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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Tang, Cheng Wang, Qingfeng Cheng, Guozhen Liang, Hao Peng, Jianhua Yang, Meiyue Liu, Wenyan Liu, Ming Sha, Lei Purple Mountain Laboratories Endogenous Security & Safety Research Center Nanjing China National Digital Switching System Engineering & Technological R&d Center Zhengzhou China

Cross-Site Scripting (XSS) vulnerabilities continue to pose a formidable challenge in the realm of web application security due to their prevalence. despite the development of various defense mechanisms, including input validation techniques and code differentiation strategies, these solutions have struggled to achieve broad adoption in real-world environments due to persistent issues such as unreliable detection, high computational overhead, scalability limitations, and compatibility concerns. This paper introduces a novel managed proxy-based framework for XSS defense, positioned strategically between the web browser and the web server. This framework employs a tag-based XSS protection mechanism alongside a defense-in-depth strategy, providing robust protection not only against XSS attacks but also against other web threats. Furthermore, the framework integrates advanced web front-end hardening techniques to significantly complicate the exploitation of potential vulnerabilities. Comprehensive practical evaluations indicate that the proposed framework is both flexible and effective, demonstrating outstanding performance across multiple dimensions. © 2024 IEEE.

关键词： Web browsers

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Near-field Channel Estimation for dynamic Metamaterial Antenna systems 9

Near-field Channel Estimation for Dynamic Metamaterial Anten...

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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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Altair: resource-Efficient Optimization and deployment for data Plane Programs

SSRN

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SSrN 2024年

作者： Cui, Zixi Hu, Yuxiang Tian, Le Yi, Peng Hou, Saifeng Chen, Hongchang PLA Strategic Support Force Information Engineering University Zhengzhou450001 China National Digital Switching System Engineering and Technological R&D Center Zhengzhou450001 China

As network applications are increasingly offloaded to the programmable switches, program fitting problems come to the fore, which means effectively mapping the programming entities into the hardware resources. However, existing works fail to achieve resource-efficient deployment in an acceptable time. In this paper, we present Altair, a high-performance compiler system for program optimization and resource allocation. In addition to proactively optimizing the program by reducing logical redundancy, Altair designs a two-layer framework to generate the optimal mapping solutions with respect to all the hardware constraints while minimizing resource usage. We also propose a new feedback mechanism between the two layers to make the best trade-offs among bottleneck resources and accelerate the solving process. Altair not only converts the network program into a functionally identical and compilable codes, but also uses up to 30% fewer hardware resources with at least 10X shorter execution time than state-of-the-art. © 2024, The Authors. All rights reserved.

关键词： resource allocation

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Altair: resource-Efficient Optimization and deployment for data Plane Programs

SSRN

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SSrN 2024年

As network applications are increasingly offloaded to the programmable switches, program fitting problems come to the fore, which means effectively mapping the programming entities into the hardware resources. However, existing works fail to achieve resource-efficient deployment in an acceptable time. In this paper, we present Altair, a high-performance compiler system for program optimization and resource allocation. In addition to proactively optimizing the program by reducing logical redundancy, Altair designs a two-layer framework to generate the optimal mapping solutions with respect to all the hardware constraints while minimizing resource usage. We also propose a novel feedback mechanism between the two layers to make the best trade-offs among bottleneck resources and accelerate the solving process. Altair not only converts the network program into a functionally identical and compilable codes, but also uses up to 30% fewer hardware resources with at least 10X shorter execution time than state-of-the-art. © 2024, The Authors. All rights reserved.

关键词： resource allocation

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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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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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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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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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