IEEE Transactions on Network and Service Management最新文献

{"title":"Exploring QUIC Security and Privacy: A Comprehensive Survey on QUIC Security and Privacy Vulnerabilities, Threats, Attacks, and Future Research Directions","authors":"Y A Joarder;Carol Fung","doi":"10.1109/TNSM.2024.3457858","DOIUrl":"10.1109/TNSM.2024.3457858","url":null,"abstract":"QUIC is a modern transport protocol aiming to improve Web connection performance and security. It is the transport layer for HTTP/3. QUIC offers numerous advantages over traditional transport layer protocols, such as TCP and UDP, including reduced latency, improved congestion control, connection migration and encryption by default. However, these benefits introduce new security and privacy challenges that need to be addressed, as cyber attackers can exploit weaknesses in the protocol. QUIC’s security and privacy issues have been largely unexplored, as existing research on QUIC primarily focuses on performance upgrades. This survey paper addresses the knowledge gap in QUIC’s security and privacy challenges while proposing directions for future research to enhance its security and privacy. Our comprehensive analysis covers QUIC’s history, architecture, core mechanisms (such as cryptographic design and handshaking process), security model, and threat landscape. We examine QUIC’s significant vulnerabilities, critical security and privacy attacks, emerging threats, advanced security and privacy challenges, and mitigation strategies. Furthermore, we outline future research directions to improve QUIC’s security and privacy. By exploring the protocol’s security and privacy implications, this paper informs decision-making processes and enhances online safety for users and professionals. Our research identifies key risks, vulnerabilities, threats, and attacks targeting QUIC, providing actionable insights to strengthen the protocol. Through this comprehensive analysis, we contribute to developing and deploying a faster, more secure next-generation Internet infrastructure. We hope this investigation serves as a foundation for future Internet security and privacy innovations, ensuring robust protection for modern digital communications.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6953-6973"},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187264","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":"Efficient Queue Control Policies for Latency-Critical Traffic in Mobile Networks","authors":"Mohammed Abdullah;Salah Eddine Elayoubi;Tijani Chahed","doi":"10.1109/TNSM.2024.3458390","DOIUrl":"10.1109/TNSM.2024.3458390","url":null,"abstract":"We propose a novel resource allocation framework for latency-critical traffic, namely Ultra Reliable Low Latency Communications (URLLC), in mobile networks which meets stringent latency and reliability requirements while minimizing the allocated resources. The Quality of Service (QoS) requirement is formulated in terms of the probability that the latency exceeds a maximal allowed budget. We develop a discrete-time queuing model for the system, in the case where the URLLC reservation is fully-flexible, and when the reservation is made on a slot basis while URLLC packets arrive in mini-slots. We then exploit this model to propose a control scheme that dynamically updates the amount of resources to be allocated per time slot so as to meet the QoS requirement. We formulate an optimization framework that derives the policy which achieves the QoS target while minimizing resource consumption and propose offline algorithms that converge to the quasi optimal reservation policy. In the case when traffic is unknown, we propose online algorithms based on stochastic bandits to achieve this aim. Numerical experiments validate our model and confirm the efficiency of our algorithms in terms of meeting the delay violation target at minimal cost.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 5","pages":"5076-5090"},"PeriodicalIF":4.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187251","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":"Survivable Payment Channel Networks","authors":"Yekaterina Podiatchev, Ariel Orda, Ori Rottenstreich","doi":"10.1109/tnsm.2024.3456229","DOIUrl":"https://doi.org/10.1109/tnsm.2024.3456229","url":null,"abstract":"","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"8 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187252","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":"Time-Distributed Feature Learning for Internet of Things Network Traffic Classification","authors":"Yoga Suhas Kuruba Manjunath;Sihao Zhao;Xiao-Ping Zhang;Lian Zhao","doi":"10.1109/TNSM.2024.3457579","DOIUrl":"10.1109/TNSM.2024.3457579","url":null,"abstract":"Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper; therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6566-6581"},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187253","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":"DGS: An Efficient Delay-Guaranteed Scheduling Framework for Wireless Deterministic Networking","authors":"Minghui Chang;Haojun Lv;Yunqi Gao;Bing Hu;Wei Wang;Ze Yang","doi":"10.1109/TNSM.2024.3456576","DOIUrl":"10.1109/TNSM.2024.3456576","url":null,"abstract":"Deterministic Networking (DetNet) aims to provide an end-to-end ultra-reliable data network with ultra-low latency and jitter. However, implementing DetNet in wireless networks, particularly in the air interface, still faces the challenge of guaranteeing bounded delay. This paper proposes a delay-guaranteed three-layer scheduling framework for DetNet, named Deterministic Guarantee Scheduling (DGS). The top layer calculates the amount of new data entering the queue in each scheduling period and timestamps the data to track its arrival time. Based on the remaining waiting time of each flow’s data volume, the middle layer proposes a scheduling algorithm based on urgency, prioritizing the scheduling of data volumes with the shortest remaining queuing time. The lower layer fine-tunes the scheduling results obtained by the middle layer for actual transmission. We implemented the DGS framework on the 5G-air-simulator platform. Simulation results demonstrate that DGS outperforms all other mechanisms by guaranteeing delay for a larger number of deterministic flows and achieving better throughput performance.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6582-6596"},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187255","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":"Reliable Task Offloading in Sustainable Edge Computing with Imperfect Channel State Information","authors":"Peng Peng;Wentai Wu;Weiwei Lin;Fan Zhang;Yongheng Liu;Keqin Li","doi":"10.1109/TNSM.2024.3456568","DOIUrl":"10.1109/TNSM.2024.3456568","url":null,"abstract":"As a promising paradigm, edge computing enhances service provisioning by offloading tasks to powerful servers at the network edge. Meanwhile, Non-Orthogonal Multiple Access (NOMA) and renewable energy sources are increasingly adopted for spectral efficiency and carbon footprint reduction. However, these new techniques inevitably introduce reliability risks to the edge system generally because of i) imperfect Channel State Information (CSI), which can misguide offloading decisions and cause transmission outages, and ii) unstable renewable energy supply, which complicates device availability. To tackle these issues, we first establish a system model that measures service reliability based on probabilistic principles for the NOMA-based edge system. As a solution, a Reliable Offloading method with Multi-Agent deep reinforcement learning (ROMA) is proposed. In ROMA, we first reformulate the reliability-critical constraint into an long-term optimization problem via Lyapunov optimization. We discretize the hybrid action space and convert the resource allocation on edge servers into a 0-1 knapsack problem. The optimization problem is then formulated as a Partially Observable Markov Decision Process (POMDP) and addressed by multi-agent proximal policy optimization (PPO). Experimental evaluations demonstrate the superiority of ROMA over existing methods in reducing grid energy costs and enhancing system reliability, achieving Pareto-optimal performance under various settings.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6423-6436"},"PeriodicalIF":4.7,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187254","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":"Causal Genetic Network Anomaly Detection Method for Imbalanced Data and Information Redundancy","authors":"Zengri Zeng;Xuhui Liu;Ming Dai;Jian Zheng;Xiaoheng Deng;Detian Zeng;Jie Chen","doi":"10.1109/TNSM.2024.3455768","DOIUrl":"10.1109/TNSM.2024.3455768","url":null,"abstract":"The proliferation of Internet-connected devices and the complexity of modern network environments have led to the collection of massive and high-dimensional datasets, resulting in substantial information redundancy and sample imbalance issues. These challenges not only hinder the computational efficiency and generalizability of anomaly detection systems but also compromise their ability to detect rare attack types, posing significant security threats. To address these pressing issues, we propose a novel causal genetic network-based anomaly detection method, the CNSGA, which integrates causal inference and the nondominated sorting genetic algorithm-III (NSGA-III). The CNSGA leverages causal reasoning to exclude irrelevant information, focusing solely on the features that are causally related to the outcome labels. Simultaneously, NSGA-III iteratively eliminates redundant information and prioritizes minority