Computer Networks最新文献

{"title":"P-HotStuff: Parallel BFT algorithm with throughput insensitive to propagation delay","authors":"Fei Zhu,&nbsp;Lin You,&nbsp;Jixiang Wang,&nbsp;Lei Li","doi":"10.1016/j.comnet.2025.111183","DOIUrl":"10.1016/j.comnet.2025.111183","url":null,"abstract":"<div><div>In this work, we present P-HotStuff, a novel variant of HotStuff consensus algorithm with multiple parallel operations, which can effectively solve the bottleneck of the Byzantine Fault Tolerance (BFT) algorithms that employ the leader-based consensus model, where the throughput is sensitive to Propagation Delay, resulting in the bandwidth of each node is frequently idle. The parallel operations consist of three parts. First, the <strong>Broadcast</strong> layer is decoupled from the <strong>Agreement</strong> layer and they run in parallel, where the <strong>Broadcast</strong> is for preparing the inputs for each consensus, and the <strong>Agreement</strong> is for determining the inputs. Secondly, instead of only the leader, all the nodes can prepare the inputs in parallel. Lastly, the node can prepare each input in parallel, which means that it can directly prepare its next input without waiting for the completion of its preceding preparation. We have conducted the experiments and compared our P-HotStuff with HotStuff and the latest work Motorway. The experimental results show that P-HotStuff can achieve an average throughput that is about 20 times that of HotStuff and 50% higher than that of Motorway under the condition of about 60 nodes, 256 Bytes payload, batch size of 400 and 100 Mbps bandwidth in a Wide Area Network spanning multiple states with an average propagation delay of 260 ms.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111183"},"PeriodicalIF":4.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642187","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}

{"title":"ArchW3: An adaptive blockchain wallet architecture for Web3 applications","authors":"E.M. Cruz ,&nbsp;J.R.D.S. Júnior ,&nbsp;Y.H.J. Souza ,&nbsp;G.L.S.S. Jesus ,&nbsp;M.L.M. Peixoto","doi":"10.1016/j.comnet.2025.111182","DOIUrl":"10.1016/j.comnet.2025.111182","url":null,"abstract":"<div><div>The evolution of Web3 technologies presents significant challenges in integrating decentralized systems with Web2 infrastructures, particularly in secure digital asset management and blockchain interoperability. Existing digital wallet solutions struggle to ensure seamless interoperability, robust key management, and effective integration with both public and private blockchain networks, all while meeting the growing demand for flexible and user-friendly solutions in this rapidly expanding market. To tackle these issues, this paper introduces ArchW3, a modular framework designed to address these challenges through three core components: a Custody Service for secure key management, a Provider Service ensuring blockchain network interoperability, and a Web2/Web3 Communication Interface to simplify application development. Experimental validation involved a <span><math><msup><mrow><mn>2</mn></mrow><mrow><mi>k</mi></mrow></msup></math></span> factorial design, analyzing <em>transaction processing time</em>, <em>memory usage</em>, <em>CPU usage</em>, and <em>energy consumption under diverse configurations</em>. Results demonstrated ArchW3’s adaptability across EVM and non-EVM networks, such as Ganache and Solana, respectively. Solana exhibited superior efficiency, achieving up to 85% in memory and energy performance under high transactional loads, while EVM-Ganache excelled in low-load processing scenarios with up to 40% better performance. The ArchW3 framework was successfully deployed at Bank BV in Brazil, showcasing its applicability in real financial environments by integrating banking services with Web3 infrastructure.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111182"},"PeriodicalIF":4.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636497","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}

