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9th International Conference on Computer and Communications, ICCC 2023

作者： Wang, Shifei Guo, Shuming Tang, Zhiqing Han, Gangtao Shen, Lingfeng Zhengzhou University School of Cyber Science and Engineering Zhengzhou China National Digital Switching System Engineering & Technological R&d Center Zhengzhou China Zhengzhou University School of Electrical and Information Engineering Zhengzhou China China Information Technology Designing & Consulting Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350317251

The next generation of wireless technology, 6G, will likely incorporate advanced technologies such as extremely large-scale antenna arrays (ELAA) and millimeter wave (mmWave) communication. These cutting-edge features have the potential to revolutionize the way we communicate wirelessly, near-field communication (NFC) can not be ignored in future 6G mobile networks. Compared to far-field communication, the waves that propagate in the near-field region have a spherical wavefront. By utilizing the time agile of dMA and the correlation between near-field guidance vectors, we propose a novel channel estimation algorithm. Compared with the classic two-dimensional (2d) multiple signal classifier (MUSIC) algorithm, this algorithm effectively reduces computational complexity and ensures estimation accuracy. Simulation results show that the proposed channel estimation scheme achieves the expected results. © 2023 IEEE.

关键词： Metamaterial antennas

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Virtual Network Function resource requirements Prediction Model Based on CNN-GrU 22

Virtual Network Function Resource Requirements Prediction Mo...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Chunqiao Mao Peng Yi dan Li Juan Shen School of Cyber Science and Engineering Zhengzhou University China National Digital Switching System Engineering & Technological R&D Center of China China

ISBN: (纸本)9781450397148

The rise of network function virtualization (NFV) technology makes the realization of network function change from hardware middleware to virtual network function (VNF). The existing methods allocate fixed resources to each VNF instance, but this resource allocation method will cause resource waste, which will affect the quality of service. The resource requirements prediction model solves the resource allocation problem by predicting the change of resource requirements. In this paper, deep learning is used to solve the regression prediction problem, and a VNF resource requirements prediction model based on convolutional neural network (CNN) and gated recurrent unit (GrU) is proposed. Compared with other single model and combined model, the experimental results show that the prediction error rate of the proposed model is reduced by 23.3%.

关键词： resource requirements prediction Virtual network function Convolutional neural network Gated circulation unit

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A comprehensive evaluation of diversity systems based on mimic defense

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Science China(Information Sciences) 2021年第12期64卷 258-259页

作者： Qing TONG Yunfei GUO National Digital Switching System Engineering and Technological R&D Center

dear editor,diversity systems increase attacking challenges through mechanisms such as system diversification design, redundancy, or dynamic rotation. They resist various external threats, including the unknown attacks on the system. Several research studies have been conducted to analyze and validate the security and effectiveness of diversity systems,

关键词： Information systems and Communication Service

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distributed Mimic Judgment Algorithm based on Multi-Strategy decentralization Model 22

Distributed Mimic Judgment Algorithm based on Multi-Strategy...

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7th International Conference on Cyber Security and Information engineering, ICCSIE 2022

作者： Song, Yu Sun, He Wang, Jiaxing Liu, Wenhe Zhang, Jianhui School of Information Engineering Zhengzhou University Zhengzhou China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Centerr Zhengzhou China

ISBN: (纸本)9781450397414

The current mimic adjudication model is centered on a single adjudicator, and the adjudication algorithm is relatively single and static, which cannot cope with today's complex and changeable network environment. First, a distributed adjudication model is proposed to decentralize the adjudicator, each sub-arbiter is different from the previous use of all executive results, but randomly selects the output of the executive as the input incentive. Secondly, based on this model, a consensus mechanism algorithm is proposed to iteratively normalize the local adjudication results of the sub-arbiters to achieve the global optimal adjudication result. The analysis of the simulation experiment results shows that the algorithm proposed in this paper is more accurate than the traditional adjudication algorithm when multiple executors are online. The algorithm can further reduce the risk of common-mode escape and improve the security of the mimic defense system. © 2022 ACM.

关键词： Network security

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downlink MMSE Beamforming for dynamic Metasurface Antennas

Downlink MMSE Beamforming for Dynamic Metasurface Antennas

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Electronic Information engineering and Computer Science (EIECS), 2021 International Conference on

作者： Zhiqiang Han Shuming Guo Shifei Wang School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

dynamic metasurface antenna (dMA) array is a new type of array structure with low complexity and low cost, which is emerging as a promising technique for next-generation wireless networks. In this paper, a downlink multi-user MISO system based on dMA arrays is investigated, and the mean square error (MSE) is chosen as a performance metric to characterize the transmission reliability. The beamforming performance of dMA arrays is investigated using the minimum mean square error (MMSE) criterion. For the multiuser scenario, the optimization problem minimizes MSE by jointly optimizing the transmit precoder and dMA weights while satisfying the transmit power constraint. Our numerical results show that the proposed MMSE scheme converges fast and performs close to full digital beamforming.

