performance of the wireless network signal data, the similarity between the directions is introduced into the WKNN algorithm for weight calculation, thereby improving the accuracy of device positioning.

The application relates to the technical field of security verification, and discloses an application security access method and device, equipment and a storage medium, the method comprising the following steps: in response to an application access request input in a browser, determining identity verification information of a user according to the application access request; obtaining a device fingerprint, wherein the device fingerprint is generated by the browser according to device information of the user; performing security check according to the identity verification information and the device fingerprint to obtain a security check result; and performing application access control according to the security check result. The application can ensure that the user can only access the application on a specified security device, and effectively improves the application access security.

This invention relates to an automatic IT asset discovery method based on multi-protocol fusion and incremental change detection. Compared with existing technologies, it solves the shortcomings of existing IT asset discovery methods, such as incomplete protocol coverage, low scanning efficiency, improper handling of multi-source data conflicts, and high update overhead, resulting in low asset discovery coverage, low accuracy, and synchronization delays. This invention includes the following steps: building a multi-protocol parallel scanning framework; intelligent scheduling of protocol priorities; multi-dimensional device fingerprint extraction; confidence-weighted fusion deduplication; incremental change detection; and intelligent identification of asset model classification. This invention introduces an intelligent asset classification mechanism based on a machine learning model to achieve automatic identification and intelligent management of asset types, realizing high coverage, high accuracy, low overhead, and low latency automatic discovery and management of IT assets.

The present disclosure relates to a fingerprint detection device, a fingerprint chip, a display device and an electronic device, which are applied to a display panel, and the device comprises a switching module connected to an optical fingerprint chip, used for switching the working mode of the optical fingerprint chip to a touch detection mode or a fingerprint collection mode, wherein when the optical fingerprint chip works in the touch detection mode, the optical fingerprint chip is used for detecting whether the display panel is touched; when the optical fingerprint chip works in the fingerprint collection mode, the optical fingerprint chip is used for collecting fingerprints. The embodiment of the present disclosure can detect whether the display panel is touched by using the optical fingerprint chip, and collect fingerprints. Compared with the related art, the use of touch sensors or pressure sensors is saved, the cost is saved, and the process complexity is reduced, so that the response speed is improved and the overall power consumption is reduced.

The present application relates to a kind of household water end equipment identity fingerprint library establishment calling identification method and system, belong to the field of Internet of Things data management and smart home.The present application establishment method includes: the flow rate and water pressure time series data of single water use event are collected;Call feature extraction interface to calculate standardized multidimensional feature vector;Based on feature vector, the statistical model of device fingerprint object is constructed;Quality verification and conflict detection are carried out;The fingerprint that passes verification is stored in local fingerprint library in read-only mode by controlled management interface.The calling method includes: the water use event to be identified is converted into query feature vector, and similarity calculation is carried out with fingerprint in library, and the matching result is returned.The advantages of the present application: by standardized feature extraction, statistical model construction, multistage quality verification and controlled management, provide stable and reliable equipment identification infrastructure for intelligent water application, effectively improve identification accuracy and system maintainability.

The application discloses a financial anti-fraud method and system based on dynamic heterogeneous graph and behavior sequence fusion, and relates to the technical field of financial transaction anti-fraud. The method comprises the following steps: obtaining and integrating application data of a user, device fingerprint data, network environment data and real-time behavior sequence data on an application interface; constructing a dynamic heterogeneous graph; learning the dynamic heterogeneous graph, aggregating neighbor node information and generating a relationship risk representation vector; learning the real-time behavior sequence data, capturing time sequence dependence and behavior patterns of user operation and generating a behavior risk representation vector; fusing the relationship risk representation vector and the behavior risk representation vector to generate a fused risk representation vector; inputting the fused risk representation vector into a pre-trained classifier to output a fraud probability score of the user, and making a risk decision according to a preset threshold. The application can significantly improve the identification ability of potential fraud gangs and the sensitivity to new fraud methods.

The application discloses a dynamic key negotiation and encryption method and system for a power wireless private network, and relates to the technical field of power communication security. The method comprises the following steps: a terminal device collects first channel characteristic parameters, generates a first random number, generates a device fingerprint through a physically unclonable function, generates a temporary key pair by taking the first random number and the device fingerprint as an entropy source, and sends a negotiation request; a base station collects second channel characteristic parameters for consistency comparison, generates a shared key raw material after matching, generates a session key through a hash operation by combining channel parameters, a base station identifier and the device fingerprint, and returns a response; and the terminal generates the same session key to complete negotiation. The terminal generates a chaotic sequence by taking the session key as a seed and combining the device fingerprint, and encrypts power service data by using an authentication encryption algorithm. The physically unclonable function is used to replace a software pseudo-random number generator, so that the problems of insufficient entropy source and easy prediction of a key of a resource-restricted power terminal are solved.

