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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

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Computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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The Adaptive Fault-tolerant Routing Based on an Improved Local security information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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Research on the Application of Digital Signature in University Electronic Government System

Research on the Application of Digital Signature in Universi...

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Tianma Wang Dongmei Zhao Jianjun Qi International Education Institute North China Electric Power University Beijing China College of Computer and Cyber Security Hebei Normal University Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang China

This article analyzes the principles and processes of digital signature technology, as well as its application methods in the university e-government system. On the one hand, it meets the business management needs of university, and on the other hand, the application of digital signature and timestamp electronic signature ensures the security of university e-government SystemThe university e-government management system based on digital signature technology verifies the identity of users such as the sender and receiver of files, and issues digital certificates. The sender uses digital certificates for system authorization. Signs and encrypts confidential files such as documents through digital signatures to protecting the security of electronic file transmission. Upload the digitally signed and encrypted documents to the system, which completes the sending and receiving of the documents. On the one hand, the recipient downloads documents through the system and uses digital signatures to verify the integrity and authenticity of the documents. On the other hand, digitally sign the document and upload it to the system. Both parties complete the distribution and upload of documents through the system.

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Multi-Feature Fusion Identity Generation Method for Fixed Location 5G Terminal Devices 5

Multi-Feature Fusion Identity Generation Method for Fixed Lo...

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5th International Conference on information Technologies and Electrical Engineering, ICITEE 2022

作者： Shao, Zhipeng Wang, Chen Guo, Yaqiong Xu, Junbo Liu, Ruolin State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Jiangsu Nanjing China State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Zhejiang Hangzhou China

ISBN: (纸本)9781450396806

While 5G is developing rapidly, it also faces greater security threats, and achieving reliable terminal identification is a prerequisite for ensuring 5G network security. For the identity reliability problems of the accessed fixed location 5G terminal devices, a multi-feature fusion identity generation method is proposed. Firstly, the channel feature sequence that can reflect the location information of 5G terminal devices is obtained at the base station side based on Sounding reference signal;then the envelope information of the intercepted leading signal is extracted at the base station side using the method of envelope detection in communication reconnaissance to obtain the estimated time width of the leading signal;finally, a multi-feature fusion identity generation method is proposed by combining the permanent identifier SUbscription Permanent Identifier that represents the terminal identity. The study shows that the key authentication based on the fusion of multiple features improves the identification security significantly compared with the key authentication based on the single feature identity, and is also more reliable than other terminal authentication techniques. © 2022 ACM.

关键词： Location

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DFMC:Feature-Driven Data-Free Knowledge Distillation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Wenhao Chen, Shunpeng Xu, Shibiao Xu, Guangyuan Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from teacher networks without access to the real dataset. However, generator-based DFKD methods often suffer from insufficient diversity or low-confidence in synthetic images, negatively impacting student network performance. This paper introduces DFMC, a generative feature-driven framework to mitigate the inherent limitations of DFKD. We propose exploiting semantic description between generative feature domains to guide augmentation strategies, avoiding random abstract inputs caused by inconsistent semantic quality. Then, by applying noise to the generative features, we produce contrastive learning pairs indirectly, limiting the sampling range of the feature domain to encourage the student network to learn domain-invariant features. Finally, we guide the student network to deeply mimic the teacher’s layer-wise implicit classification behavior for the augmented synthetic images. Extensive experiments across various datasets and downstream tasks demonstrate the effectiveness of DFMC, achieving significant improvements while preventing student networks from overfitting to semantic ambiguous images. © 1991-2012 IEEE.

关键词： Knowledge transfer

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A Brief Survey on Privacy-Preserving Methods for Graph-Structured Data

A Brief Survey on Privacy-Preserving Methods for Graph-Struc...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Zhang, Yunan Wu, Tao Xian, Xingping Xu, Yuqing Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9789811669620

