International Journal of Network Management最新文献

{"title":"Management of IT Infrastructure Library Processes Based on Cloud Computing: A Comprehensive Study, Current Applications, and Future Research Directions","authors":"Xi Zhao","doi":"10.1002/nem.70056","DOIUrl":"https://doi.org/10.1002/nem.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper addresses the critical intersection of IT Infrastructure Library (ITIL) processes and cloud computing adoption, a relationship requiring structured examination despite ITIL's established role in IT service management. Organizations migrating to the cloud face complexities in risk reduction and seamless transfer, making the synergy between ITIL and cloud strategies essential. Although ITIL is recognized for enhancing business operations, measuring performance, and reducing costs, its specific influence on cloud-centric ITIL service strategy execution needs further elucidation. Employing a systematic literature review (SLR) methodology, this study analyzes recent research to examine how cloud computing influences ITIL service strategy procedures and their sub-processes. The findings aim to provide a clear understanding of this evolving landscape for IT professionals and researchers. The systematic review reveals that integrating ITIL with cloud computing not only drives substantial cost savings and enhances user experience but also necessitates adapting ITIL processes to the dynamic nature of cloud environments, particularly in areas of security and configuration management.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149222803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taylor Series-based Activity and Trust Aware Federated Learning Aggregation for Malware Detection in IoT","authors":"R. Bhavani,&nbsp;Veeramalai Sankaradass,&nbsp;Indhumathi V,&nbsp;Sunitha T,&nbsp;Saranya K","doi":"10.1002/nem.70053","DOIUrl":"https://doi.org/10.1002/nem.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>The Internet of Things (IoT) has been developed progressively because of its effective data transmission. Still, the IoT devices are affected by malware attacks. The malware detection in IoT is complex owing to the varied capacities of devices. The Federated Learning (FL) is utilized for enhancing privacy against malware activities. Hence, this paper develops the Taylor Series-enabled Activity and Trust Aware Federated Learning Aggregation (TaTFedLA) for malware detection. The client and the server are the components in FL, in which steps like data acquisition, preprocessing, feature fusion, and malware detection are performed in the training. The Min-Max normalization is utilized for preprocessing. Deep Kronecker Network (DKN) with a non-correlation similarity is utilized in feature fusion. The Fractional Lotus Effect Optimization-based Deep Belief Neural Network Fused SpinalNet (FLEO_DBNFSpinalNet) using a FL approach is employed for malicious detection. After evaluating the trained model, the server controls model aggregation. Hence, trust is established between the server and the IoT device, and aggregation is done using the Taylor series concept. Moreover, the TaTFedLA-based malware detection achieves the accuracy, Mean Average Precision, False Positive Rate (FPR), loss, Mean Square Error (MSE), Root MSE (RMSE), bandwidth, energy, precision, recall and F-measure, of 94.99%, 94.25%, 0.121, 5.008, 0.107, 0.326, 36.156 Mbps, 484.318 J, 96.14%, 87.90%, and 91.84%.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Vehicular Fog Computing With Novel Joint Task Scheduling Algorithm Based on Hybrid Genetic Metaheuristics","authors":"Alaguvathana Paravel,&nbsp;Dhurgadevi Muthusamy","doi":"10.1002/nem.70055","DOIUrl":"https://doi.org/10.1002/nem.70055","url":null,"abstract":"<div>\u0000 \u0000 <p>To address the requirement of latency-sensitive intelligent transportation applications, vehicular fog computing has proven to be a promising paradigm to push the computation towards the vehicles. However, efficient task offloading and scheduling is a challenging optimization problem because of the highly dynamic nature of vehicular environments, heterogeneous resource availability, task dependency, and varying network conditions. To address these issues, this paper proposes a joint task optimization framework that simultaneously optimizes task-to-node assignment and execution scheduling within a unified optimization model. The task scheduling problem is formulated as a two-dimensional bin-packing variant capable of capturing both temporal execution constraints and computational resource limitations. The proposed framework integrates a heuristic-guided genetic algorithm for global exploration with simulated annealing for local refinement, enabling an effective balance between exploration and exploitation. Furthermore, a cost-aware heuristic mutation strategy is introduced to improve workload balancing while reducing communication and computation costs. The framework also incorporates dynamic task adaptation to support real-time task arrivals and changing vehicular network conditions. Extensive simulations are conducted under varying vehicular densities, workload intensities, queue stability conditions, and dynamic task arrival rates. Experimental results demonstrate that the proposed framework consistently outperforms existing approaches in terms of response time, energy consumption, task completion time, offloading cost, and load balancing efficiency. In particular, the proposed achieves an average response time of 0.42 s, an energy consumption of 1200 J, an offloading cost of 100 vehicles, and a task completion time of 620 s for 200 tasks. Scalability analysis also validates the stable performance up to 300 vehicles and 500 computational tasks, while the confidence interval analysis shows the robustness and reliability of the optimization process.