The application relates to a software runtime memory fingerprint watermark protection method and system, and belongs to the field of computer software security protection.The method comprises the following steps: collecting various hardware characteristic information of a target device to generate a device fingerprint, selecting a predetermined position segment as a deterministic random seed, driving a pseudo-random number generator by the seed, constructing a hierarchical object tree, recursively traversing the object tree to calculate final hash values of each node, collecting node identification, the number of child nodes, the final hash value of the node and the address difference between the parent node and the child node, and obtaining a comprehensive topology hash value through a cryptographic hash operation, repeatedly executing comparison of the comprehensive topology hash value at random time intervals during operation, and identifying debugging behavior in multiple dimensions by using different detection methods, and executing a hierarchical threat response according to the comparison result and the anti-debugging detection result.The application closely binds the device fingerprint and the runtime memory layout, realizes strong association protection of a software running environment and a memory topology, and effectively resists copying, tampering and dynamic debugging attacks.

This invention provides a software license usage detection method and apparatus based on multi-dimensional environmental awareness, comprising: acquiring the environmental characteristics of the software under test during its initial runtime; wherein the environmental characteristics include hardware layer characteristics, network layer characteristics, and system layer characteristics; dynamically generating an initial device fingerprint based on the environmental characteristics; binding the initial device fingerprint with the software license information of the software under test; acquiring the current environmental characteristics and current behavior pattern of the software under test in real time, and dynamically generating a current device fingerprint based on the current environmental characteristics; comparing the initial device fingerprint with the current device fingerprint, and the current behavior pattern with the historical behavior pattern of the software under test, to obtain the detection result. This solution improves the security, flexibility, and user experience of license detection.

The application discloses a kind of based on feature orthogonal decoupling's radio frequency fingerprint authentication data set construction method, it is related to radio frequency fingerprint identity authentication field, the method of the present application collects multiple equipment radio frequency signal and constructs training data set, trains orthogonal decoupling auto-encoder network, and through the orthogonal separation of mutual information minimization's confrontation training in latent space between baseband semantic information and hardware fingerprint feature;When applying, the fingerprint feature vector of target device and local device is extracted and pre-distortion compensation vector is calculated, the content vector of pure baseband data is spliced after with compensation vector, and then decoding reconstruction is generated, with the radio frequency simulation signal of target device fingerprint feature.The application fundamentally eliminates secondary nonlinear distortion, and can efficiently construct large-scale radio frequency fingerprint authentication data set with high fidelity.

This invention belongs to the field of data security technology, and specifically relates to a method for secure data transmission in the Internet of Things (IoT). Addressing the challenges of limited resources and numerous security vulnerabilities in IoT device transmission, this method constructs end-to-end protection: generating dynamic device fingerprints based on physically unclonable functions and behavioral characteristics to ensure identity trustworthiness; employing an improved elliptic curve Diffie-Hellman algorithm to generate a shared key, adaptable to lightweight devices and resistant to attacks; dynamically fragmenting based on information entropy, encrypting data differently according to sensitivity and embedding redundant data; fragmenting data for multi-path transmission; fragment-level verification and precise retransmission, with alarm isolation triggered after more than three failures. This invention balances security and efficiency, comprehensively resisting threats such as forgery, eavesdropping, and hacking, ensuring the integrity and reliability of data transmission, and adapting to complex IoT application scenarios.

The application relates to a vehicle passing control method and device, a terminal device and a computer program product. The method comprises the following steps: when a vehicle travels to a first collection area, acquiring first passing information of the vehicle, including first license plate information, first position information and a first device fingerprint of at least one in-vehicle terminal device; when the vehicle travels to a second collection area, acquiring second passing information, including second license plate information, second position information and a second device fingerprint of at least one in-vehicle terminal device; in the case that the first device fingerprint matches the second device fingerprint and the first license plate information does not match the second license plate information, correcting the second license plate information by using the first license plate information to obtain third passing information; and performing passing control on the vehicle based on the first passing information and the third passing information. Through the application, the problem that the accuracy of license plate recognition is low in the related art, thereby reducing the accuracy of vehicle passing control, is solved.

