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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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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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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

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IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 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 National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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Robust Dynamic Broadcasting for Multi-Hop Wireless networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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SCRIPT: A Scalable Continual Reinforcement Learning Framework for Autonomous Penetration Testing

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Expert Systems with Applications 2025年 285卷

作者： Zhou, Shicheng Liu, Jingju Lu, Yuliang Yang, Jiahai Zhang, Yue Lin, Bo Zhong, Xiaofeng Hu, Shulong College of Electronic Engineering National University of Defense Technology Hefei230037 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Hefei230037 China Institute for Network Sciences and Cyberspace and the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing10084 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Compared with manual-based penetration testing (pentesting), autonomous pentesting offers advantages in autonomy and efficiency, emerging as a trending research area. Reinforcement learning (RL) is a natural fit for studying this topic. However, the proliferation of interconnected devices and systems has expanded attack surfaces, coupled with the constant emergence of new vulnerabilities, making pentesting tasks and scenarios in the real world non-stationary. This non-stationarity presents a new challenge for RL-based autonomous pentesting, as it requires pentesting agents to possess strong generalization capabilities, thereby achieving continuous learn new tasks while mitigating catastrophic forgetting. Previous research in this field overlooked this real-world challenge. To this end, we present the first scalable continual RL framework for autonomous pentesting, namely SCRIPT. SCRIPT trains the agent to learn continually like humans in a large-scale task sequence, allowing it to realize positive forward transfer (leveraging previously learned knowledge to accelerate new task learning) and resist catastrophic forgetting. Inspired by humans’ lifelong learning process, we separate SCRIPT's learning process into new task learning and knowledge consolidation processes. Specifically, we introduce a scaffolded curriculum learning approach to bootstrap new task learning through rolling exploration and policy imitation. Besides, we propose a policy retention method that utilizes regularization-based weight importance constraints and progressive self-distillation-based policy retrospection to mitigate catastrophic forgetting. Experiments are conducted using Vulhub, a widely used repository of vulnerable Docker environments. Results demonstrate that SCRIPT effectively enables agents to achieve positive forward transfer and enhance their resistance to catastrophic forgetting, making it a feasible approach for pentesting agents to handle the real-world non-stationarity challen

关键词： Reinforcement learning

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Graph Learning-Based Multi-User Multi-Task Offloading in Wireless Computing Power networks

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IEEE Internet of Things Journal 2025年

作者： Dai, Yueyue Rao, Xiaoyang Gu, Bruce Qu, Youyang Yang, Huiran Lu, Yunlong Huazhong University of Science and Technology Research Center of6G Mobile Communications School of Cyber Science and Engineering Wuhan430074 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Australia University of Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences Beijing China and the School of Cyber Security Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

To enhance service quality, wireless computing power networks (WCPNs) need to realize flexible scheduling and allocation of computation resources across heterogeneous computing servers. Due to large user scales and diverse computation tasks, it is difficult for the current WCPN to serve multiple users and handle multiple tasks concurrently. Graph learning is a promising approach that can learn the representations of nodes through graph structures, enabling the exploration of dependencies among multiple users and tasks, and thereby facilitating computation task offloading. In this paper, we propose a graph learning-based multi-user multi-task offloading scheme for WCPN. First, we propose a wireless computing power network with multi-user and multi-task in which users need to make full use of distributed computing resources through task offloading to ensure efficient task execution. We formulate a system energy consumption minimization problem to jointly optimize computation resources, transmission power, and task offloading. To address the problem, we utilize graph learning to transform the joint optimization problem into a graph regression problem and leverage line graph to explore the solution. Numerical results demonstrate that our proposed scheme can improve computation efficiency, enhance optimization performance, and maintain transferability compared with the benchmarks. © 2014 IEEE.

