37 results about "Spoofing attack" patented technology

To provide a positioning device that can reduce the risk of decreased positioning accuracy in an environment of spoofing attacks. [Solution] A positioning device comprising: a receiving unit that receives a first satellite signal in a first frequency band and a second satellite signal in a second frequency band different from the first frequency band transmitted from a first satellite; a positioning unit; a determination unit that compares the value of a predetermined parameter included in the first satellite information of the first satellite based on the first satellite signal with the value of the predetermined parameter included in the second satellite information of the first satellite based on the second satellite signal to determine whether or not the first satellite is spoofed; and a control unit that controls the positioning unit so as not to perform positioning using the first satellite information and the second satellite information of the first satellite when the determination unit determines that the first satellite is spoofed.

A method for detecting anomalies in image data captured by a vehicle sensor includes receiving first and second image frames, and first and second observation clouds generated based on LiDAR data capt

The application relates to an adaptive filtering method based on sliding window innovation evaluation and joint constraint. The method comprises the following steps: calculating the innovation covariance through parallel calculation theory and the empirical innovation covariance estimated based on the sliding window, dynamically quantifying the mismatch degree of the two, and adaptively amplifying the GNSS measurement noise covariance according to the mismatch degree, so as to suppress the weight of abnormal signals in fusion. The second module is a state prediction covariance joint constraint unit, which prevents excessive convergence of filtering by introducing a dynamic forgetting factor and applying a minimum boundary constraint related to the position state to the prediction covariance matrix, thereby ensuring that the LiDAR maintains effective state correction capability within the continuous GNSS update interval. Through the synergistic effect of the above mechanisms, the method realizes early perception and active inhibition of advanced spoofing attacks, and significantly improves the positioning safety and robustness of the multi-sensor fusion system in a complex dynamic environment.

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

The application relates to the technical field of fraud detection, and discloses a detection method and system for spoofing attacks / natural electromagnetic interference, which comprises: a first detection quantity based on absolute tracking loop output (ATLO); mobile variance filtering processing (ATLO-MV) is performed on the first detection quantity to construct a second detection quantity; and the detection probability information of the second detection quantity of ATLO-MV of multiple satellites is used to distinguish spoofing attacks, natural electromagnetic interference and no interference. Through construction of the first detection quantity based on absolute tracking loop output, subtle fluctuations in tracking loop output can be accurately captured; the second detection quantity of ATLO-MV obtained through mobile variance enhancement processing effectively improves detection sensitivity and anti-noise performance; finally, a new type of detection algorithm is constructed through multi-satellite joint processing (ATLO-MV-MSC) of ATLO-MV, the distinction between no interference, natural electromagnetic interference and spoofing attacks is realized, and the pain point of fuzzy judgment existing in traditional algorithms is solved.

This application discloses a security control method and system for smart cockpits. The solution acquires user authentication information, historical behavior baselines, and authentication features. It continuously collects current environmental and user behavior information, dynamically determines user trust levels based on the aforementioned information, and adjusts user operation permissions in real time according to the trust level. Upon detecting a user interaction command, it acquires multimodal signals within the corresponding time window of the command and performs multi-dimensional consistency verification on the command based on authentication features. Based on the verification results and the user's current operation permissions, it determines the command execution strategy. This application's technical solution achieves precise matching of permissions and real-time risks through dynamic trust level assessment. Relying on multimodal signal verification, it accurately identifies abnormal interactions and deception attacks, achieving refined security control of interaction commands. Through the coordinated decision-making of trust level, operation permissions, and interaction verification results, it improves the reliability and adaptability of smart cockpit security control.

The application discloses a GNSS spoofing detection method based on adaptive RAIM and hidden space diffusion model, and relates to the fields of satellite navigation and artificial intelligence.The method comprises the following steps: constructing an adaptive pseudo-range residual feature extractor, wherein the adaptive pseudo-range residual feature extractor is composed of a weight adaptive network and a weighted least squares RAIM; the weight adaptive network is constructed by a graph convolutional neural network, can adaptively correct the pseudo-range observation weight matrix of the weighted least squares RAIM, and can enhance the sensitivity of the pseudo-range residual to the geometric consistency destruction caused by spoofing; the weighted least squares RAIM processes the pseudo-range residual on the basis to extract the multi-scale trend features; constructing a hidden space diffusion model, wherein the hidden space diffusion model is trained by using the multi-scale trend features which are not spoofed, and performs spoofing detection through a state evaluation function.The application can effectively improve the detection performance of the receiver on the GNSS spoofing attack.