{"title":"Deep Reinforcement Learning and SQP-driven task offloading decisions in vehicular edge computing networks","authors":"Ehzaz Mustafa ,&nbsp;Junaid Shuja ,&nbsp;Faisal Rehman ,&nbsp;Abdallah Namoun ,&nbsp;Muhammad Bilal ,&nbsp;Kashif Bilal","doi":"10.1016/j.comnet.2025.111180","DOIUrl":"10.1016/j.comnet.2025.111180","url":null,"abstract":"<div><div>Vehicular Edge Computing offers low latency and reduced energy consumption for innovative applications through computation offloading in vehicular networks. However, making optimal offloading decisions and resource allocation remains challenging due to varying speeds, locations, channel quality constraints, and characteristics of both vehicles and tasks. To address these challenges, we propose a three-layered architecture and introduce a two-level algorithm named Sequential Quadratic Programming-based Dueling Double Deep Q Networks (SQ-DDTO) for optimal offloading actions and resource allocation. The joint computation offloading decision and resource allocation is a mixed integer nonlinear programming problem. To solve it, we first decouple the computation offloading decision sub-problem from resource allocation and address it using Dueling DDQN, which incorporates separate state values and action advantages. This decomposition allows for more granular control of computation tasks, leading to significantly better results. To enhance sample efficiency and learning in such complex networks, we employ Prioritized Experience Replay (PER). By prioritizing experiences based on their importance, PER enhances learning efficiency, allowing the agent to adapt quickly to changing conditions and optimize task offloading decisions in real time. Following this decomposition, we use Sequential Quadratic Programming (SQP) to solve for optimal resource allocation. SQP is chosen due to its effectiveness in handling non-convexity and complex constraints. Moreover, it has strong local convergence properties and utilizes gradient information which is crucial where rapid decision-making is necessary. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of average delay, energy consumption, and task loss rate. For example. the proposed algorithm reduces the system cost by 25.1% compared to DQN and 16.67% compared to both DDQN and DDPG. Similarly. our method reduces the task loss rate by 37.06% compared to DQN, 34.78% compared to DDPG and 10.2% compared to DDQN.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111180"},"PeriodicalIF":4.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620578","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}

{"title":"SK-CFR: Rerouting critical flows through discrete soft actor–critic within the KP-GNN framework","authors":"Lianming Zhang,&nbsp;Shuqiang Peng,&nbsp;Pingping Dong","doi":"10.1016/j.comnet.2025.111175","DOIUrl":"10.1016/j.comnet.2025.111175","url":null,"abstract":"<div><div>Intelligent routing methodologies often necessitate the rerouting of a significant portion of traffic, leading to superfluous overhead and erratic network performance marked by heightened End-to-End (E2E) latency. A promising approach involves harnessing reinforcement learning to pinpoint and redirect traffic that exerts a substantial impact on network performance. To minimize overhead and achieve optimal latency, we introduce an innovative routing solution, SK-CFR — founded on Discrete Soft Actor–Critic and <span><math><mi>K</mi></math></span>-hop message Passing Graph Neural Network (KP-GNN) for Critical Flow Rerouting — that is rooted in this strategic framework. This solution integrates bounding subgraphs within the KP-GNN framework, enabling enhanced feature extraction via an expanded dimensionality in the graph’s structure. Furthermore, to seamlessly adapt to the discrete action space, we have refined and deployed the Discrete Soft Actor–Critic (DSAC) algorithm, guaranteeing a more efficient exploration of critical flows by leveraging entropy regularization throughout the training phase. Our solution has undergone rigorous simulation across four real-world network topologies, yielding a remarkable 12% reduction in network latency compared to state-of-the-art Critical Flow Rerouting-Reinforcement Learning (CFR-RL) methods, while demonstrating robust resilience against dynamic network changes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111175"},"PeriodicalIF":4.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643850","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}

{"title":"RAGN: Detecting unknown malicious network traffic using a robust adaptive graph neural network","authors":"Ernest Akpaku ,&nbsp;Jinfu Chen ,&nbsp;Mukhtar Ahmed ,&nbsp;Francis Kwadzo Agbenyegah ,&nbsp;William Leslie Brown-Acquaye","doi":"10.1016/j.comnet.2025.111184","DOIUrl":"10.1016/j.comnet.2025.111184","url":null,"abstract":"<div><div>As network environments evolve, detecting unknown malicious network traffic becomes increasingly challenging due to the dynamic and sophisticated nature of modern cyberattacks. Graph Attention Networks (GATs) have shown promise in modeling complex network interactions but remain vulnerable to adversarial attacks that exploit weaknesses in the graph structure. In this work, we propose the Robust Adaptive Graph Neural Network (RAGN), an enhanced GAT-based framework that introduces adaptive attention mechanisms to improve detection accuracy and robustness against adversarial manipulations in network traffic graphs. RAGN iteratively adjusts the graph structure and feature space to suppress adversarial perturbations by assigning lower attention scores to unreliable edges and refining feature representations based on the feature smoothness regularization principle. To assess the robustness of the proposed RAGN model and compare it with baseline models, we introduced an effective dynamic graph attack method known as Semantic-Preserving Adversarial Node Injection (SPAN). We benchmarked its performance against state-of-the-art graph attack methods, including DICE, DGA, and RWCS. SPAN incrementally injects small batches of malicious nodes, refining their edges and features to target both the structural and temporal aspects of dynamic graphs. It preserves semantic integrity, and generates effective yet imperceptible perturbations, providing a rigorous test of the resilience of graph neural networks. Experiments conducted on four datasets, demonstrate that RAGN demonstrates robustness against adversarial, and zero-day attacks. It also demonstrates resilience against targeted, malicious node injection attacks in dynamic network environments. RAGN demonstrated consistent robustness, with misclassification rates increasing only marginally (by less than 1.2%) even under significant dynamic changes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111184"},"PeriodicalIF":4.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620582","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}