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interventional Root-Cause Analysis of Quality-of-Service Degradation in LEO Satellite-User Assignment","authors":"Edson José Pacheco,&nbsp;Carlos Marcelo Pedroso","doi":"10.1002/nem.70054","DOIUrl":"https://doi.org/10.1002/nem.70054","url":null,"abstract":"<p>Operators of low Earth orbit (LEO) access networks increasingly diagnose quality-of-service (QoS) degradation with observational machine-learning attribution, yet when the candidate causes are produced by the resource-management policy itself, observational attribution is unsafe: it assigns the degradation to factors that an operator cannot act on or that only proxy the true cause. We treat the satellite-user assignment policy as the object of root-cause analysis and use the assignment simulator as an intervention oracle, decomposing per-user degradation into policy-induced congestion and channel impairment through structural interventions, and contrasting interventional attribution with observational Shapley attribution and constraint-based causal discovery. Across seven scenarios spanning three regions and assignment instances of up to half a million binary variables, policy-induced congestion is the dominant degradation and the load-balancing control recovers per-user throughput by factors of 1.8 to 10 at fixed coverage, with paired tests that remain statistically significant under multiple-comparison correction. Shapley attribution assigns the degradation to geography under its observational and interventional forms, and under the causal form in the main scenario. Constraint-based discovery orients the load relation against its structural direction, and the rank correlation between link signal-to-noise ratio and delivered throughput inverts as offered load grows. The interventional analysis identifies the policy as the actionable cause and separates degradation that the policy can repair from degradation bounded by fleet capacity or by the channel, a distinction that observational root-cause analysis does not provide.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148878348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-Enhanced Secure Data-Driven QoS Framework for Wireless Sensor Networks","authors":"Muhammad Usman,&nbsp;Juhua Pu,&nbsp;Attiq Ur Rehman,&nbsp;Saima Zainab,&nbsp;Muhammad Khalil Afzal","doi":"10.1002/nem.70051","DOIUrl":"https://doi.org/10.1002/nem.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents the Blockchain-Enhanced Secure Data-Driven Quality of Service (QoS) Framework (BES-DQF) for security and efficiency of wireless sensor networks (WSNs) and Internet of Things (IoT) systems. The framework combines data integrity verification using blockchain, smart contract enforcement, security compliance, and deep learning algorithms for QoS optimization. The novel combination of these technologies ensures that only verified data enters the deep learning model, avoiding adversarial attacks, such as data poisoning and Sybil attacks. Experimental results show that the accuracy of BESDQF in QoS prediction is 94%, and the attack detection rate is 92%, which is better than that of traditional QoS systems, which are only 93% accurate and 61% attack detection. In addition, BES-DQF maintains an acceptable latency overhead (+6.8%) and energy consumption (+5.4%), proving it to be practical for large-scale IoT applications. Our contributions include (1) the development of a secure blockchain-integrated QoS optimization model, (2) the introduction of a novel attack-resistant learning pipeline, and (3) a comprehensive experimental validation to show the superior performance and robustness of BES-DQF in adversarial conditions. Future work will be done on consensus optimization for scalability and federated learning for enhanced privacy and decentralization.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148463131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coin Mixing Protocols: A Structured Evaluation and Review of Detection Methods","authors":"Jeongheon Kim,&nbsp;James Won-Ki Hong","doi":"10.1002/nem.70052","DOIUrl":"https://doi.org/10.1002/nem.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>Cryptocurrencies, beginning with Bitcoin, have been widely adopted as they enable transactions while preserving user privacy. However, prior studies have revealed that the anonymity provided by cryptocurrencies is incomplete, with Bitcoin in particular relying on pseudonymity rather than true anonymity. To address this limitation, a variety of coin mixing protocols have been proposed to enhance anonymity. Yet, enhanced anonymity also carries the risk of misuse by malicious actors, and coin mixing protocols are no exception. Several mixing services built on these protocols have been employed in laundering illicit funds from major hacks, drawing the attention of regulators. This paper analyzes the strengths and limitations of existing coin mixing protocols and evaluates them from the perspectives of privacy, security, efficiency, and practicality. Furthermore, it reviews research on the detection of mixing protocols and discusses future directions for advancing this line of work.