The present application provides a kind of face video identification method and system, method includes: obtaining the video frame image sequence corresponding to face video, video frame image sequence is by multiple video frame images, and video frame image is obtained by frame by frame processing to face video;According to video frame image sequence, obtain the human heart rate feature and texture feature in face video;According to human heart rate feature and texture feature, determine the authenticity of face video.The system executes the method.The present application can significantly reduce the probability of identifying fake face video as real face video, can effectively prevent face spoofing attack and fake face video attack, reduce the risk of secret information leakage.

There is provided a computer-implemented method for detecting a spoofing attack in an image, the method comprising: receiving an image of a user; obtaining a first image portion from the image, the first image portion being less that the whole image of the user; processing the first image portion to generate a first output indicative of a first estimated human age; processing the image of the user to generate a second output indicative of a second estimated human age; and based on the first output and the second output, determine whether the image of the user comprises the spoofing attack.

An adaptive control method for cyber-physical systems (CPS) subjected to injection and deception attacks includes the following steps: S1: Constructing a control system model under a cyber-physical framework; S2: Defining actuator attacks when the system's sensors and actuators are subjected to adversary injection and deception attacks; S3: Defining sensor attacks when the system is subjected to sensor attacks; S4: Rewriting the system model under cyberattacks; S5: Based on the rewritten system model, obtaining the adaptive control law u using the BackStepping method; S6: Further deriving that all signals in the closed-loop system are globally bounded based on the adaptive control law u in S5, and designing a controller by introducing Nussbaum even functions and their variable derivatives. When the system is subjected to injection and deception attacks, the control parameters are adjusted to make the adjustment error arbitrarily small. This invention can ensure that the adjustment error can be arbitrarily small under injection and deception attacks by adjusting the control parameters.

The present specification relates to the technical field of automatic driving, and provides a spoofing attack detection method and device, equipment and a storage medium. The method comprises: acquiring point cloud data currently collected by a laser radar of a vehicle; identifying whether an abnormal object exists in the point cloud data; when the abnormal object exists in the point cloud data, broadcasting an assistance detection request carrying point cloud data and a spatial position of the abnormal object to surrounding vehicles; receiving an assistance detection response returned by the surrounding vehicles in response to the assistance detection request; and determining whether a spoofing attack exists in the laser radar according to the assistance detection response. The embodiments of the present specification can improve the detection accuracy of spoofing attacks on the laser radar and improve the driving safety of the automatic driving vehicle.

A method, apparatus, and computer-readable storage medium for voiceprint spoofing defense include: acquiring a speech signal to be verified; extracting multi-dimensional features from the speech signal to obtain a multi-dimensional feature vector, wherein the multi-dimensional feature vector includes at least speech text content features, identity features, sound quality features representing recording or synthesis traces, liveness features representing the physiological characteristics of the sound source, and adversarial perturbation features; fusing the multi-dimensional feature vector to obtain a joint feature vector; inputting the joint feature vector into a pre-trained joint risk decision model to obtain a spoofing risk score; and determining the verification result of the speech signal to be verified based on the comparison result of the spoofing risk score and a preset threshold. This application can effectively detect multiple attack modes such as replay attacks, speech synthesis attacks, and adversarial example attacks simultaneously; and accurately detect high-quality spoofing attacks.

This invention discloses a memory-based dynamic event triggering control method for a single-link robotic arm system based on spoofing attacks. The method first establishes a Markov model of the single-link robotic arm system based on Markov jump system theory, considering a system model with a general transition rate. Next, a mode-dependent memory controller is designed to overcome the influence of spoofing attacks and external disturbances on the system. A memory-based dynamic event triggering mechanism is also designed to reduce communication transmission frequency. Compared with existing memoryless event triggering schemes, this scheme utilizes a series of recently released signals and introduces a threshold function and an internal dynamic factor, which can automatically adjust according to the triggering error. Finally, vertex separator processing is introduced to address the uncertainty in the Markov transition rate. A mode-dependent state feedback controller is designed to control the stochastic stability of a single-link robotic arm system. When applied to a single-link robotic arm system, this method ensures the normal operation of the system under spoofing attacks and disturbances.