This invention discloses a multi-terminal collaborative cryptographic evaluation system and method, relating to the field of information security technology. It includes a multi-terminal connection module and a connection evaluation module. The invention uses device fingerprint information and data certificates from the tool terminal for authentication, blocks downgrade requests during the establishment of a secure communication channel to improve data interaction security, obtains the tool terminal's startup measurement logs and compares them with benchmark values, extracts random number features from physical noise sources within the tool terminal, evaluates the authenticity of random number generation on the tool terminal, and, based on the actual state of the tool terminal, divides the evaluation task into parallel subtasks for allocation. It performs correlation analysis and deep detection on the evaluation results uploaded by the tool terminal to obtain associated risk items, generates supplementary verification tasks and sends them to the tool terminal for retesting, and, combined with entropy source authenticity comparison, identifies weak random number defects, thereby improving the comprehensiveness of risk identification.

The application discloses a multi-terminal non-susceptible login access method based on tokens and device fingerprints and an electronic device, and relates to the technical field of electronic devices. The method comprises the following steps: when a first user accesses multiple servers through a first terminal, the first terminal obtains a first token for accessing a first server according to a device fingerprint of the first terminal and login information for the first server, and stores the device fingerprint of the first terminal in a user device fingerprint set of the first user; the first terminal generates a second token for accessing a second server according to the device fingerprint of the first terminal and the fact that the first token exists in the user device fingerprint set of the first user; the user device fingerprint set comprises device fingerprints of part or all terminal devices corresponding to part or all login information of the user, and the device fingerprint is a unique identifier of the terminal device. The application can realize non-susceptible login and access of the user in a multi-terminal multi-server system.

The invention provides a media data processing method and device, electronic equipment and a readable medium. The method comprises the steps that a video playing request sent by a playing terminal is acquired, the video playing request comprises an equipment fingerprint of the playing terminal, and the equipment fingerprint is generated according to equipment information of the playing terminal; according to the video playing request, a risk query request containing the equipment fingerprint is sent to a risk control server, so that risk state information of the playing terminal is obtained through the risk control server according to the equipment fingerprint, and the risk state information is generated by carrying out risk assessment according to equipment information of the playing terminal; and according to the risk state information sent by the risk control server, if the risk state information satisfies a predetermined risk control strategy, sending the to-be-played video to the playing terminal, so as to play the to-be-played video on the playing terminal. According to the method, the media data leakage can be prevented, and the data confidentiality and the data security of the media content are improved.

This invention belongs to the field of secure communication technology for IoT devices, specifically relating to a method for mutual identity verification between multiple hardware devices, including the following steps: a dynamic device fingerprint generation step; a two-way challenge-response authentication step; and a secure channel establishment step. This invention features a dynamically changing device identity: each time authentication is performed, the device generates a temporary, unique dynamic fingerprint (based on hardware ID + timestamp + random number), instead of using a fixed key or a long-term valid certificate. Time-limited control: the dynamic fingerprint has a short validity period (e.g., 3-5 minutes), automatically expiring after the timeout, preventing attackers from reusing historical authentication data even if they intercept it. Resistance to copying attacks: because the fingerprint is strongly correlated with time and random number, attackers cannot forge valid historical fingerprints for replay attacks. Mutual verification mechanism: not only does the server verify the client, but the client also verifies the server's identity, completely eliminating the risk of forged devices or "man-in-the-middle" attacks.

A method of authenticating a user device in providing access to a computer resource, the method includes identifying a device fingerprint record stored in an access log. The method includes produce, based on the device fingerprint record, a digital signature includes one or more session characteristics. The method includes determining, by a processing device from the digital signature, a root signature pattern associated with the one or more session characteristics. The method includes identifying an access request for a computer resource as an unauthorized access based on the device fingerprint record and the root signature pattern.

The application provides an Android application trusted environment detection method, device and equipment and a storage medium, and belongs to the technical field of computer security, which comprises the following steps: obtaining environment information of an Android application; the environment information comprises device fingerprint information, emulator information or system integrity information; the device fingerprint information comprises an Android ID, hard disk information or a MAC address; performing device fingerprint detection, emulator detection and system integrity detection according to a preset method; and if the device fingerprint detection, emulator detection and system integrity detection all satisfy preset conditions, determining that the running environment of the Android application is trusted. The application collects the characteristics of a mobile phone in multiple ways, and combines multi-dimensional detection technology in multiple levels to detect the trusted environment of the Android application, thereby greatly improving the accuracy of the trusted environment identification of the Android application, and effectively resisting attacks such as emulators, disguising tampering and simulated clicks.

