IEEE Transactions on Network Science and Engineering最新文献_第6页

Intensity Based Event Detection in Sensor Based IoT 传感器物联网中基于强度的事件检测

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-03 DOI: 10.1109/TNSE.2025.3556057

Anubhav Shivhare;Adarsh Prasad Behera;Manish Kumar

{"title":"Intensity Based Event Detection in Sensor Based IoT","authors":"Anubhav Shivhare;Adarsh Prasad Behera;Manish Kumar","doi":"10.1109/TNSE.2025.3556057","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556057","url":null,"abstract":"Finding an optimum trade-off between event detection and network lifetime is a major problem in the sensor-based Internet of Things framework. Further, reliable, effective, and accurate event detection is a perennial research problem explored in the domain of Sensor Based Internet of Things (SBIoT). Major research problems focusing on event detection depend upon models like Boolean and probabilistic sensing models. However, event detection is practically dependent upon the intensity and persistence of the event. The traditional non-intensity-based event sensing models fix a predefined sensing radius. Any occurrence outside the sensing radius is not considered an event, independent of its severity. The present work argues that the intensity and persistence of the event are also relevant parameters for event detection. This paper proposes two novel event intensity and persistence-based models for detecting different types of events and improving upon the quality of detection. The proposed <italic>’Improved' model proves to be more efficient than the proposed <italic>’Conventional' model. Further, the simulation results indicate the proposed algorithm's efficiency and effectiveness, and compare it with <italic>Non-intensity based models. Additionally, the results are compared in terms of detection accuracy, node activation, and network lifetime to show the efficiency and trade-offs of the proposed scheme.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3039-3050"},"PeriodicalIF":6.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492474","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

An Adaptive AQM Based on the Consecutive Change Detection in the Programmable Queue 基于可编程队列连续变化检测的自适应AQM

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-02 DOI: 10.1109/TNSE.2025.3557164

Xinyue Jiang;Dezhang Kong;Xiang Chen;Shuangxi Chen;Haifeng Zhou;Chunming Wu;Xuan Liu;Wei Ruan

引用次数: 0

Mitigating Social Engineering Attacks Through Cost-Effective Security Awareness Training Policy 通过具有成本效益的安全意识培训策略减轻社会工程攻击

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-02 DOI: 10.1109/TNSE.2025.3556927

Yang Qin;Xiaofan Yang;Lu-Xing Yang;Kaifan Huang

{"title":"Mitigating Social Engineering Attacks Through Cost-Effective Security Awareness Training Policy","authors":"Yang Qin;Xiaofan Yang;Lu-Xing Yang;Kaifan Huang","doi":"10.1109/TNSE.2025.3556927","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556927","url":null,"abstract":"Human beings are often considered the weakest link in cybersecurity. Social engineering attacks exploit this vulnerability, posing significant threats to the digital assets of organizations. A highly effective strategy to protect users from falling into traps set by attackers is to implement comprehensive security awareness training focused on social engineering. In this context, the organization needs to find a cost-effective policy of allocating the security awareness training cost. We refer to the problem of finding such a policy as the security awareness training (SAT) problem. This paper addresses the SAT problem. First, an opinion dynamics-based security awareness evolution model is introduced. On this basis, the SAT problem is reduced to an optimal control model (the SAT model). Second, by deriving the optimality system for the SAT problem, an algorithm of solving the SAT model is proposed. Next, the feasibility of the proposed algorithm is validated through numerical experiments. Additionally, further exploration of the SAT algorithm are conducted. Finally, for greater versatility, the problem is formulated as a discrete-time problem (the discrete SAT problem), and the discrete SAT algorithm is proposed to solve it. This work takes the first step toward the prevention of social engineering attack through optimal control approach.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3145-3158"},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492338","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

A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty 拓扑不确定性下探索性学习辅助社区检测的统一框架

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-02 DOI: 10.1109/TNSE.2025.3557041