{"title":"Decentralized traffic detection utilizing blockchain-federated learning with quality-driven aggregation","authors":"Wei Liu ,&nbsp;Wentao Cui ,&nbsp;Bin Wang ,&nbsp;Heng Pan ,&nbsp;Wei She ,&nbsp;Zhao Tian","doi":"10.1016/j.comnet.2025.111179","DOIUrl":"10.1016/j.comnet.2025.111179","url":null,"abstract":"<div><div>Federated Learning (FL) has been widely applied in network traffic detection to address issues such as insufficient data, data imbalance, and limited data sources. However, FL still has some drawbacks, including excessive load on the central server, vulnerability to attacks, and the potential presence of malicious or low-quality local models during aggregation. In this paper, we propose a novel approach for encrypted traffic classification to promote reliable data sharing and improve classification accuracy. First, we design a four-layer framework for secure traffic classification, based on FL and blockchain to replace the central server. In this framework, each client dynamically switches between the trainer and the validator, either training or validating the local model, with the validator ultimately uploading the global model to the blockchain. Furthermore, to address the issues of potential malicious and low-quality model in aggregation, we propose a new Quality-Driven Validator-Trainer Aggregation (QDVTA) algorithm. The algorithm selectively filters out malicious and low-quality models in each round of aggregation, improving the robustness of the framework while minimizing the loss in model accuracy. Experiments were conducted on the ISCXVPN2016, ISCXTor2016, and CICIoT2022 datasets. Compared to existing methods, the proposed approach achieves accuracy rates of 89.19%, 89.50%, and 94.42% in the presence of malicious nodes, demonstrating its effectiveness over state-of-the-art methods.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111179"},"PeriodicalIF":4.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620579","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}

{"title":"Social network botnet attack mitigation model for cloud","authors":"Hooman Alavizadeh,&nbsp;Ahmad Salehi S.,&nbsp;A.S.M. Kayes,&nbsp;Wenny Rahayu,&nbsp;Tharam Dillon","doi":"10.1016/j.comnet.2025.111160","DOIUrl":"10.1016/j.comnet.2025.111160","url":null,"abstract":"<div><div>Online Social Network (OSN) botnet attacks pose a growing threat to the cloud environment and reduce the services’ availability and reliability for users by launching distributed denial of service (DDoS) attacks on crucial servers in the cloud. These attacks involve the deployment of sophisticated botnets that exploit the interconnected nature of social networks to identify targets, exploit vulnerabilities, and launch attacks. The prevalence and impact of these botnet-driven attacks have recently been studied. Although the detection of these botnet attacks is still a challenging process, it remains crucial to gain a comprehensive understanding of and evaluate the best defense strategies against botnet attacks. This evaluation can be further utilized to formulate effective defense plans to mitigate the impact of such botnet attacks. In this paper, we first investigate the properties of OSN botnet attack stages that eventually lead to launching DDoS attacks toward a cloud system. Then, we formalize a defensive model using a sequential game model to analyze both the attacker’s and defenders’ best equilibrium strategies for the proposed botnet attack scenario. Moreover, we formulate optimal strategies for the defender against various attack strategies. Our experiments reveal the best defense strategies against various attack rates to maintain cloud functionality. Finally, we discuss possible countermeasures for these OSN botnet threats.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111160"},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620580","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}