The application discloses a kind of intelligent home voice security test method and device based on voiceprint anti-counterfeiting, it is related to intelligent home security test technical field.The core of the device includes main control module, attack simulation module and response acquisition module.Attack simulation module generates multi-dimensional voice deception attack sample including replay, synthesis, conversion and countermeasure sample.Main control module is built-in dynamic countermeasure reinforcement learning algorithm, and dynamically generates digital domain control instruction and physical domain control instruction according to the response state of the device to be measured, wherein the physical domain control instruction directly drives the stepper motor of physical replay sub-module to carry out angle rotation, realizes the deep binding of algorithm strategy and physical hardware action.Response acquisition module comprehensively captures the state feedback of the device to be measured by multi-modal perception means.The device can further include environment simulation module and evaluation and output module to construct physical test space with specific acoustic characteristics and output quantitative evaluation report.The application first proposes a comprehensive test device combining software and hardware from the perspective of active attack test, which can comprehensively and automatically evaluate the voiceprint anti-counterfeiting performance of intelligent home voice control system and Internet of Things devices.

This application belongs to the field of microgrid frequency control, specifically disclosing a model-free adaptive microgrid frequency control method to resist network attacks. The method includes the following steps: considering the mixed effects of denial-of-service attacks and spoofing attacks, a system output model incorporating attack coefficients is established, modeling the microgrid frequency control system as a discrete-time nonlinear system; dynamic linearization technology is used to transform the nonlinear system into an equivalent linear data model; for the linear data model, a partial pseudo-derivative estimation algorithm is designed, and a model-free adaptive control law incorporating a decay function is designed; the time-varying partial pseudo-derivative matrix is ​​iteratively updated using the partial pseudo-derivative estimation algorithm, and based on the updated partial pseudo-derivative matrix, the control input at the current moment is calculated using the model-free adaptive control law to control the microgrid frequency. This application can achieve effective frequency control even when the microgrid frequency control system model is unknown and faces mixed network attack threats.

This invention discloses a privacy-preserving elastic average consensus method and system for multi-agent systems, belonging to the fields of multi-agent cooperative control and network security technology. The method includes: a privacy-preserving phase, where agents decompose their initial state into public and private sub-states, introducing free non-zero parameters to ensure the global mean remains unchanged, with only public sub-states participating in interaction, thus cutting off eavesdropping paths; and an elastic consensus phase, maintaining an enhanced local information set containing the agent's and neighbors' states, true values, attack flags, and compensation inputs, rapidly detecting based on two-hop neighbor information, calculating state deviations and dynamically compensating for malicious data, and iterating according to the average consensus rule until regular nodes converge to the arithmetic mean of the global initial state. This invention simultaneously achieves initial state privacy protection and accurate elastic average consensus under the conditions that deceptive attack nodes are not adjacent and there is no attack at the initial moment, making it suitable for high-security distributed collaborative scenarios such as smart grids, unmanned clusters, and the Industrial Internet of Things.

Positioning device, positioning method, and positioning program products. A positioning device is provided that can reduce the possibility of decreased positioning accuracy in an environment of spoofing attacks. The positioning device includes: a receiving unit that receives a first satellite signal in a first frequency band and a second satellite signal in a second frequency band transmitted from a first satellite; a positioning unit; a determination unit that compares the values ​​of predetermined parameters based on the first satellite signal and the values ​​of the predetermined parameters based on the second satellite signal to determine whether the first satellite is a correct satellite; and a control unit that, if the determination unit determines that the first satellite is not a correct satellite, controls the positioning unit not to use the first satellite information and the second satellite information for positioning.