samples, thereby enhancing detection performance. To quantitatively assess the improvements achieved, we introduce two indices: a detection balance index and an optimal feature subset index. These indices, along with the causal effect weights, serve as fitness metrics for iterative optimization. The optimized individuals are then selected for subsequent population generation on the basis of nondominated reference point ordering. The experimental results obtained with four real-world network attack datasets demonstrate that the CNSGA significantly outperforms existing methods in terms of overall precision, the imbalance index, and the optimal feature subset index, with maximum increases exceeding 10%, 0.5, and 50%, respectively. Notably, for the CICDDoS2019 dataset, the CNSGA requires only 16-dimensional features to effectively detect more than 70% of all sample types, including 6 more network attack sample types than the other methods detect. The significance and impact of this work encompass the ability to eliminate redundant information, increase detection rates, balance attack detection systems, and ensure stability and generalizability. The proposed CNSGA framework represents a significant step forward in developing efficient and accurate anomaly detection systems capable of defending against a wide range of cyber threats in complex network environments.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6937-6952"},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187256","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":"MobiFi: Mobility-Aware Reactive and Proactive Wireless Resource Management in LiFi-WiFi Networks","authors":"Hansini Vijayaraghavan;Wolfgang Kellerer","doi":"10.1109/TNSM.2024.3455105","DOIUrl":"10.1109/TNSM.2024.3455105","url":null,"abstract":"This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6597-6613"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187257","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":"Multi-Agent DRL-Based Two-Timescale Resource Allocation for Network Slicing in V2X Communications","authors":"Binbin Lu;Yuan Wu;Liping Qian;Sheng Zhou;Haixia Zhang;Rongxing Lu","doi":"10.1109/TNSM.2024.3454758","DOIUrl":"10.1109/TNSM.2024.3454758","url":null,"abstract":"Network slicing has been envisioned to play a crucial role in supporting various vehicular applications with diverse performance requirements in dynamic Vehicle-to-Everything (V2X) communications systems. However, time-varying Service Level Agreements (SLAs) of slices and fast-changing network topologies in V2X scenarios may introduce new challenges for enabling efficient inter-slice resource provisioning to guarantee the Quality of Service (QoS) while avoiding both resource over-provisioning and under-provisioning. Moreover, the conventional centralized resource allocation schemes requiring global slice information may degrade the data privacy provided by dedicated resource provisioning. To address these challenges, in this paper, we propose a two-timescale resource management mechanism for providing diverse V2X slices with customized resources. In the long timescale, we propose a Proximal Policy Optimization-based multi-agent deep reinforcement learning algorithm for dynamically allocating bandwidth resources to different slices for guaranteeing their SLAs. Under the coordination of agents, each agent only observes its partial state space rather than the global information to adjust the resource requests, which can enhance the privacy protection. Moreover, an expert demonstration mechanism is proposed to guide the action policy for reducing the invalid action exploration and accelerating the convergence of agents. In the short-term time slot, with our proposed Cross Entropy and Successive Convex Approximation algorithm, each slice allocates its available physical resource blocks and optimizes its transmit power to meet the QoS. Simulation results show our proposed two-timescale resource allocation scheme for network slicing can achieve maximum 8.4% performance gains in terms of spectral efficiency while guaranteeing the QoS requirements of users compared to the baseline approaches.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6744-6758"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187259","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":"A Deep Learning System for Detecting IoT Web Attacks With a Joint Embedded Prediction Architecture (JEPA)","authors":"Yufei An;F. Richard Yu;Ying He;Jianqiang Li;Jianyong Chen;Victor C. M. Leung","doi":"10.1109/TNSM.2024.3454777","DOIUrl":"10.1109/TNSM.2024.3454777","url":null,"abstract":"The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"21 6","pages":"6885-6898"},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187260","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}