11results about How to "Improve authentication accuracy" patented technology

The invention discloses a multi-factor network security authentication method and system based on an SDN (Software Defined Network). The method comprises the following steps: constructing a multi-dimensional authentication space comprising an identity feature dimension, a behavior feature dimension and an environment feature dimension in an SDN controller; decomposing the eigenvalues into deterministic sequence components and random sequence components by adopting a dynamic sequence decomposition method, and calculating the correlation degree of the eigenvalues among dimensions; according to the method, the stability index is calculated based on the feature stability evaluation function, the fusion authentication decision function is constructed to carry out authentication decision, safe and reliable network authentication is achieved through the technical means of multi-dimensional feature fusion analysis, dynamic sequence decomposition, self-adaptive decision and the like, and the method has high practical value.

The invention discloses an access control system based on face recognition, relates to the technical field of biological recognition, and solves the problem that feature parameters are difficult to extract according to face images and iris images. Secondly, face comparison detection is difficult to carry out according to the extracted feature parameters of the face image; then, it is difficult to analyze the iris image of the incomer according to the face comparison result, and it is difficult to analyze the abnormal behavior according to the monitoring image; and finally, the risk coefficient is difficult to comprehensively analyze, and the risk level of the access control system is difficult to assess. By analyzing the face image and the iris image, the identity verification accuracy of the access control system is improved; abnormal behaviors of strangers in a monitoring image are analyzed by using a deep learning model, and a risk coefficient is comprehensively analyzed according to an identification failure rate and a potential abnormal coefficient, so that the risk level of the access control system is evaluated.

This invention discloses a behavior-adaptive continuous identity authentication method and system. Through component attribution analysis, the basic continuous identity authentication model is deconstructed into multiple computational units. These computational units are used as components, and an ablation vector is designed to determine whether the component's parameters are set to zero during inference. Ablation experiments are conducted on the components based on a user behavior dataset to obtain a set of optimal-performing components. Through parameter search adaptation, scaling factors are applied to the parameters of key components based on the current user's behavior data. With the optimization objective of maximizing identity authentication accuracy, an optimization search is performed to obtain the optimal scaling factor. This optimal scaling factor is then applied to the key components of the basic continuous identity authentication model to generate an adaptive continuous identity authentication model for the current user's behavior, enabling continuous identity authentication for the current user. This invention improves the authentication accuracy and computational efficiency of continuous identity authentication, and is particularly suitable for mobile devices.

The present invention discloses a method for secure authentication of vehicle-mounted wireless devices based on deep metric learning, and belongs to the technical field of machine learning. The method comprises: pre-processing radio frequency signals of multiple vehicle-mounted wireless devices to generate a training data set; constructing an open class authentication model based on improved class anchor loss; removing a part of the training data set as improper signal samples, and sending the remaining samples to network training respectively to achieve network model optimization; and authenticating the signals of the vehicle-mounted wireless devices to be authenticated. The present invention achieves efficient identification of vehicle-mounted wireless devices and malicious attack devices while improving the feature clustering effect by constructing an open class recognition model with improved class anchor loss, and balances the recognition rates of the two. At the same time, sparsity is introduced by using a multi-scale feature extraction module, and features are extracted by using different convolution kernels between adjacent convolution layers, and the results of multiple layers of convolution are spliced ​​as the input of the next layer, thereby improving the authentication accuracy of the model without increasing the computational overhead.

The present invention discloses an item authentication method, system and storage medium based on a digital ID card, which belongs to the technical field of data identification and authentication, including: generating a digital ID card of an item based on data information, generating a first code corresponding to the digital ID card based on an encryption mode; generating a blockchain network based on a storage node, dividing the first code and storing it in the blockchain network in sequence, and generating verification data and combined data corresponding to the first code; obtaining a search node in the blockchain network, generating a retrieval code corresponding to the data to be verified, generating a decryption key based on the encryption mode and the search node, decrypting the retrieval code based on the decryption key, and generating data to be analyzed; constructing an authentication mode, determining whether the data to be verified is modified based on the authentication mode, verification data, combined data and data to be analyzed, and completing the authentication of the item to be verified based on the determination result. The present invention can improve the security and accuracy of item authentication.

The invention discloses an AGV (Automatic Guided Vehicle) physical layer authentication method based on path loss and speed and position estimation. The method comprises the following steps that: a plurality of edge computing units receive IDs (Identity) and speeds and positions thereof reported by to-be-authenticated AGVs (Automatic Guided Vehicles); each edge calculation unit is used for extracting a channel frequency response matrix from the signal sent by the trolley and inputting the channel frequency response matrix into a Gaussian process regression model to obtain predicted path loss, position and speed; calculating acceptance domains of the path loss, the position and the speed according to the reported ID and the predicted path loss, the position and the speed; calculating path loss at the current moment according to the reported position; judging whether path loss and speed are in an acceptance domain or not, if so, authenticating that the trolley is legal, judging whether the position is in the acceptance domain or not, and if so, authenticating that the trolley does not encounter position forgery / spoofing attack; otherwise, the trolley is authenticated to be illegal; the authentication result is reported to other edge computing units; and finally authenticating the legality of the trolley according to all authentication results. According to the invention, the authentication accuracy and robustness are improved.

