IEEE Transactions on Network and Service Management最新文献_第8页

Optimizing Traffic Management in Airborne Power Line Communication Networks: A Credit-Based Shaping Approach Using Network Calculus 机载电力线通信网络流量管理优化：基于信用的网络微积分整形方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-16 DOI: 10.1109/TNSM.2025.3529871

Ruowen Yan;Qiao Li;Huagang Xiong

{"title":"Optimizing Traffic Management in Airborne Power Line Communication Networks: A Credit-Based Shaping Approach Using Network Calculus","authors":"Ruowen Yan;Qiao Li;Huagang Xiong","doi":"10.1109/TNSM.2025.3529871","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3529871","url":null,"abstract":"As the aviation industry progresses towards More Electric Aircraft (MEA), the demand for robust and efficient data communication systems intensifies. Traditional fieldbus systems are burdened by high installation costs and substantial weight due to extensive cabling requirements. The Power Line Communication (PLC) technology presents a promising alternative; however, its adaptation to the stringent real-time demands of airborne environments poses significant challenges. To address this, this paper introduces a novel Credit-Based Shaper with Channel Contention (CBSCC) mechanism designed to optimize traffic management in airborne PLC networks. This mechanism operates at the Medium Access Control (MAC) layer of the HomePlug AV 2 protocol, employing a dynamic configuration approach informed by Network Calculus (NC). This approach utilizes End-to-End Delay (E2ED) requirements of data flows and network configuration details to calculate the parameters for the CBSCC traffic shaper. Comprehensive simulations conducted with OMNeT++ demonstrate the efficacy of CBSCC, demonstrating marked improvements in E2ED satisfaction for all data frames, reduced average access delays, and enhanced fairness across different priority levels compared to the HomePlug AV2 protocol and previous traffic management strategies. The findings confirm that the CBSCC mechanism substantially alleviates the starvation of lower-priority traffic, boosts network efficiency, and ensures robust real-time guarantees essential for the safety and reliability of airborne communication systems. This research represents a substantial advancement over existing solutions, aligning with the evolving needs of MEA implementations.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1437-1449"},"PeriodicalIF":4.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MDTL: Maliciously Secure Distributed Transfer Learning Based on Replicated Secret Sharing 基于复制秘密共享的恶意安全分布式迁移学习

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-14 DOI: 10.1109/TNSM.2025.3529471

Zhengran Tian;Hao Wang;Zhi Li;Ziyu Niu;Xiaochao Wei;Ye Su

{"title":"MDTL: Maliciously Secure Distributed Transfer Learning Based on Replicated Secret Sharing","authors":"Zhengran Tian;Hao Wang;Zhi Li;Ziyu Niu;Xiaochao Wei;Ye Su","doi":"10.1109/TNSM.2025.3529471","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3529471","url":null,"abstract":"As data continues to grow at an unprecedented rate and informationization accelerates, concerns over data privacy have become more prominent. In image classification tasks, the challenge of insufficient labeled data is common. Transfer learning, an effective and important machine learning method, can address this issue by leveraging knowledge from the source domain to enhance performance in the target domain. However, existing privacy-preserving transfer learning schemes continue to face challenges related to low security and multiple rounds of communication. In the following works, we design a three-party privacy-preserving transfer learning protocol based on the Joint Distributed Adaptation (JDA) algorithm, which ensures malicious security under an honest majority model. To realize this protocol, we designed a series of sub-protocols for constant-round communication, including distributed solving of eigenvalues and eigenvectors based on replicated secret sharing techniques. Compared to existing work, our protocol requires fewer rounds and satisfies malicious security. We provide formal security proofs for the designed protocol and assess its performance using real datasets. Our protocol for computing the eigenvalues of matrices in a given dimension is approximately 2.5 times faster than existing methods. The results of the experiments demonstrate both the security and effectiveness of the proposed approach.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"877-891"},"PeriodicalIF":4.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Preference and Social Attribute-Based Data Sharing for Information-Centric IoT 信息中心物联网中基于个性化偏好和社会属性的数据共享

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-14 DOI: 10.1109/TNSM.2025.3529291