An apparatus for spoofing attack detection in a wireless system and a method of operation thereof. The apparatus includes a multi-antenna RF array, a beamformer, a preprocessor and a machine learning-based detector. The array produces signals in response to an RF signal from a transmitter having one or more transmitter beam patterns. The beamformer combines the signals using a receiver beam pattern to produce a received signal. The preprocessor produces feature vectors of an RF fingerprint, where a component of a feature vector is a strength of the received signal for a measured beam pattern pair, the beam pattern pair having a transmitter beam pattern and a receiver beam pattern, or a designated value for an unmeasured beam pattern pair. The detector is configured to detect a spoofing attack from an incomplete feature vector that has at least one component corresponding to an unmeasured beam pattern pair.

This disclosure provides an Address Resolution Protocol (ARP) attack detection method and system, comprising: sending probe data packets to key network nodes from a host and calculating the round-trip time; determining whether network behavior is abnormal based on the round-trip time; if so, extracting the mapping relationship; obtaining a legitimate mapping benchmark; determining whether the MAC addresses corresponding to the IP addresses are consistent; if so, continuing periodic probing; otherwise, marking cached entries as suspicious infected entries; constructing and sending ARP request data packets to the network based on the IP addresses corresponding to the suspicious infected entries; receiving the ARP request data packets and performing consistency verification; determining whether the consistency verification result contains ARP spoofing attack behavior; if so, performing overwrite repair and converting the repaired entries into static entries; otherwise, continuing periodic probing; otherwise, sending the probe data packets to the key network nodes.

The application discloses a kind of consistency control methods of safe operation of communication network, the method includes the following steps: S1, the dynamic change in the process of communication network operation is analyzed, communication network node is regarded as point set, the communication state of data interaction between each communication network node is regarded as edge set and is constructed communication relation diagram, and multi-agent system model is established;S2, the random packet loss framework in the operation of communication network is constructed;S3, the consistency control protocol in the operation of communication network under the condition of fraud attack is designed;S4, for the model, framework and designed control protocol established, the system is positively constrained and verified;S5, the consistency of communication network system under random packet loss and fraud attack is verified.The method of the application models communication network using positive system, designs distributed consistency control protocol, solves the data packet loss problem in network communication process, and ensures the consistency control of safe operation of communication network system data.

This invention discloses a power FDIA detection method and system based on multi-granularity dynamic graphs. The method includes: dynamically constructing a two-layer power grid topology graph at the device and region levels based on real-time switch states, and calculating a weighted adjacency matrix; inputting power grid measurement data and the graph structure into the spatiotemporal feature extraction module of the detection model, extracting and fusing spatiotemporal features through a graph attention network and a temporal convolutional network; outputting anomaly probability scores and physical interpretability indicators such as node power imbalance and line power flow anomaly amplitude through a physical consistency constraint module; employing a hierarchical inference strategy for efficient detection, and combining an online incremental learning mechanism to enable the model to continuously adapt to new attacks and topology changes. This invention, by deeply integrating power grid physical constraints and spatiotemporal deep learning, significantly improves the detection accuracy, robustness, and interpretability of covert spoofing attacks, and possesses adaptive capabilities to dynamic topologies.

This disclosure proposes a method and system for longitudinal flight control safety estimation under random deviation conditions, relating to the field of UAV safety control technology. The longitudinal flight control safety estimation method includes: establishing an actual received measurement signal model of the longitudinal flight control system under network transmission subjected to random spoofing attacks using a measurement signal model corresponding to the dynamic model of the longitudinal flight control system under random deviation in a composite state; establishing a dynamic model of an estimator based on the single-rate system model corresponding to the actual received measurement signal model and the measurement signal model under the composite state; determining a dynamic model of the augmented estimation error corresponding to the longitudinal flight control system based on the single-rate system model and the dynamic model of the estimator; and determining the estimator gain to be designed corresponding to the dynamic model of the augmented estimation error. This method enables longitudinal flight control safety estimation.

Embodiments of the present application disclose a sensor spoofing attack detection method, device, equipment and medium. The method comprises: acquiring a current frame collected by a sensor in a vehicle, and at least two historical continuous frames before the current frame; determining a first key point of a target object in the current frame according to a preset directional fast rotation algorithm, and determining a second key point of the target object in each historical image frame, and converting each second key point into a three-dimensional map point; performing key point matching based on a mapping relationship between the three-dimensional map point and the first key point to obtain a key point matching error; acquiring scene data corresponding to the vehicle, and detecting spoofing attack of the sensor based on the scene data and the key point matching error. Through the above technical solution, all sensor attacks and spoofing can be defended and identified, and the detection delay can be controlled within 100 milliseconds, thereby improving the safety of autonomous driving.