A two-element authentication system (90) is provided with an additional authentication device (200). The additional authentication device (200) has an authentication operation determination unit (250). An authentication operation determination unit (250) determines an authentication operation when the number of character inputs when a user inputs a target password to the password authentication device (100) is not the minimum number of character inputs for inputting the target password. On the basis of auxiliary information indicating a job executed by the user on the password authentication device (100) when the user has entered the target password, and information acquired by the sensor (201) during a time period when the user has entered the target password, it is determined whether or not the user has entered the target password on the password authentication device (100).

The invention discloses a distributed optical fiber sensing event noise reduction method based on a denoising mask auto-encoder. The method comprises the following steps: step 1, carrying out DAS data acquisition; step 2, data structuring; step 3, data enhancement; step 4, constructing a pre-training network based on a de-noising mask auto-encoder; step 5, non-label data pre-training: taking the pre-processed non-label image as input, adding noise and masking, reconstructing an original image through an encoder E1 and a decoder D, carrying out repeated training to enable the reconstructed image to approach to a real image, and storing the weight of the pre-trained encoder E1; step 6, constructing a downstream classification task network; step 7, performing fine tuning on the downstream classification task network: using a small amount of labeled images, performing fine tuning on an encoder E2 under a random smooth framework, so that the encoder E2 keeps consistent prediction under noise input, and the authentication robustness is improved; according to the invention, end-to-end high-fidelity noise reduction is realized, and the authentication accuracy is improved.

The invention relates to an unsupervised multi-user physical layer authentication method based on a twin converter neural network, and belongs to the technical field of wireless communication security. The method specifically comprises the following steps: system modeling: constructing a dynamic multi-user wireless communication system consisting of a plurality of legal sending ends in a mobile state, at least one attacker and a single legal receiving end, and collecting channel state information (CSI) of each device under a multipath and time-varying channel through wireless links such as WiFi; carrying out expansion, amplitude calculation and normalization on the collected complex CSI to obtain CSI amplitude samples arranged according to a time sequence; the method comprises the following steps: constructing CSI of the same legal equipment at adjacent moments into a positive sample pair based on short-time invariance characteristics of a dynamic channel, and constructing CSI with a relatively large time interval and / or CSI from other equipment into a negative sample pair, so as to form an unsupervised contrast learning sample set; the method comprises the following steps: constructing a lightweight twin Transform model CSI-UST, carrying out linear embedding, position coding and self-attention feature extraction on a CSI amplitude sequence, and training by comparing a loss function, so that the CSI feature distance of the same equipment within a short time is converged, and the CSI feature distance of different equipment or moments is greater than a preset boundary; in an authentication stage, CSI to be authenticated and historical CSI of each legal device are sequentially input into CSI-UST to calculate a feature distance, a source device is determined based on a distance threshold, a CSI sample of a corresponding device is updated when authentication succeeds, and a new device is incorporated into a legal device set when threshold determination fails and upper layer confirmation passes, thereby realizing dynamic expansion without updating model parameters. The method does not need to depend on the CSI of an attacker or large-scale annotation data, is low in model parameter quantity and small in memory occupation, can still keep high authentication accuracy and low misjudgment rate in various dynamic scenes, and is suitable for being deployed in resource-limited Internet of Things equipment.

The invention relates to an identity permission authentication method and device, equipment and a storage medium. The signature value can be generated according to the information of the user, whether the user has the access permission of the target block chain is verified based on the signature value, after it is determined that the user has the access permission of the target block chain, the comprehensive permission value of the user to the requested target data is determined based on the information of the user, and after the comprehensive permission value reaches the preset requirement, the user can access the target block chain. According to the method, the user is determined to have the target data acquisition authority, and finally the target data on the target block chain is sent to the user, so that whether the user has the target data acquisition authority on the block chain or not can be determined by verifying the user information twice, and the data acquisition authority of the user on the block chain can be accurately authenticated; the authentication precision of the data authority on the block chain is improved, and the security of the data on the block chain is improved.

This invention discloses a radio frequency fingerprinting method and apparatus based on multi-dimensional time series feature fusion, comprising: extracting sampled data corresponding to the signal data of a wireless communication device and performing standardization processing; extracting multi-dimensional signal features from the processed sampled data and constructing a multi-dimensional feature vector; constructing a multi-dimensional feature sequence and corresponding labels based on the multi-dimensional feature vectors of consecutive frames to obtain a dataset for wireless communication device authentication; training a classifier using the dataset to obtain a trained classifier; inputting the signal feature sequence to be identified into the trained classifier; determining the optimal sequence length based on the obtained initial identification results; using the identification results with the optimal sequence length corresponding to the initial identification results as the target identification results; and performing multiple authentications based on the target identification results to obtain the final identification result. This invention improves the fingerprint recognition accuracy of devices by fully exploiting the sequential characteristics of signals in the time dimension.