关键词： Computing power

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NDFuzz:a non-intrusive coverage-guided fuzzing framework for virtualized network devices

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Cybersecurity 2023年第1期6卷 54-74页

作者： Yu Zhang Nanyu Zhong Wei You Yanyan Zou Kunpeng Jian Jiahuan Xu Jian Sun Baoxu Liu Wei Huo Institute of Information Engineering Chinese Academy of SciencesBejingChina School of Cyber Security University of Chinese Academy of SciencesBejingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Renmin University of China BeijingChina

network function virtualization provides programmable in-network middlewares by leveraging virtualization tech-nologies and commodity hardware and has gained popularity among all mainstream network device *** it is challenging to apply coverage-guided fuzzing,one of the state-of-the-art vulnerability discovery approaches,to those virtualized network devices,due to inevitable integrity protection adopted by those *** this paper,we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protec-tion bypassing method,which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference *** implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols,SNMP,DHCP and NTP,on nine popular virtualized network *** obtains an average 36%coverage improvement in comparison with its black-box *** discovers 2 O-Day vulnerabilities and 11-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of *** discovered vulnerabilities are confirmed by corresponding vendors.

关键词： Coverage-guided fuzzing network devices network function virtualization

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PosFuzz:augmenting greybox fuzzing with effective position distribution

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Cybersecurity 2023年第4期6卷 123-143页

作者： Yanyan Zou Wei Zou JiaCheng Zhao Nanyu Zhong Yu Zhang Ji Shi Wei Huo Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Zhongguancun Laboratory BeijingChina

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and ***-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the ***,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing *** paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical *** makes use of a concept of effective position distribution to represent the semantics of the input and to guide the *** first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation *** then leverages two sampling methods in different mutating stages to select the positions from the *** have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M *** result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery *** with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on *** also triggers 275%more unique bugs on average.

关键词： Greybox fuzzing Mutation position Mutation operator Code coverage Vulnerability discovery

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ESRFuzzer:an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities

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Cybersecurity 2021年第1期4卷 364-385页

作者： Yu Zhang Wei Huo Kunpeng Jian Ji Shi Longquan Liu Yanyan Zou Chao Zhang Baoxu Liu Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Institute for Network Science and Cyberspace Tsinghua UniversityBeijingChina Beijing National Research Center for Information Science and Technology BeijingChina

SOHO(small office/home office)routers provide services for end devices to connect to the Internet,playing an important role in ***,security vulnerabilities pervasively exist in these routers,especially in the web server modules,greatly endangering end *** discover these vulnerabilities,fuzzing web server modules of SOHO routers is the most popular ***,its effectiveness is limited due to the lack of input specification,lack of routers’internal running states,and lack of testing environment recovery ***,existing works for device fuzzing are more likely to detect memory corruption *** this paper,we propose a solution ESRFuzzer to address these *** is a fully automated fuzzing framework for testing physical SOHO *** continuously and effectively generates test cases by leveraging two input semantic models,i.e.,key-VALUE data model and CONF-READ communication model,and automatically recovers the testing environment with power *** also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type *** the guidance of the two semantic models,ESRFuzzer can work in two ways:general mode fuzzing and D-CONF mode *** mode fuzzing can discover both issues which occur in the CONF and READ operation,while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode *** ran ESRFuzzer on 10 popular routers across five *** total,it discovered 136 unique issues,120 of which have been confirmed as 0-day vulnerabilities we *** an improvement of SRFuzzer,ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types,and 23 of them have been confirmed as 0-day vulnerabilities by *** experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.

关键词： Fuzzing IoT Automatic vulnerability detection

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Cross-Layer security for Semantic Communications: Metrics and Optimization

arXiv

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arXiv 2025年

作者： Wang, Lingyi Wu, Wei Zhou, Fuhui Qin, Zhijin Wu, Qihui College of Science Nanjing University of Posts and Telecommunications Nanjing210003 China College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing China The State Key Laboratory of Space Network and Communications Beijing China

Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to fill this gap, a unified semantic security metric, the cross-layer semantic secure rate (CLSSR), is defined to estimate cross-layer security requirements at both the physical layer and the application layer. Then, we formulate the maximization problem of the CLSSR with the mixed integer nonlinear programming (MINLP). We propose a hierarchical AI-native semantic secure communication network with a reinforcement learning (RL)based semantic resource allocation scheme, aiming to ensure the cross-layer semantic security (CL-SS). Finally, we prove the convergence of our proposed intelligent resource allocation, and the simulation results demonstrate that our proposed CLSS method outperforms the traditional physical layer semantic security (PL-SS) method in terms of both task reliability and CLSSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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