The application uses an alternating electromagnetic signal as a fingerprint to completely passively authenticate a wireless charging device, and due to the omnipresence of the electromagnetic signal, more reliable bidirectional authentication in the system can be achieved. The application is very convenient for users, and only needs to be passively collected and detected using a commercial magnetic sensor, and does not depend on a wireless charging authentication method based on omnipresent electromagnetic signals, comprising: collecting an electromagnetic signal; performing privacy protection processing; preprocessing to obtain a signal sample for a certain smart device; performing feature extraction on the signal sample to obtain a series of feature sets constituted by each sample as a device fingerprint; and inputting the device fingerprint into a classifier for training, so that when a device needs to access a wireless charging system, whether to allow access and whether there is an intrusion behavior can be judged through detailed comparison of the fingerprint. Relying on additional manual operation.

The application provides an electronic coupon information anomaly detection method, relates to the technical field of Internet data security and risk control, and is characterized in that the method comprises the following steps: collecting multi-source heterogeneous data of a target object in an electronic coupon life cycle in real time; the multi-source heterogeneous data comprises user behavior sequence data, device fingerprint data, account attribute data, coupon state data, geographic location data and network environment data. The application has the following advantages: by constructing a 'person-device-behavior-relationship' heterogeneous correlation graph, the collaborative cheating behavior of a black production team can be effectively identified, and the false positive rate and the false negative rate can be greatly reduced; the application has dynamic self-adaptive capability, can monitor attack evolution in real time and update the model online, and overcomes the hysteresis of traditional static defense; meanwhile, multi-dimensional feature deep fusion and hierarchical interception strategies are adopted, so that the application can accurately distinguish automated attacks, balance security and user experience, and form a continuously evolving closed-loop protection system.

This invention discloses a digital instruction release control method, chip, and system based on physical disturbance characteristics and device-intrinsic fingerprints. The method acquires real-time physical state information related to the execution object, execution environment, or execution node corresponding to the digital instruction to be verified; acquires the device-intrinsic fingerprint parameters or their derived response values ​​of the execution node; and acquires the digital feature information of the digital instruction to be verified. Based on the real-time physical state information, the device-intrinsic fingerprint parameters or their derived response values, and the digital feature information, a judgment value is generated to characterize whether the digital instruction meets preset release conditions. When the judgment value indicates that the digital instruction does not meet the preset release conditions, a blocking control signal is output to prevent the digital instruction from being executed, propagated, or written to the target link. In some embodiments, the real-time physical state information can be represented by a spatial residual component D1 and a temporal variation component D2, and the device-intrinsic fingerprint parameters can be represented by a static fingerprint, a digest value, or a challenge-response value. This invention can bind the execution qualification of digital instructions to the real-time physical state of the execution node and the inherent non-cloning characteristics of the device, thereby upgrading the verification conditions of digital instructions from logically executable to physically passable. It is applicable to scenarios such as warehouse settlement, industrial control, content authenticity verification, scientific instruments, unmanned equipment and energy scheduling.

The application discloses a kind of based on function call stack backtracking and defect management method of crash sample clustering, comprising: subscribing and collecting Java exception, ANR and Native crash signal, record time stamp, thread name, device fingerprint, APK / SDK version meta information, it is encapsulated as "crash sample unit";Load corresponding version mapping file and NDK (Native Development Kit) symbol, restore method name and source code line number, and execute path normalization and noise frame clipping;Splice scattered call segment, generate complete trigger path;Based on exception type, TopK key frame, trigger path hash and environment abstract, generate robust signature;Similarity calculation and configurable threshold clustering are carried out in signature space, and same source or similar crash is merged into "crash cluster";Representative sample is selected to each cluster, combined with event sequence, page state and life cycle track, automatically generate replayable PoC script and structured report.The application can improve the accuracy of defect deduplication and aggregation and enhance defect reproduction and positioning ability.

A sample data generation method, a model training method, a device fingerprint classification method and device, the sample data generation method comprising: grabbing smart substation messages; analyzing the smart substation messages, and using the analyzed message fields, combining with the smart substation structure information stored in the SCD file, obtaining a device type label and updating a device label table; based on the updated device label table, obtaining a device type label value and a message feature field and encapsulating them as a PTD data set to serve as sample data of a smart substation measurement and control device fingerprint classification model. The model training using the sample data generated by the method provided in the embodiment of the application can improve the feature extraction effect and the generalization of the model, and thus the recognition and classification of the smart substation measurement and control device by the model can be realized.