{"title":"Yardstick-Stackelberg pricing-based incentive mechanism for Federated Learning in Edge Computing","authors":"Qianhui Yu ,&nbsp;Hai Xue ,&nbsp;Celimuge Wu ,&nbsp;Ya Liu ,&nbsp;Wunan Guo","doi":"10.1016/j.comnet.2025.111186","DOIUrl":"10.1016/j.comnet.2025.111186","url":null,"abstract":"<div><div>Federated Learning (FL) enables collaborative model training across multiple participants without sharing original data, making it a valuable tool for preserving privacy in Mobile Edge Computing (MEC) environment. However, due to users’ varying levels of motivation and commitment, it is challenging to incentivize effective participation in FL. To address this, we propose a pricing-based incentive mechanism that enhances FL efficiency and energy sustainability in MEC. To be specific, we firstly develop the formula of incentive mechanism based on the yardstick pricing rule. Subsequently, we determine the optimal hyperparameters of the utility function aiming to maximize model accuracy. Additionally, we formulate a Stackelberg game to derive optimal reward strategies, balancing users’ transmission power allocation and the server’s reward distribution. Simulation results show that our proposed scheme outperforms other existing schemes with over 98.2% accuracy, 0.7% server utility enhancement, and 14.6% server loss decrease compared with static incentives. Moreover, our proposed scheme contributes to faster growth in both server and users utilities when compared with the advanced schemes by varying user numbers, which demonstrates its better scalability and adaptability.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111186"},"PeriodicalIF":4.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620581","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}

{"title":"Dynamic reconfiguration of wireless sensor networks: A survey","authors":"Salma Najjar ,&nbsp;Michael David ,&nbsp;William Derigent ,&nbsp;Ahmed Zouinkhi","doi":"10.1016/j.comnet.2025.111176","DOIUrl":"10.1016/j.comnet.2025.111176","url":null,"abstract":"<div><div>To control the lifetime and optimize the energy consumption of wireless sensor networks (WSNs), while ensuring that application needs are met, it is essential to dynamically reconfigure the network by adjusting the parameters of its nodes. A great deal of research has been devoted to proposing methods for reconfiguring WSNs. However, there are no comprehensive studies analyzing the different reconfiguration parameters, architectures or strategies. For this reason, this paper proposes the first systematic literature review on reconfiguration mechanisms for self-organized wireless sensor networks (SOWSNs) and those controlled by an external controller, following the principles of software-defined wireless sensor networks (SDWSNs), with a particular focus on energy optimization. The main objective of this work is to explore reconfiguration strategies in depth from three different aspects: the parameters adjusted to optimize energy (What?), the architectures used for decision making (Who?), and the moments of reconfiguration implementation (When?). In addition, this study identifies unexploited areas in this field and suggests that hybrid and predictive approaches could be a promising way of overcoming these gaps.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111176"},"PeriodicalIF":4.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"MAID: Mobility-aware information dissemination in mobile IoT using temporal point processes","authors":"Yongqing Cai,&nbsp;Dianjie Lu,&nbsp;Jing Chen,&nbsp;Guijuan Zhang","doi":"10.1016/j.comnet.2025.111173","DOIUrl":"10.1016/j.comnet.2025.111173","url":null,"abstract":"<div><div>The mobile Internet of Things (IoT) integrates mobile communication technology with IoT to connect various physical devices (e.g., sensors, smart devices, vehicles, and home appliances) for data collection, processing, and distribution. However, current research on mobile IoT information dissemination overlooks the stochastic nature of device mobility, leading to inaccurate predictions of dissemination scale. To address this, we propose a mobility-aware information dissemination model (MAID) using the temporal point process (TPP) to investigate the stochastic dynamics introduced by device mobility. First, we develop a TPP-based model to describe random events, such as movement, linking, unlinking, and information dissemination. We propose a mobility-aware intensity prediction method to calculate event intensities within the TPP framework. Finally, we predict the scale of information dissemination on the basis of the calculated intensity and develop an event-driven simulation system to model network structure changes and information dissemination within the mobile IoT. The simulation results indicate that device mobility accelerates network structure changes, thereby increasing the scope and scale of information dissemination. This dynamic has a two-sided effect on dissemination efficiency, depending on the initial network sparsity. Extensive experiments on synthetic datasets show that our method improves the accuracy of dissemination scale prediction by over 92% compared to four baseline methods.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"262 ","pages":"Article 111173"},"PeriodicalIF":4.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609794","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}