Yu Hou;Cong Tran;Ming Li;Won-Yong Shin

{"title":"A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty","authors":"Yu Hou;Cong Tran;Ming Li;Won-Yong Shin","doi":"10.1109/TNSE.2025.3557041","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3557041","url":null,"abstract":"In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often <italic>uncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we present <monospace>META-CODE</monospace>, a unified framework for detecting overlapping communities via <italic>exploratory learning aided by <italic>easy-to-collect node metadata when networks are topologically unknown (or only partially known). Specifically, <monospace>META-CODE</monospace> consists of three iterative steps in addition to the initial network inference step: 1) node-level <italic>community-affiliation embeddings based on graph neural networks (GNNs) trained by our new reconstruction loss, 2) <italic>network exploration via community-affiliation-based node queries, and 3) <italic>network inference using an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority of <monospace>META-CODE</monospace> over benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module in <monospace>META-CODE</monospace>, (c) the effectiveness of node queries in <monospace>META-CODE</monospace> based on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3159-3176"},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492339","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

Attack Detection and Location Using State Forecasting in Multivariate Time Series of ICS 基于状态预测的ICS多变量时间序列攻击检测与定位

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3555764

Guoyan Cao;Yue Wu;Dengxiu Yu;Zhen Wang

引用次数: 0

QECO: A QoE-Oriented Computation Offloading Algorithm Based on Deep Reinforcement Learning for Mobile Edge Computing QECO：一种面向qoe的基于深度强化学习的移动边缘计算卸载算法

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556809

Iman Rahmaty;Hamed Shah-Mansouri;Ali Movaghar

{"title":"QECO: A QoE-Oriented Computation Offloading Algorithm Based on Deep Reinforcement Learning for Mobile Edge Computing","authors":"Iman Rahmaty;Hamed Shah-Mansouri;Ali Movaghar","doi":"10.1109/TNSE.2025.3556809","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556809","url":null,"abstract":"In the realm of mobile edge computing (MEC), efficient computation task offloading plays a pivotal role in ensuring a seamless quality of experience (QoE) for users. Maintaining a high QoE is paramount in today's interconnected world, where users demand reliable services. This challenge stands as one of the most primary key factors contributing to handling dynamic and uncertain mobile environments. In this study, we delve into computation offloading in MEC systems, where strict task processing deadlines and energy constraints can adversely affect the system performance. We formulate the computation task offloading problem as a Markov decision process (MDP) to maximize the long-term QoE of each user individually. We propose a distributed QoE-oriented computation offloading (QECO) algorithm based on deep reinforcement learning (DRL) that empowers mobile devices to make their offloading decisions without requiring knowledge of decisions made by other devices. Through numerical studies, we evaluate the performance of QECO. Simulation results reveal that compared to the state-of-the-art existing works, QECO increases the number of completed tasks by up to 14.4%, while simultaneously reducing task delay and energy consumption by 9.2% and 6.3%, respectively. Together, these improvements result in a significant average QoE enhancement of 37.1%. This substantial improvement is achieved by accurately accounting for user dynamics and edge server workloads when making intelligent offloading decisions. This highlights QECO's effectiveness in enhancing users' experience in MEC systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3118-3130"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492445","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

A Blockchain-Assisted Authentication Protocol for RFID-Enabled Supply Chain Management System 基于rfid的供应链管理系统的区块链辅助认证协议

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556736

Tayyaba Tariq;Mohammad S. Obaidat;Wen-Chung Kuo;Khalid Mahmood;Muhammad Faizan Ayub;Mohammed J.F. Alenazi