Xiaonan Wang;Yajing Song

引用次数: 0

Auditable Homomorphic-Based Decentralized Collaborative AI With Attribute-Based Differential Privacy 基于属性差分隐私的可审计同态分布式协作AI

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-14 DOI: 10.1109/TNSM.2025.3529774

Lo-Yao Yeh;Sheng-Po Tseng;Chia-Hsun Lu;Chih-Ya Shen

{"title":"Auditable Homomorphic-Based Decentralized Collaborative AI With Attribute-Based Differential Privacy","authors":"Lo-Yao Yeh;Sheng-Po Tseng;Chia-Hsun Lu;Chih-Ya Shen","doi":"10.1109/TNSM.2025.3529774","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3529774","url":null,"abstract":"In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it may also significantly deteriorate the model performance. To address these issues, we propose a novel decentralized collaborative AI framework, named Auditable Homomorphic-based Decentralised Collaborative AI (AerisAI), to improve security with homomorphic encryption and fine-grained differential privacy. Our proposed AerisAI directly aggregates the encrypted parameters with a blockchain-based smart contract to get rid of the need of a trusted third party. We also propose a brand-new concept for eliminating the negative impacts of differential privacy for model performance. Moreover, the proposed AerisAI also provides the broadcast-aware group key management based on ciphertext-policy attribute-based encryption (CP-ABE) to achieve fine-grained access control based on different service-level agreements. We provide a formal theoretical analysis of the proposed AerisAI as well as the functionality comparison with the other baselines. We also conduct extensive experiments on real datasets to evaluate the proposed approach. The experimental results indicate that our proposed AerisAI significantly outperforms the other state-of-the-art baselines.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"989-1004"},"PeriodicalIF":4.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

zkFabLedger: Enabling Privacy Preserving and Regulatory Compliance in Hyperledger Fabric zkFabLedger：在 Hyperledger Fabric 中实现隐私保护和合规性

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-13 DOI: 10.1109/TNSM.2024.3525045

Xingyu Yang;Jipeng Hou;Lei Xu;Liehuang Zhu

{"title":"zkFabLedger: Enabling Privacy Preserving and Regulatory Compliance in Hyperledger Fabric","authors":"Xingyu Yang;Jipeng Hou;Lei Xu;Liehuang Zhu","doi":"10.1109/TNSM.2024.3525045","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3525045","url":null,"abstract":"Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the sender’s identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"2243-2263"},"PeriodicalIF":4.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance 面向多目标监视的弹性多跳自主无人机网络

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-13 DOI: 10.1109/TNSM.2025.3528495

Abdulsamet Dağaşan;Ezhan Karaşan

{"title":"Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance","authors":"Abdulsamet Dağaşan;Ezhan Karaşan","doi":"10.1109/TNSM.2025.3528495","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3528495","url":null,"abstract":"Cooperative utilization of Unmanned Aerial Vehicles (UAVs) in public and military surveillance applications has attracted significant attention in recent years. Most UAVs are equipped with sensors and wireless communication equipment with limited ranges. Such limitations pose challenging problems to monitor mobile targets. This paper examines fulfilling surveillance objectives to achieve better coverage while building a resilient network between UAVs with an extended lifetime. The multiple target tracking problem is studied by including a relay UAV within the fleet whose trajectory is autonomously calculated in order to achieve a reliable connected network among all UAVs. Optimization problems are formulated for single-hop and multi-hop communications among UAVs. Three heuristic algorithms are proposed for multi-hop communications and their performances are evaluated. A hybrid algorithm, which dynamically switches between single-hop and multi-hop communications is also proposed. The effect of the time horizon considered in the optimization problem is also studied. Performance evaluation results show that the trajectories generated for the relay UAV by the hybrid algorithm can achieve network lifetimes that are within 95% of the maximum possible network lifetime which can be obtained if the entire trajectories of all targets were known a priori.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1500-1513"},"PeriodicalIF":4.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

6G mmWave Security Advancements Through Federated Learning and Differential Privacy 通过联合学习和差异隐私实现 6G 毫米波安全进步

