International Journal of Network Management最新文献

{"title":"Buy Crypto, Sell Privacy: An Extended Investigation of the Cryptocurrency Exchange Evonax","authors":"Alexander Brechlin,&nbsp;Jochen Schäfer,&nbsp;Frederik Armknecht","doi":"10.1002/nem.2325","DOIUrl":"https://doi.org/10.1002/nem.2325","url":null,"abstract":"<p>Cryptocurrency exchanges have become a multi-billion dollar industry. Although these platforms are not only relevant for economic reasons but also from a privacy and legal perspective, empirical studies investigating the operations of cryptocurrency exchanges and the behavior of their users are surprisingly rare. A notable exception is a study analyzing the cryptocurrency exchange <i>ShapeShift</i>. While this study described new heuristics to retrieve a significant fraction of trades made on the plaform, its approach relied on identifying cryptocurrency transactions based on previously scraped trade data. This limited the analysis to the timeframe for which data had been acquired and likely led to false negatives in the transaction identification process. In this paper, we replicate and extend previous work by conducting an in-depth investigation of the cryptocurrency exchange <i>Evonax</i>. Our analysis is based on actual trading data acquired by using a novel methodology allowing to extract detailed information from the public blockchain and the interface of the exchange platform. We are able to identify 30,402 transactions between the launch of Evonax in February 2018 and December 31, 2022, which should be close to a complete set of all transactions. This allows us not only to analyze the business practices of a cryptocurrency exchange but also to identify a number of interesting use cases that are likely to be associated with illegal activity. This paper is an extended version of a research article previously accepted at the CryptoEx Workshop at IEEE ICBC 2024.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119279","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":"Music Transmission and Performance Optimization Based on the Integration of Artificial Intelligence and 6G Network Slice","authors":"Honghui Zhu","doi":"10.1002/nem.70000","DOIUrl":"https://doi.org/10.1002/nem.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>Network slicing, which enables efficient resource management to meet specific service requirements, provides a scalable solution for optimizing music transmission and live performance in mobile networks beyond 5G and into 6G. The research focuses on optimizing live performances as well as music transmission. Since AI-driven resource management improves performance quality and real-time music streaming in dynamic 6G network situations, these factors are interconnected. This approach allows multiple tenants, such as event organizers and music producers, to share infrastructure while customizing communication and quality standards for real-time music services. To ensure optimal resource allocation, including high bandwidth, low latency, and consistent service quality, network slices are dynamically configured by the infrastructure provider. Although the implementation of network slicing in the core network has been well studied, applying it within the radio access network (RAN) presents challenges, especially given the unpredictability of wireless channels and the strict quality of service (QoS) demands for live music streaming. For 6G networks, the article suggests a tenant-driven RAN slicing method improved by artificial intelligence (AI) to maximize music performance and transmission. A hybrid AI framework integrates a deep recurrent neural network (DRNN) for continuous monitoring and prediction of network conditions with a deep Q-network (DQN) augmented by prioritized experience replay (PER) for real-time resource adaptation. The DRNN forecasts network states to guide high-level resource allocation, whereas DQN with PER dynamically manages routing and bandwidth based on past critical network states, enabling rapid responses to fluctuating performance demands. Comparative results indicate that the suggested approach outperforms conventional techniques, achieving a latency of 25 ms, an audio quality of 4.6, and a bandwidth utilization of 90%. Simulation results in live music and enhanced mobile broadband (eMBB) environments demonstrate the proposed approach's effectiveness in minimizing latency, enhancing audio quality, and stabilizing transmission, surpassing traditional network allocation techniques.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117930","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 New English Education Model Based on 6G and Sliced Network Virtual Reality Platform","authors":"Xiaozheng Liu","doi":"10.1002/nem.2324","DOIUrl":"https://doi.org/10.1002/nem.2324","url":null,"abstract":"<div>\u0000 \u0000 <p>The information society has led to a shift in traditional English education methods, with the evolution of technology, particularly internet and communication network technologies, reshaping the teaching landscape. This facilitated innovative instructional approaches and enhanced the learning experience. This research introduces a novel virtual learn net architecture (VLNA) within the 6G network layers, which processes the performance of the virtual reality-based English education system (VR-EES) model to provide a seamless, personalized learning experience for online learners. This architecture is structured into several layers: The user equipment (UE) layer connects VR headsets to the network with ultrareliable, low-latency links; the radio access network (RAN) layer, employing massive MIMO and beam forming, enhances connection speed, capacity, and coverage. Edge computing handles latency-sensitive tasks like speech recognition and adaptive content delivery, reducing the load on the core network. The core network layer (CLN) manages network slices for specific learning tasks such as real-time interaction, high-definition multimedia, and computation-intensive processes, with control plane and user plane separation (CUPS) optimizing network management and security through end-to-end encryption. Software-defined networking (SDN) and network function virtualization (NFV) provide centralized, dynamic control, allowing real-time resource allocation based on demand. Cloud-edge