8 results about "Darknet" patented technology

Methods and systems for network scanning activity detection are disclosed. The methods and systems include: obtaining darknet data from darknet monitoring sensors; applying the darknet data to a trained machine learning model; obtaining one or more labels of honeypot data corresponding to the darknet data based on the trained machine learning model; and provide a result of threat behaviors of internet protocols based on the one or more labels. Other aspects, embodiments, and features are also claimed and described.

To accurately identify a conning-tower server in a botnet and perform detailed analysis, such as classifying the botnet.SOLUTION: An information processing device 10 detects bots that constitute a botnet by analyzing packets observed on a dark net. Based on the packets transmitted from the detected bots, the information processing device 10 classifies the bots according to their types and identifies the common communication destination of the classified bots as a conning-tower server for the bots.SELECTED DRAWING: Figure 2

The application provides a dark web traffic classifier based on natural scene statistics and an anti-attack defense method, and belongs to the technical field of network security. The method comprises the following steps: obtaining dark web traffic original data to form an original data set; using four attack algorithms to attack each data in the original data set to obtain four kinds of adversarial attack samples; using a natural scene statistics method to characterize the dark web traffic original data and the adversarial attack samples generated by one of the four attack algorithms to obtain a parameter set of a generalized Gaussian distribution and an asymmetric generalized Gaussian distribution; training a detector using each parameter set; adding Gaussian noise to the data in the original data set, and training an autoencoder using the data after the Gaussian noise is added; combining the detector and the autoencoder into a two-layer defense mechanism; and identifying unknown traffic data by using the two-layer defense mechanism, feeding unknown traffic data with a classification result of attack traffic to the autoencoder for reconstruction to obtain benign data.

An adaptive, AI-based system for threat analysis of the darknet, consisting of: a) an adaptive network access module configured to establish secure and undetectable connections to Darknet sources using dynamically optimized Tor connections; b) a natural language processing engine specifically developed for cybersecurity, trained on domain-specific datasets to analyze and classify multilingual threat communications; c) a multi-database fusion architecture that integrates document, graph, search and time-series databases through a unified query translation interface; d) a behavioral mimicry system configured to emulate human browsing behavior and randomize browser fingerprints to evade detection by anti-bots; e) a predictive threat intelligence engine that uses machine learning algorithms to predict emerging cyber threats and generate early warnings; f) a distributed crawling infrastructure consisting of containerized crawler instances deployed on geographically distributed, load-balanced servers; and g) an integration and API gateway module configured for secure real-time data exchange with external cybersecurity systems, with all the aforementioned modules communicatively coupled via a central orchestration layer, enabling continuous real-time collection, analysis, correlation and prediction of dark web threats.

The invention relates to an animal attitude estimation method based on key point perception enhancement, and belongs to the field of computer vision. The method comprises the following steps: constructing and training an animal posture estimation network RCBPose; wherein the RCBPose comprises an improved Darknet-53 backbone network, a CSConv module, a Neck network and a Head part, and the improved Darknet-53 backbone network, the CSConv module, the Neck network and the Head part are connected in sequence; performing feature extraction on an image to be subjected to animal attitude estimation through the improved Darknet-53 backbone network to obtain a high-dimensional feature map; performing convolution processing on the high-dimensional feature map through a CSConv module, and outputting a multi-scale feature map; performing feature fusion on the multi-scale feature map through a Neck network to obtain fusion features including animal key point positions and global position information; and the fusion features are identified and predicted through the Head part to obtain spatial position information between animal key points, and animal attitude estimation is completed. The objective of the invention is to solve the technical problem of inaccurate animal posture and key point information caused by shielding and background interference in a complex farm environment in the prior art.

This invention relates to the fields of computer vision and autonomous driving perception technology, and discloses a method for target detection in adverse weather conditions. Addressing the shortcomings of existing technologies such as insufficient real-time performance, weak noise suppression, and poor multi-scale perception capabilities, an AWR-YOLO end-to-end framework is constructed. This framework uses an enhanced CSP-Darknet as its backbone, embeds MS-Block to expand the effective receptive field, and deploys LAS strategies (including GSConv and GDAM) to correct geometric distortion and suppress noise. A selective replacement strategy is employed to optimize deployment, achieving a balance between accuracy and efficiency. Experiments show that the model achieves an mAP50 of 68.89%, an mAP50-95 of 55.69%, and an inference speed of 118.5 FPS, demonstrating excellent robustness and suitability for autonomous driving edge devices.

The present invention discloses a meta-learning-based substation equipment defect detection method and system. The method comprises: obtaining defect images of substation equipment to form a data set; using DarkNet-53 as a detection framework, adding a GCB module for extracting global target information of different fine-grained levels to the last layer of its network, adding an attention mechanism to the last two CSP modules of the network to form an ACSP module; finally, integrating a meta-learning algorithm (MAML) algorithm into the training process of the detection framework to obtain a meta-learning-based defect detection model; training the defect detection model using the training set to obtain a final defect detection model; and using the final defect detection model to identify defect objects and locations in input images to obtain a final substation equipment defect detection result. The advantages of the present invention are: improving the detection capability of the network when facing small sample tasks and improving the accuracy of the detection results.

The application relates to an animal posture estimation method based on key point perception enhancement and belongs to the field of computer vision. The method comprises the following steps: constructing and training an animal posture estimation network RCBPose; wherein the RCBPose comprises an improved Darknet-53 backbone network, a CSConv module, a Neck network and a Head part; the improved Darknet-53 backbone network is used for performing feature extraction on an image to be subjected to animal posture estimation, so as to obtain a high-dimensional feature map; the CSConv module is used for performing convolution processing on the high-dimensional feature map, so as to output a multi-scale feature map; the Neck network is used for performing feature fusion on the multi-scale feature map, so as to obtain fused features containing animal key point positions and global position information; and the Head part is used for identifying and predicting the fused features, so as to obtain spatial position information between animal key points and complete animal posture estimation. The application aims to solve the technical problem that, in the prior art, animal posture and key point information are inaccurate due to occlusion and background interference in a complex breeding farm environment.