This invention relates to the field of smart grid technology, specifically a method and system for quantum time-frequency synchronization in distribution network areas. The method includes: deploying a master reference unit on the distribution transformer side to generate a high-precision reference time signal and distribute entangled photon pairs within the distribution area; deploying multiple slave node synchronization units on each smart terminal device to receive entangled photon pairs and record photon arrival times; calculating the real-time quantum clock difference between each slave node and the master reference unit using the coincidence counting principle; taming the local CPT atomic clock using an adaptive feedback control module based on the quantum clock difference to achieve time synchronization with the master reference unit; and monitoring the status of the distribution network communication network in real time, automatically switching between closed-loop control mode and open-loop hold mode based on the network status. This invention achieves highly reliable and accurate synchronization of distribution network areas without relying on the public network, possesses anti-spoofing attack capabilities, and provides a secure and accurate time reference for distribution network equipment.

This invention discloses a method, system, terminal device, and storage medium for detecting multi-antenna spoofing attacks. This method does not rely on PVT calculation results but instead achieves rapid discrimination directly during the signal tracking stage using observations. This not only reduces reliance on complex hardware configurations but, more importantly, significantly advances the detection process, enabling early warning before attacks affect navigation calculations, thus providing receivers with more timely and efficient anti-spoofing protection.

This invention discloses a method and device for full hand vein biometric recognition to prevent spoofing attacks, relating to the fields of full hand vein recognition, biometric information recognition, and identity authentication. The method includes: Step 1, image acquisition and detection: acquiring a frontal hand image covering the palm and finger areas, ensuring the presence of a palm and a real living person in the frontal hand image; Step 2, ROI extraction: extracting the palm vein ROI and the multi-finger ROI; Step 3, feature extraction: extracting palm vein features and features of the four fingers, and generating corresponding feature hash codes; Step 4, feature matching: first matching the palm vein features, then matching the four finger features; similarity comparison is achieved by calculating the Hamming distance between the feature hash codes; Step 5, score fusion: calculating the matching scores for the palm vein features and the four finger features respectively, then performing Tanh normalization on the matching scores, and finally fusing the normalized matching scores to obtain the final recognition result.

The application discloses a robust face spoofing attack detection method suitable for multi-modal instance misalignment data, and proposes an instance aggregation modal alignment learning framework which is composed of three technologies of class center aggregation, gradient enhancement and boundary relaxation. By independently introducing class center constraints in each modal, the same class instance features are forced to be aggregated to a shared representation, and the distance between different class features is enlarged. A gradient enhancement factor is introduced to dynamically adjust the optimization strength, so that the problem of gradient disappearance in the later aggregation stage is solved. And by using the boundary relaxation mechanism, the constraint is weakened after the features reach the similarity threshold, and the necessary instance diversity is reserved. The application breaks through the limitation that multi-modal learning must rely on expensive instance-level paired data, greatly reduces the data construction cost, fundamentally alleviates the feature deviation between training and reasoning, and significantly improves the robustness and generalization ability of the system in the cross-race and unknown attack scene.

This invention discloses an adaptive closed-loop control method and system for a continuous stirred tank reactor, relating to the field of continuous stirred tank reactor control technology. The method acquires a preset homogeneous evolution model; performs multi-rate sampling through target nodes to obtain monitoring feature data; uses an attack assessment model to identify random spoofing attack characteristics of the communication channel and performs cleaning and noise reduction to obtain a measured data stream; calculates the data deviation, and when it exceeds a preset evolution threshold, uses the measured data stream as trigger data; inputs the trigger data into a preset distributed filter, solves the local filter gain to generate local state estimates, and weights and fuses these estimates to obtain global state estimates; finally, it compares the estimated state with a preset target state value to generate a state deviation signal, corrects the local filter gain, and generates an execution compensation control command. This invention effectively overcomes interference from multi-rate sampling, network attacks, and bandwidth limitations, achieving high-precision and robust adaptive closed-loop control of the reactor.