integration supports Artificial intelligence (AI)-driven adaptive learning, optimizing educational content delivery based on individual progress. The study results demonstrate that stimulation of VLNA achieved significant improvements in latency reduction, bandwidth utilization, throughput, packet loss rate, jitter, user engagement, learning efficiency, and user satisfaction. The integration of edge computing and network slicing led to a significant reduction in latency, while the enhanced throughput enabled seamless VR experiences. In this study, latency reduction, bandwidth utilization, and user satisfaction emerge as the most significant factors, with user satisfaction standing out as the top performer due to its substantial impact on enhancing the overall learning experience. The packet loss rate is maintained to a certain level, ensuring reliable data transmission. The VR-EES model's experimental results also enhanced visual learning, multimedia quality, user pleasure, learning effectiveness, and user engagement.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114896","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":"Log-TF-IDF and NETCONF-Based Network Switch Anomaly Detection","authors":"Sukhyun Nam,&nbsp;Eui-Dong Jeong,&nbsp;James Won-Ki Hong","doi":"10.1002/nem.2322","DOIUrl":"https://doi.org/10.1002/nem.2322","url":null,"abstract":"<div>\u0000 \u0000 <p>In this study, we propose and evaluate a model that utilizes both log data and state data to detect abnormal conditions in network switches. Building upon our previous research and drawing inspiration from TF-IDF used in natural language processing to measure word importance, we propose a statistical method, Log-TF-IDF, to quantify the rarity of each log pattern in the log data. Furthermore, based on this Log-TF-IDF, we introduce the AB Score, which quantifies how abnormal the current log pattern is. Our findings indicate that the AB Score is notably higher and more volatile in abnormal conditions. We confirm that anomaly detection is feasible through the AB Score, which has the advantage of being computationally efficient due to its statistical basis. We combined the metrics generated during the AB Score calculation with resource data collected with NETCONF and developed a machine-learning model to detect abnormal conditions in network switches. We confirm that this model can detect abnormal conditions with an F1 score of 0.86 on our collected dataset, confirming its viability for detecting abnormal states in network equipment.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114895","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":"Multitopology Routing With Virtual Topologies and Segment Routing","authors":"Nicolas Huin,&nbsp;Sébastien Martin,&nbsp;Jérémie Leguay","doi":"10.1002/nem.2321","DOIUrl":"https://doi.org/10.1002/nem.2321","url":null,"abstract":"<div>\u0000 \u0000 <p>Multitopology routing (MTR) provides an attractive alternative to segment routing (SR) for traffic engineering when network devices cannot be upgraded. However, due to a high overhead in terms of link state messages exchanged by topologies and the need to frequently update link weights to follow evolving network conditions, MTR is often limited to a small number of topologies and the satisfaction of loose QoS constraints. To overcome these limitations, we propose virtual MTR (vMTR), an MTR extension where demands are routed over virtual topologies that are silent; that is, they do not exchange LSA messages and that are continuously derived from a very limited set of real topologies, optimizing each QoS parameter. In this context, we present a polynomial and exact algorithm for vMTR and, as a benchmark, a local search algorithm for MTR. We show that vMTR helps to reduce drastically the number of real topologies and that it is more robust to QoS changes. In the case where SR can actually be rolled-out, we also show that vMTR allows to drastically reduce SR overhead.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114966","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":"Muno: Improved Bandwidth Estimation Scheme in Video Conferencing Using Deep Reinforcement Learning","authors":"Van Tu Nguyen,&nbsp;Sang-Woo Ryu,&nbsp;Kyung-Chan Ko,&nbsp;Jae-Hyoung Yoo,&nbsp;James Won-Ki Hong","doi":"10.1002/nem.2323","DOIUrl":"https://doi.org/10.1002/nem.2323","url":null,"abstract":"<div>\u0000 \u0000 <p>Many studies have used machine learning techniques for bitrate control to improve the quality of experience (QoE) of video streaming applications. However, most of these studies have focused on HTTP adaptive streaming with one-to-one connections. This research examines video conferencing applications that involve real-time, multiparty, and full-duplex communication among participants. In conventional video conferencing systems, a rule-based algorithm is typically employed to estimate the available bandwidth of each participant, and the outcomes are then used to control the video delivery rate to the participant. This paper proposes Muno, a bandwidth prediction framework based on deep reinforcement learning (DRL) for multiparty video conferencing systems. Muno aims to enhance the overall QoE by using DRL to improve bandwidth estimation for each connection. The experimental results indicate that Muno achieves a significantly higher video streaming rate, video resolution, and framerate while lowering delay in highly dynamic networks when compared to the state-of-the-art rule-based algorithms and roughly equivalent streaming rate and delay in stable networks. Moreover, Muno can generalize well to different network conditions which were not included in the training set. We also implemented a high-performance and scalable version of Muno for in-campus deployment.