As one of the main manifestations of big data, graph-structured data widely exists in various fields such as social networks, smart cities, medical health, and finance, and is characterized by high dimension, nonlinear, scale-free, small world, etc. Extensive graph-structured data provides sufficient data resources for scientific research and commercial applications. However, graph data mining not only reveals the intrinsic value of data, but also brings the risk of privacy disclosure. Therefore, how to protect graph-structured data privacy is of great significance. In this paper, we survey the very recent research development on privacy preserving methods for graph-structured data. We introduce the common sensitive information and the related privacy risks in graph data, and elaborate the background knowledge for privacy inference attack. Then, the privacy inference attack methods and privacy preservation methods for graph-structured data are summarized. Finally, the shortcomings of the current research about graph-structured data privacy preservation and the possible research directions are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Design and development of smart terminal self-sensing module 5

Design and development of smart terminal self-sensing module

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Qigui, Yao Xiangqun, Wang Xiaojian, Zhang Zhuo, Lv Mingyan, Li Zheng, Zhang Interconnection Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Global Energy Nanjing branch Jiangsu Province China State Grid Henan Electric Power Research Institute State Grid Henan Electric Power Company Henan Province China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

In recent years, with the rapid development of the Internet of Things, the scalability and efficiency of the Internet are not enough to process the big data of the Internet of Things. The edge agent platform tilts the powerful computing and storage capabilities and user-satisfactory business service capabilities to the user side and migrates to the edge of the network to meet the needs of users to securely obtain Internet of Things services. This paper designs a security monitoring module, including host system security monitoring;Docker system security configuration monitoring;container security monitoring;linkage control configuration management and eBPF kernel-level security monitoring. The architecture, technology and design of each part are described in detail, and the interface of the module is configured. It provides a new and available solution for the security protection of edge IoT agents. © 2022 IEEE.

关键词： Internet of things

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Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition 39

Focus on Local: Finding Reliable Discriminative Regions for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Changwei Chen, Shunpeng Song, Yukun Xu, Rongtao Zhang, Zherui Zhang, Jiguang Yang, Haoran Zhang, Yu Fu, Kexue Du, Shide Xu, Zhiwei Gao, Longxiang Guo, Li Xu, Shibiao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China MAIS Institute of Automation Chinese Academy of Sciences China Tongji University China College of Computer and Data Science Fuzhou University China Shandong University China

ISBN: (纸本)157735897X

Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effects while mundane background regions do not contribute or even cause perceptual aliasing because of easy overlap. However, existing methods lack precisely modeling and full exploitation of these discriminative regions. In this paper, we propose the Focus on Local (FoL) approach to stimulate the performance of image retrieval and re-ranking in VPR simultaneously by mining and exploiting reliable discriminative local regions in images and introducing pseudo-correlation supervision. First, we design two losses, Extraction-Aggregation Spatial Alignment Loss (SAL) and Foreground-Background Contrast Enhancement Loss (CEL), to explicitly model reliable discriminative local regions and use them to guide the generation of global representations and efficient re-ranking. Second, we introduce a weakly-supervised local feature training strategy based on pseudo-correspondences obtained from aggregating global features to alleviate the lack of local correspondences ground truth for the VPR task. Third, we suggest an efficient re-ranking pipeline that is efficiently and precisely based on discriminative region guidance. Finally, experimental results show that our FoL achieves the state-of-the-art on multiple VPR benchmarks in both image retrieval and re-ranking stages and also significantly outperforms existing two-stage VPR methods in terms of computational efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Lightweight network Intrusion Detection Method Based on Improved Federated Learning

Lightweight Network Intrusion Detection Method Based on Impr...

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2022 International Conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2022

作者： He, Yang Jia, Yongliang Tao, Peng State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co.Ltd Nanjing Branch Nanjing210003 China State Grid Hebei Electric Power Company Marketing Center Hebei Shijiazhuang050000 China

ISBN: (纸本)9781510655942

Computer network has been widely used in all walks of life, network security has also received unprecedented attention, network intrusion detection technology is one of the key technologies to maintain network security. Traditional intrusion detection methods based on rules have some disadvantages, such as dependence on manual intervention, difficulty in updating rules database, and difficulty in detecting unknown intrusion. Therefore, a lightweight network intrusion detection method is designed based on improved federated learning. Firstly a network intrusion detection model is constructed based on improved federated learning to realize lightweight network intrusion detection. The experimental results show that the designed network intrusion detection method has good detection effect and has certain application value, which can be used as a reference for subsequent network intrusion detection. © 2022 SPIE.

关键词： network security

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SFTA: Spiking Neural networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIST and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIST and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

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