10 results about "Channel analysis" patented technology

The present application relates to a kind of password algorithm artificial intelligence side channel analysis method based on dimension reduction and clustering, belong to information security cryptography field.First, the energy trace obtained is analyzed, to determine the area of the energy trace in which the execution of the decryption process is related to secret information operation.Analyze the energy trace of the key operation part, use the method based on sliding window gradient value to divide each operation, obtain energy trace segment.Combined with the implementation of cryptographic algorithm, the energy trace segment is analyzed, and the operation type is identified.Use principal component analysis on energy trace segment for dimension reduction, take the component corresponding to the expected dimension as the principal component of each operation.Combined with the characteristics of energy trace segment, the cluster number obtained by clustering is estimated.Use clustering algorithm to cluster the energy trace segment after dimension reduction, analyze the clustering result to determine the operation type represented by each cluster, obtain the operation type corresponding to the energy trace segment, finally obtain the secret index sequence, realize the efficient analysis of the side channel of cryptographic algorithm.

This invention relates to the field of computer technology and provides an algorithm for analyzing the complexity of EEG signals based on multi-domain fusion. The method includes: data preprocessing to acquire multi-band, multi-channel EEG signals; single-scale single-channel analysis at the single-scale level, performing single-scale single-channel analysis on the multi-band, multi-channel EEG signals and outputting multi-band fuzzy entropy; multi-scale single-channel analysis at the multi-scale level, performing multi-scale single-channel analysis on the multi-band, multi-channel EEG signals and outputting a multi-scale fuzzy entropy vector; and multi-scale multi-channel analysis at the multi-scale level, performing multi-scale multi-channel analysis on the multi-band, multi-channel EEG signals and outputting multi-channel multi-scale fuzzy entropy. This invention, through multi-band decomposition combined with multi-scale multi-channel analysis, can comprehensively capture the dynamic variation characteristics of EEG signals across different frequency bands, scales, and channels, effectively solving the problem of incomplete information extraction in traditional methods and improving the ability to characterize the dynamic characteristics and complexity of EEG signals.

The present application provides a kind of process single-line diagram weld AI identification method, medium and system, belong to weld identification technical field, the present application is by to process pipeline single-line diagram PDF file starting vector channel and raster channel Double-channel analysis and generate global coordinate mapping table, the double-channel result is sent into curvature perception weld detection model output candidate frame and obtains page level weld candidate frame set by non-maximum suppression, executes ant colony pheromone diffusion path tracking algorithm based on pipeline skeleton diagram to determine weld order sequence, executes different numbering strategy according to adaptive numbering confidence comprehensive score value and generates weld information table, executes perspective correction after sending in character recognition module after executing the certificate image of construction personnel to fill the result into weld information table, to field variation trigger approval process and synchronize data to welding management platform, solve the problem that process pipeline single-line diagram weld cannot be accurately identified under complex topological pipeline structure.

This application discloses a computing system configured to measure a step response of a channel in an electronic device, determine a feedback voltage that a decision feedback equalizer adds to the channel in response to detecting inter-symbol interference in received symbols, and predict a signal integrity of the channel based, at least in part, on the step response of the channel and the feedback voltage. The computing system can utilize the predicted signal integrity to determine a probability that the feedback voltage added by the decision feedback equalizer to the channel was erroneous, and determine the signal integrity of the channel based, at least in part, on the step response of the channel, the feedback voltage, and the probability that the feedback voltage added by the decision feedback equalizer to the channel was erroneous.

This application relates to the field of blockchain security technology and discloses a method and system for analyzing user behavior in smart contract interactions based on network dynamics. The method first parses the smart contract program, constructing a heterogeneous graph containing multiple contracts. Next, the edge weights of the heterogeneous graph are dynamically reconstructed to distinguish key interaction paths from internal computational noise. Then, based on the reconstructed edge weights, cross-node message passing and feature aggregation are performed on the graph to generate updated node features. Finally, based on the updated node features, local node importance analysis and global graph structure analysis are performed and fused to determine whether the smart contract interaction behavior constitutes a malicious attack pattern. This invention effectively solves the problems of semantic silos, noise interference, and information dilution in cross-contract scenarios through dynamic weight reconstruction and dual-channel analysis, significantly improving the detection accuracy and robustness against complex cross-contract attacks.

The present application relates to the technical field of information security, and in particular to a multi-label learning based side-channel analysis method for different devices, which first proposes a shortest principle to form the energy traces corresponding to the single-byte key labels of each device according to the characteristics of the energy trace data sets collected under different devices and the requirements for deep learning training, and forms a multi-label data set for different devices, then applies the multi-label learning technology to the side-channel environment, realizes the algorithm adaptation method of multi-label learning based on a convolutional neural network, further sets various hyperparameter combinations to optimize the multi-label learning model, and finally uses the corresponding test sets under each device to evaluate the byte key attack effect of the multi-label model on the cryptographic algorithm under different devices, so as to verify the generalization ability of the multi-label learning model. The present application enables a single model to perform attacks on the same cryptographic algorithm under different devices, increases the attack efficiency, and reduces the model construction and training time.

Data of a plurality of channels of a spread-spectrum network are received. For example, the data of the plurality of channels of the spread-spectrum network may be captured by a spread-spectrum router (e.g., a WiFi router). The data of the plurality of channels of the spread-spectrum network is analyzed to identify an anomalous cross-channel pattern across the plurality of channels of the spread-spectrum network. For example, the attack may be a sequential attack across each of the channels of the spread-spectrum network. In response to identifying the anomalous cross-channel pattern across the plurality of channels of the spread-spectrum network, an action is taken to protect the spread-spectrum network. For example, the action may be to notify an administrator of the spread-spectrum network that a potential attack is occurring on the spread-spectrum network or to block access to the spread-spectrum router.

Systems and methods described herein provide for a processing unit that generates side-channel countermeasures to protect sensitive information. The processing unit can include two circuits, a primary circuit and a countermeasure circuit that each process the same input but each circuit can be configured by respective randomized clock signals and respective secrets or secret keys. The secret key used by the countermeasure circuit, can be a different secret than the secret used by the first circuit, but it can be based on the secret used by the first circuit. The output of the cryptographic algorithm of the first circuit can have an associated side-channel leakage or emission (power consumption, electromagnetic radiation, sound, vibration, temperature variation, etc.). The countermeasure circuit's side-channel leakage can obscure the first circuit's side-channel leakage or make it difficult to interpret the side-channel leakage of the first circuit.

This invention relates to a hardware Trojan side-channel detection method based on an autoencoder, belonging to the field of network security side-channel analysis technology, specifically a side-channel detection method for artificial intelligence hardware Trojans based on an autoencoder. The method of this invention is easy to implement, the normal data used for training is easy to collect, and the autoencoder method has relatively low computational cost, making it suitable for application in practical scenarios. This method uses side-channel information as input to the autoencoder and performs anomaly detection based on the encoding results obtained by the encoder, providing intuitive and accurate results.