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113096","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":"Real-Time Encrypted Traffic Classification in Programmable Networks with P4 and Machine Learning","authors":"Aristide Tanyi-Jong Akem,&nbsp;Guillaume Fraysse,&nbsp;Marco Fiore","doi":"10.1002/nem.2320","DOIUrl":"https://doi.org/10.1002/nem.2320","url":null,"abstract":"<p>Network traffic encryption has been on the rise in recent years, making encrypted traffic classification (ETC) an important area of research. Machine learning (ML) methods for ETC are widely regarded as the state of the art. However, most existing solutions either rely on offline ETC based on collected network data or on online ETC with models running in the control plane of software-defined networks, all of which do not run at line rate and would not meet the strict requirements of ultra-low-latency applications in modern networks. This work exploits recent advances in data plane programmability to achieve real-time ETC in programmable switches at line rate, with high throughput and low latency. An extensive analysis is first conducted to show how tree-based models excel in ETC on various datasets. Then, a workflow is proposed for in-switch ETC with tree-based models. The proposed workflow builds on (i) an ETC-aware random forest (RF) modelling process where only features based on packet size and packet arrival times are used and (ii) an encoding of the trained RF model into off-the-shelf P4-programmable switches. The performance of the proposed in-switch ETC solution is evaluated on three use cases based on publicly available encrypted traffic datasets. Experiments are then conducted in a real-world testbed with Intel Tofino switches, in the presence of high-speed background traffic. Results show how the solution achieves high classification accuracy of up to 95<i>%</i> in QUIC traffic classification, with submicrosecond delay while consuming less than 10<i>%</i> on average of the total hardware resources available on the switch.</p>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/nem.2320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113095","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":"A Method for Sharing English Education Resources in Multiple Virtual Networks Based on 6G","authors":"Hongliu He","doi":"10.1002/nem.2319","DOIUrl":"https://doi.org/10.1002/nem.2319","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid advancement of communication technologies, particularly in English language learning, is sharing education with the implementation of sixth-generation (6G) networks, offering immersive and interactive learning experiences. The purpose of the research is to establish an advanced method for sharing English education resources across multiple virtual networks enabled by 6G technology. Traditional resource-sharing systems lack the effectiveness and optimization requirement for large-scale instructional assignments, especially in virtual settings with various user demands. To address this, the study proposed a novel Dynamic Tunicate Swarm Refined Graph Neural Networks (DTS-RGNN) model to optimize resource allocation and improve the efficiency of resource sharing among educational tasks. The approach uses TSO for resource allocation scalable through 6G technology and GNN for task assignment according to the previous performances and interaction with the students to balance resource utilization. The experimental group performed writing (90%), sharing (91%), listening (85%), and reading (75%), finishing the task in 5.5 s at 1000 GB. Throughput increased by 5.0 GBps and resource utilization efficiency improved to (96%) and student outcomes showed high satisfaction (93%), retention (89%), and engagement (90%). The findings demonstrated the proposed method significantly improves the sharing of online English education resources, promoting more interactive and effective language learning experiences in virtual networks.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118095","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":"An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent Autoencoder in Cloud Computing","authors":"Saurav Tripathi,&nbsp;Sarsij Tripathi","doi":"10.1002/nem.2318","DOIUrl":"https://doi.org/10.1002/nem.2318","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM-GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE-BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM-GJO method for multiworkflow scheduling. GM-GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861696","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":"Security Protection Method for Electronic Archives Based on Homomorphic Aggregation Signature Scheme in Mobile Network","authors":"Junwei Li,&nbsp;Huaquan Su,&nbsp;Li Guo,&nbsp;Wanshuo Wang,&nbsp;Yongjiao Yang,&nbsp;You Wen,&nbsp;Kai Li,&nbsp;Pingyan Mo","doi":"10.1002/nem.2316","DOIUrl":"https://doi.org/10.1002/nem.2316","url":null,"abstract":"<div>\u0000 \u0000 <p>Electronic archives are now widely used in many different industries and serve as the primary method of information management and storage because of the rapid growth of information technology and mobile networks. To enhance the security of electronic archives in mobile networks, the research utilizes the federated learning mechanism to design a federated learning model based on homomorphic aggregation cryptographic signature scheme combined with mobile network management. The use of homomorphic encryption technology in the signing process of electronic archives enables the aggregation of multiple electronic file signatures into a single signature without exposing the data of the electronic archives. This reduces the computational and storage requirements for signature verification. At the same time, a secure aggregation signature scheme is used to ensure the integrity and security of the data in the aggregation process. A novel approach is presented in this study, whereby trusted federated learning models are innovatively combined with homomorphic aggregate signature technology. This integration ensures data integrity through aggregate signature schemes. The results showed that, under mobile network management, the longest encryption time of the trusted federated learning model was 52 ms, and the longest decryption time was 44 ms. The accuracy of the optimized learning model reached 97.49%, and the loss value was significantly reduced to 0.09. To summarize, the electronic archive security protection method based on homomorphic aggregation signature scheme effectively improves the archive data protection efficiency and security.</p>\u0000 </div>","PeriodicalId":14154,"journal":{"name":"International Journal of Network Management","volume":"35 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861454","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}