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A BErT-GCN-Based detection Method for FBS Telecom Fraud Chinese SMS Texts

A BERT-GCN-Based Detection Method for FBS Telecom Fraud Chin...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Xuanming Zhang ruiyang Huang Lixiao Jin Fangjie Wan School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

In the past decade, Fake Base Stations (FBS) have been consistently employed by criminals to target mobile users through spam text messages. despite the introduction of several techniques to mitigate this problem, spam messages remain a persistent and challenging issue in some countries, such as China, resulting in billions of dollars in annual economic losses. Therefore, this paper proposes an algorithm named BErT-GCN that combines large-scale pre-training models with Graph Convolutional Networks (GCN) for multi-class detection of fraudulent base station telecommunications scams in Chinese text messages. First, BErT (Bidirectional Encoder representation from Transformers) is used to encode the corpus and generate word embeddings. Subsequently, the generated word embeddings are fed into GCN for training. Finally, a Softmax layer is employed to perform multi-class detection on fraudulent base station telecommunications scams in Chinese text messages. Experimental results demonstrate that the proposed model achieves favorable performance on the FBS-SMS-dataset and exhibits outstanding performance in the domain of multi-class detection.

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Malicious Website detection Method Based on BGresNet Multi-feature Fusion

Malicious Website Detection Method Based on BGResNet Multi-f...

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IEEE Eurasia Conference on IOT, Communication and engineering (ECICE)

作者： Lixiao Jin ruiyang Huang Xuanming Zhang School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

Website security detection is important for Internet security. Existing machine learning-based malicious UrL detection methods have a low accuracy and weak generalization ability. Thus, we proposed a new multi-feature fusion malicious website detection model BGresNet by integrating the advantages of Bidirectional Encoder representations from Transformers (BErT), Graph Convolutional Network (GCN), and residual neural network (resNet). The method integrates three features: Uniform resource Locator (UrL) creator, rule features, and website titles. First, we used BGresNet to process UrL characters and website titles separately, transforming them into vector representations. Then, these two vectors were fused with UrL rule vectors. Finally, we employed the softmax function to realize the detection of malicious websites. The experimental results showed that the proposed model exhibited significant superiority in detecting malicious websites, providing a new and effective method for malicious UrL detection in Internet security.

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User Profile Construction Based on Multi-Channel Fusion

User Profile Construction Based on Multi-Channel Fusion

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Chao Miao Shaomei Li Fangjie Wan School of Cyber Science and Engineering Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

Aiming at the shortcomings of existing user profile construction algorithms that do not fully utilize contextual structural information and multidimensional feature information., a user profile construction model based on the pre-training model NEZHA, deep Pyramidal Convolutional Neural Networks (dPCNN), Gated recurrent Unit (GrU), and attention mechanism is proposed, NCdGA. The method uses multiple channels to process the text, capturing different dimensions of information and constructing features to reduce the prediction bias that occurs when a single model captures a single feature. The experimental results show that the NCdGA model has better accuracy and effectiveness compared with other modeling methods.

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An Incremental dynamic Community detection Algorithm Based on Node Participation 6

An Incremental Dynamic Community Detection Algorithm Based o...

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6th IEEE International Conference on Cloud Computing and Big data Analytics, ICCCBdA 2021

作者： Li, Hui Chen, Fucai Zhang, Jianpeng National Digital Switching System Engineering Technological R D Center Information Engineering University Zhengzhou China

ISBN: (纸本)9780738105338

Many networks in the real world are dynamically changing over time, and the community structure also changes accordingly. The community detection in the dynamic and constantly evolving network has received great attention from researchers. In order to uncover the community structure in the dynamic network, this paper proposes an incremental dynamic community detection algorithm based on node participation (NPICd). It defines the node participation to the community and the community fitness. In response to the changes of nodes and edges in the network, different update strategies are proposed to discover the community at different moments in an incremental manner. The experimental results on the real network show that compared with several widely used methods, our algorithm can quickly and accurately mine the community structure in the dynamic network. © 2021 IEEE.

关键词： Population dynamics

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CLMSI: A Novel Image-Text Named Entity recognition Method Based on Contrast Learning and Multimodal Semantic Interaction 11

CLMSI: A Novel Image-Text Named Entity Recognition Method Ba...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNEr) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNEr accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNEr benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

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