{"title":"A Blockchain-Assisted Authentication Protocol for RFID-Enabled Supply Chain Management System","authors":"Tayyaba Tariq;Mohammad S. Obaidat;Wen-Chung Kuo;Khalid Mahmood;Muhammad Faizan Ayub;Mohammed J.F. Alenazi","doi":"10.1109/TNSE.2025.3556736","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556736","url":null,"abstract":"In the dynamic domain of supply chain management, integrating Radio-Frequency Identification (RFID) technology with blockchain technology represents a significant leap forward. This integration has created a blockchain-assisted RFID-enabled Supply Chain Management System (SCMS). With its ability to utilize electromagnetic fields to identify and track tags attached to objects, RFID technology has revolutionized product management and tracking within supply chains. SCMS transmits tracking and product management information through public communication channels. However, SCMS’s communication on these channels is vulnerable to various security attacks and privacy challenges. Numerous authentication protocols have recently been proposed to tackle these security and privacy challenges. Unfortunately, most protocols are susceptible to different security attacks, such as tag or reader impersonation, denial of service, physical cloning, desynchronization attacks, etc. Therefore, we have proposed an authentication protocol for an RFID-enabled SCMS that also leverages blockchain technology. The integration of blockchain technology ensures data integrity, immutability and transparency across each department involved in SCMS. In the proposed protocol, we also employ a Physically Unclonable Function (PUF) to secure SCMS against physical cloning attacks. We validate the security of the proposed protocol through both informal and formal analysis. The informal analysis confirms that our protocol significantly enhances security and efficiency. Moreover, a performance analysis of the proposed protocol against various competing existing protocols shows its superior performance. Notably, the proposed protocol substantially reduces computational and communication costs by 24.38% and 8.03%, respectively, which underscores its enhanced performance and resource efficiency.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3108-3117"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492275","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

An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning 基于多任务深度强化学习的自适应服务功能链映射

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-01 DOI: 10.1109/TNSE.2025.3556390

Wenting Wei;Qingyi Wang;Huaxi Gu;Danyang Zheng;Ning Zhang;Celimuge Wu

{"title":"An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning","authors":"Wenting Wei;Qingyi Wang;Huaxi Gu;Danyang Zheng;Ning Zhang;Celimuge Wu","doi":"10.1109/TNSE.2025.3556390","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3556390","url":null,"abstract":"Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3093-3107"},"PeriodicalIF":6.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492430","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

Temporal Graph Reproduction With RWIG 用RWIG再现时间图

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3555974

Sergey Shvydun;Anton-David Almasan;Piet Van Mieghem

引用次数: 0

Advancing Non-Intrusive Load Monitoring: Predicting Appliance-Level Power Consumption With Indirect Supervision 推进非侵入式负荷监测：用间接监测预测电器级电力消耗

IF 6.7 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI: 10.1109/TNSE.2025.3555618

Jialing He;Junsen Feng;Shangwei Guo;Zhuo Chen;Yiwei Liu;Tao Xiang;Liehuang Zhu

{"title":"Advancing Non-Intrusive Load Monitoring: Predicting Appliance-Level Power Consumption With Indirect Supervision","authors":"Jialing He;Junsen Feng;Shangwei Guo;Zhuo Chen;Yiwei Liu;Tao Xiang;Liehuang Zhu","doi":"10.1109/TNSE.2025.3555618","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3555618","url":null,"abstract":"Deep Neural Networks (DNNs) have made significant progress in addressing the Non-Intrusive Load Monitoring (NILM) task, which aims to disaggregate appliance-level power signals from aggregated meter readings. Despite these advancements, existing DNN-based NILM approaches rely on training with power signals from individual appliances, which are obtained intrusively through sensor installations. This method is not only expensive but also poses a risk of damaging the original circuits. To overcome these limitations, we introduce the State-based Supervised NILM (SS-NILM) problem. Instead of using appliance power signal labels, we leverage on-off state information, which can be collected in a non-intrusive manner. However, solving SS-NILM presents a challenge, as it requires developing a model that maps on-off state labels to the corresponding appliance power signals in an indirectly supervised setting. In this work, we propose a state-based DNN that predicts the power signals of multiple target appliances simultaneously. The model is trained by minimizing the discrepancy between the aggregated prediction and the true aggregated power signal. Additionally, the model predicts the on-off states of appliances, which are used as auxiliary information to improve the accuracy of power signal predictions. Extensive experiments conducted on real-world datasets demonstrate that our model, trained using non-intrusive on-off state information, achieves performance comparable to that of traditional NILM models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2957-2973"},"PeriodicalIF":6.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492213","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