This invention relates to the field of data retrieval technology and discloses a statistical data association retrieval and mining system integrating graph neural networks. The system includes a data acquisition and graph construction module configured to acquire multi-source heterogeneous data and extract entities and relationships to construct a heterogeneous graph data. The heterogeneous graph data includes nodes and edges; the nodes include at least user identifiers, device fingerprints, IP addresses, and transaction events, and the edges represent the associations between nodes. A graph neural network embedding module is configured to extract features from the nodes in the heterogeneous graph data using a pre-trained graph neural network model. This invention, by constructing a heterogeneous graph and integrating graph neural network embedding with a few-shot learning mechanism, enables the system to achieve accurate node identification with only a very small number of labeled fraud samples, effectively overcoming the shortcomings of traditional methods that rely on large-scale labeled data. It is particularly suitable for real-world business scenarios where fraud patterns evolve rapidly.

The application is suitable for the technical field of fingerprint identification, and provides a device fingerprint identification method and device, the device fingerprint identification method comprises: in the process that a user's finger contacts and presses until leaves a sensor, an image frame sequence of a fingerprint to be identified is collected at a fixed frame rate; a static feature vector is extracted based on a frame image corresponding to a maximum contact area in the image frame sequence; a time sequence behavior feature is extracted based on the image frame sequence; whether a user corresponding to the image frame sequence is a device user is identified based on the static feature vector and the time sequence behavior feature. The introduction of the dynamic feature makes the identification process not only depend on static image data, but also consider the actual behavior of the user in the operation process, thereby improving the identification ability of the real user.

This application relates to a method for generating a USB device fingerprint, comprising: obtaining the serial number, padding model, plug-and-play identifier, and device driver firmware signature of a target USB device; performing a verification operation on the serial number, padding model, plug-and-play identifier, and device driver firmware signature; and generating a device fingerprint of the target USB device based on the serial number, padding model, plug-and-play identifier, and device driver firmware signature if the verification operation passes. Therefore, the technical solution of this application performs a verification operation on the obtained serial number, padding model, plug-and-play identifier, and device driver firmware signature. This verification operation can prevent security vulnerabilities caused by malicious tampering of the serial number, padding model, plug-and-play identifier, or device driver firmware signature from the source, avoiding counterfeit USB devices from bypassing system access control and illegally accessing business systems using tampered false information, thereby reducing the risk of malicious attacks on the system.

The present application relates to the technical field of information security, in particular to a computer network information security management method and system based on data processing, the method comprising: collecting key security data and historical security data of the current login session of a user, and preprocessing the key security data to form a current login behavior feature vector; constructing a login time probability distribution model, a login space position clustering center model and a historical device fingerprint model for each user based on the historical security data, and forming a user behavior baseline vector; comparing the current login behavior feature vector with the user behavior baseline vector within 1 second after the user completes login, and calculating the time deviation, the space deviation and the device abnormality degree. The present application introduces a dynamic device fingerprint technology based on multiple fractal spectrum analysis and hardware aging drift model, effectively overcoming the problems of traditional static fingerprint being easily forged and high false alarm rate caused by natural hardware aging.

This invention discloses a smart home water and heating safety protection system and method based on dual AI engine collaboration, belonging to the field of smart home water and heating safety protection technology. The system includes: a hardware sensing and execution unit deployed in the water and heating pipelines, integrating multiple types of sensors and a smart shut-off valve with built-in torque feedback; a dual AI engine collaborative control unit, comprising a user-end water supply AI engine, a heating AI engine, and a collaborative arbitration module, respectively realizing functions such as leak classification response, device fingerprint recognition, out / back home logic, multi-evidence fusion leak detection, blockage diagnosis and quantification, valve maintenance, and health prediction; and a cloud-based intelligent platform that achieves model self-evolution through federated learning. The advantages of this invention are: through innovations such as differentiated collaboration of dual AI engines, multi-dimensional feature fusion, device fingerprint recognition, multi-evidence fusion decision-making, predictive maintenance, and three-level power supply protection, it solves the problems of existing products such as single function, high false alarm rate, lack of predictive ability, and inability to coordinate water and heating supply.