Abstract: Disclosed are a Nested Named Entity Recognition method based on part-of-speech awareness, system, device and storage medium therefor. The method uses a BiLSTM model to extract a feature of text word data in order to obtain a text word depth feature, and each text word of text to be recognized is initialized into a corresponding graph node, and a text heterogeneous graph of the text to be recognized is constructed according to a preset part-of-speech path, the text word data of the graph nodes is updated by an attention mechanism, and the features of all graph nodes of the text heterogeneous graph are extracted using the BiLSTM model, and a nested named entity recognition result is obtained after decoding and annotating. The present disclosure can recognize ordinary entities and nested entities accurately and effectively, and enhance the performance and advantages of the nested named entity recognition model.

Abstract: Disclosed is a system for classifying encrypted traffic based on a data packet. The system includes a traffic capture module, a traffic analysis module, and a traffic classification module. The system collects data packets from a network flow to construct a machine learning model, so as to classify encrypted traffic and identify normal traffic and malicious traffic. In a process of constructing a feature matrix, basic spatial-temporal features, header features, load features, and statistical features are obtained. In addition, behavioral features of the data packets are obtained and used to demonstrate differences between the normal traffic and the malicious traffic. Moreover, the present disclosure focuses on a difference between different versions of an encryption protocol, especially a transport layer security (TLS) protocol, and introduces the difference into a model for analysis, so that the system classifies encrypted traffic in a more efficient manner.

Abstract: The invention discloses a weighted heterogeneous graph-oriented malicious behavior identification method, system and storage medium. The method comprises the following steps: constructing an inductive graph neural network model. The inductive graph neural network model comprises a subgraph extraction module, a plurality of feature vector generation and fusion modules and a classification learning module; performing training and learning for the inductive graph neural network model, extracting subgraphs, learning the latent vector representation of nodes in the subgraphs, obtaining a plurality of subgraph feature vectors corresponding to the subgraphs, and fusing a plurality of subgraph feature vectors. The node feature vector obtained by fusion is used for classification learning in the classification learning module; using the trained inductive graph neural network model for malicious behavior identification.

Abstract: An APT organization identification method, system and storage medium based on a stacking ensemble are provided, the method comprising: using a TF-IDF algorithm combined with an n-gram to extract and vectorize behavior features from malware samples to form a malicious behavior vector feature set; based on the malicious behavior vector feature set, calculating correlations between features and chi-square values between the features and categories, performing screening twice on the malicious behavior vector feature set to obtain an improved low-dimensional feature subset data; constructing a multi-model fusion stacking ensemble, learning an APT organization identification model, using the APT organization identification model to perform an identification on new ATP attacks.

Abstract: A method to create and run a program in a computer including includes: connecting a preset USB-HID device to a computer, wherein the USB-HID device is provided with a first storage area, stores working codes in the first storage area in binary form; the USB-HID device injects a control instruction to the computer, such that the computer creates a text document or opens a command line program according to the control instruction; if the computer creates a text document, the USB-HID device inputs the working codes in the form of keyboard sequence to generate an application corresponding to the working codes, and controls the computer to run the application; and if the computer opens a command line program, the USB-HID device inputs the working codes in the form of keyboard sequence, such that the computer directly runs the working codes through the command line program.

Abstract: The present invention provides a slurry catalyst and a method for preparing the same, and belongs to the technical field of preparing catalyst. Particularly, the present invention provides a slurry catalyst directly used in a slurry bed reactor for synthesizing methanol and dimethyl ether and a method for preparing the same, which uses the complete liquid phase preparation from solution to slurry without the conventional slurry-producing process of firstly forming a solid catalyst and dispersing it into an inert medium after crushing and milling. This catalyst mainly comprises Cu, Zn, Al and Zr, wherein atomic ratios of each of components are Cu/Zn/(Al+Zr)=1/0.1-5/0.15-15 and Zr/Al=1:1.0-1:30, and one or two selected from the group consisting of lanthanide metals, Mn, Mo, Si, V, W, Cr, Mg, Ni, K, Pd, Rh, Ru, Re, Pt and Sr is used a promoter.

Abstract: The present invention provides a slurry catalyst and a method for preparing the same, and belongs to the technical field of preparing catalyst. Particularly, the present invention provides a slurry catalyst directly used in a slurry bed reactor for synthesizing methanol and dimethyl ether and a method for preparing the same, which uses the complete liquid phase preparation from solution to slurry without the conventional slurry-producing process of firstly forming a solid catalyst and dispersing it into an inert medium after crushing and milling. This catalyst mainly comprises Cu, Zn, Al and Zr, wherein atomic ratios of each of components are Cu/Zn/(Al+Zr)=1/0.1-5/0.15-15 and Zr/Al=1:1.0-1:30, and one or two selected from the group consisting of lanthanide metals, Mn, Mo, Si, V, W, Cr, Mg, Ni, K, Pd, Rh, Ru, Re, Pt and Sr is used a promoter.