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-10 DOI: 10.1109/TNSM.2025.3528235

Ammar Kamal Abasi;Moayad Aloqaily;Mohsen Guizani

{"title":"6G mmWave Security Advancements Through Federated Learning and Differential Privacy","authors":"Ammar Kamal Abasi;Moayad Aloqaily;Mohsen Guizani","doi":"10.1109/TNSM.2025.3528235","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3528235","url":null,"abstract":"This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1911-1928"},"PeriodicalIF":4.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis With Diffusion-Based Approach 扩散模型与网络管理的结合：用基于扩散的方法改进交通矩阵分析

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-09 DOI: 10.1109/TNSM.2025.3527442

Xinyu Yuan;Yan Qiao;Zhenchun Wei;Zeyu Zhang;Minyue Li;Pei Zhao;Rongyao Hu;Wenjing Li

{"title":"Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis With Diffusion-Based Approach","authors":"Xinyu Yuan;Yan Qiao;Zhenchun Wei;Zeyu Zhang;Minyue Li;Pei Zhao;Rongyao Hu;Wenjing Li","doi":"10.1109/TNSM.2025.3527442","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3527442","url":null,"abstract":"Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1259-1275"},"PeriodicalIF":4.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Priority-Based Blockchain Packing for Dependent Industrial IoT Transactions 基于优先级的区块链打包，用于依赖性工业物联网交易

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-09 DOI: 10.1109/TNSM.2025.3527810

Chaofeng Lin;Jinchuan Tang;Shuping Dang;Gaojie Chen

{"title":"Priority-Based Blockchain Packing for Dependent Industrial IoT Transactions","authors":"Chaofeng Lin;Jinchuan Tang;Shuping Dang;Gaojie Chen","doi":"10.1109/TNSM.2025.3527810","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3527810","url":null,"abstract":"Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1618-1628"},"PeriodicalIF":4.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated Learning Under Attack: Exposing Vulnerabilities Through Data Poisoning Attacks in Computer Networks 攻击下的联邦学习：通过计算机网络中的数据中毒攻击暴露漏洞

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2025-01-03 DOI: 10.1109/TNSM.2025.3525554

Ehsan Nowroozi;Imran Haider;Rahim Taheri;Mauro Conti

{"title":"Federated Learning Under Attack: Exposing Vulnerabilities Through Data Poisoning Attacks in Computer Networks","authors":"Ehsan Nowroozi;Imran Haider;Rahim Taheri;Mauro Conti","doi":"10.1109/TNSM.2025.3525554","DOIUrl":"https://doi.org/10.1109/TNSM.2025.3525554","url":null,"abstract":"Federated Learning is an approach that enables multiple devices to collectively train a shared model without sharing raw data, thereby preserving data privacy. However, federated learning systems are vulnerable to data-poisoning attacks during the training and updating stages. Three data-poisoning attacks—label flipping, feature poisoning, and VagueGAN—are tested on FL models across one out of ten clients using the CIC and UNSW datasets. For label flipping, we randomly modify labels of benign data; for feature poisoning, we alter highly influential features identified by the Random Forest technique; and for VagueGAN, we generate adversarial examples using Generative Adversarial Networks. Adversarial samples constitute a small portion of each dataset. In this study, we vary the percentages by which adversaries can modify datasets to observe their impact on the Client and Server sides. Experimental findings indicate that label flipping and VagueGAN attacks do not significantly affect server accuracy, as they are easily detectable by the Server. In contrast, feature poisoning attacks subtly undermine model performance while maintaining high accuracy and attack success rates, highlighting their subtlety and effectiveness. Therefore, feature poisoning attacks manipulate the server without causing a significant decrease in model accuracy, underscoring the vulnerability of federated learning systems to such sophisticated attacks. To mitigate these vulnerabilities, we explore a recent defensive approach known as Random Deep Feature Selection, which randomizes server features with varying sizes (e.g., 50 and 400) during training. This strategy has proven highly effective in minimizing the impact of such attacks, particularly on feature poisoning.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 1","pages":"822-831"},"PeriodicalIF":4.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0