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Two-Timescale O-RAN-Native Framework for Priority-Aware Resource Orchestration and Task Offloading in Edge Computing Networks","authors":"Amin Mohajer,&nbsp;Abbas Mirzaei,&nbsp;Mostafa Darabi,&nbsp;Xavier Fernando","doi":"10.1002/nem.70045","DOIUrl":"https://doi.org/10.1002/nem.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>Edge computing networks must support heterogeneous services with sharply different latency, reliability, and bandwidth requirements under time-varying traffic, limited radio capacity, and distributed compute resources. These challenges become more critical when orchestration decisions must be coordinated across multiple edge regions with strict response-time constraints. This paper proposes a two-timescale O-RAN-native framework for priority-aware resource orchestration and task offloading in edge computing networks. The framework follows the O-RAN control hierarchy by assigning long-timescale workload analysis, policy adaptation, and service-aware resource planning to the Non-RT RIC, whereas a Near-RT RIC xApp performs fast runtime decisions for task offloading, bandwidth assignment, and compute allocation according to instantaneous network conditions. To support cooperative control among distributed edge nodes, the proposed design combines graph-based internode context modeling with a priority-aware decision mechanism that captures queue state, service urgency, link quality, and available processing resources. This architecture enables rapid local adaptation without losing global policy consistency. Simulation results under dynamic multiservice workloads show that the proposed framework reduces service delay, improves completion reliability for urgent traffic, and increases overall resource efficiency compared with representative baseline methods.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for the IJNM Special Issue From the Best Papers of IEEE CryptoEx 2024 “Enhancing and Securing Decentralized Financial Systems”","authors":"Andreas Veneris,&nbsp;Hongtaek Ju,&nbsp;Taeyeol Jeong,&nbsp;James W. Hong","doi":"10.1002/nem.70050","DOIUrl":"https://doi.org/10.1002/nem.70050","url":null,"abstract":"&lt;p&gt;This special issue contains extended versions of the best papers from the 2024 IEEE CryptoEx Workshop on Blockchain and Cryptocurrency. The workshop was held from May 27 to May 31, 2024, at Trinity College Dublin, Ireland. The papers collected in this special issue address critical challenges and emerging opportunities at the intersection of blockchain technology, decentralized finance, and artificial intelligence, spanning topics that include cryptocurrency exchange forensics, decentralized derivatives markets, and AI-driven trading strategies. The following papers highlight significant advances and address complex open problems in these domains.&lt;/p&gt;&lt;p&gt;In the first contribution, entitled “Buy Crypto, Sell Privacy: An Extended Investigation of the Cryptocurrency Exchange Evonax,” Brechlin et al. conduct an in-depth empirical investigation of the cryptocurrency exchange Evonax by reverse-engineering its internal payment processing mechanisms. Employing a novel, fully automated methodology that combines public blockchain analysis with the exchange's web interface, the authors compile a near-complete dataset of 30,402 trades executed between February 2018 and December 2022. The study reveals critical privacy vulnerabilities inherent in exchange platform design and uncovers evidence of illicit activity including connections to the Upbit hack, rug pull schemes, and potential money laundering through coin swap operations. The findings underscore the broader implications for privacy and regulatory oversight in the cryptocurrency exchange ecosystem.&lt;/p&gt;&lt;p&gt;In the second paper, entitled “Option Contracts in the DeFi Ecosystem: Opportunities, Solutions, and Technical Challenges,” Singh et al. present the first comprehensive examination of the options trading landscape within decentralized finance. The paper systematically analyzes four major centralized exchanges and eight leading decentralized option protocols on Ethereum, comparing their design choices across oracle selection, pricing mechanisms, and collateralization strategies. The authors further demonstrate, through analysis of historical on-chain data for 14 ERC20 token pairs, that the absence of options markets results in measurable liquidity deficiencies on automated market makers, identifying 1143 instances where liquidity levels fell below a theoretically derived threshold and could have been increased by a factor of up to six. The paper provides a valuable reference for practitioners and researchers working toward the next generation of decentralized derivatives infrastructure.&lt;/p&gt;&lt;p&gt;In the third contribution, entitled “A2C Reinforcement Learning for Cryptocurrency Trading: Analyzing the Impact of Price Prediction Accuracy and Data Granularity,” Kang et al. investigate the application of the Advantage Actor-Critic reinforcement learning model to cryptocurrency portfolio management. The study systematically examines how varying levels of noise in price predictions and different data granularit","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"36 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}