Biblio

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

To meet the application need of dynamic visualization VR display of 3D time-varying field, this paper designed an immersive visualization VR system of 3D time-varying field based on the Unity 3D framework. To reduce visual confusion caused by 3D time-varying field flow line drawing and improve the quality and efficiency of visualization rendering drawing, deep learning was used to extract features from the mesoscale vortex of the 3D time-varying field. Moreover, the 3D flow line dynamic visualization drawing was implemented through the Unity Visual Effect Graph particle system.

Recently, mobile ad-hoc networks (MANET) have been widely used in several scenarios. Due to its generally high demands on clock synchronization accuracy, the conventional synchronization algorithms cannot be applied in many high-speed MANET applications. Hence, in this paper, a clock synchronization algorithm based on motion information such as the speed of nodes is proposed to eliminate the error of round-trip-time correction. Meanwhile, a simplified version of our algorithm is put forward to cope with some resource-constrained scenes. Our algorithm can perform well in most situations and effectively improve the clock synchronization accuracy with reasonable communication overhead, especially in high-speed scenes. Simulation results confirm the superior accuracy performance achieved by our algorithm.

With the development of scientific information technology, the emergence of the Internet of Things (IOT) promoted the information industry once again to a new stage of economic and technological development. From the perspective of confidentiality, integrity, and availability of information security, this paper analyzed the current state of the IOT and the security threats, and then researched the security primary technologies of the IOT security architecture. IOT security architecture established the foundation for a reliable information security system for the IOT.

Nowadays, encrypted traffic classification has become a challenge for network monitoring and cyberspace security. However, the existing methods cannot meet the requirements of encrypted traffic classification because of the encryption protocol in communication. Therefore, we design a novel neural network named Stereo Transform Neural Network (STNN) to classify encrypted network traffic. In STNN, we combine Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) based on statistical features. STNN gains average precision about 95%, average recall about 95%, average F1-measure about 95% and average accuracy about 99.5% in multi-classification. Besides, the experiment shows that STNN obviously accelerates the convergence rate and improves the classification accuracy.