Data stream processing holds great potential value in lots of practical application scenarios. This paper studies two new but important patterns for items in data streams, called promising and damping items. The promising items mean that the frequencies of an item in multiple continuous time windows show an upward trend overall, while a slight decrease in some of these windows is allowed. In contrast to promising items exhibiting an increasing trend, the definition of damping items indicates a decreasing trend. Many applications can benefit from the property of promising or damping items, e.g., monitoring latent attacks in computer networks, pre-adjusting bandwidth allocation in communication channels, detecting potential hot events/news, or finding topics that gradually lose momentum in social networks. We first introduce how to accurately find promising items in data streams in real-time under limited memory space. To this end, we propose a novel structure named Scout Sketch, which consists of Filter and Finder. Filter is devised based on the Bloom filter to eliminate the ungratified items with less memory overload; Finder records some necessary information about the potential items and detects the promising items at the end of each time window, where we propose some tailor-made detection operations. We then enhance Scout Sketch (called Scout Sketch+) to adaptively detect both types of promising and damping items simultaneously. Finally, we conducted extensive experiments on four real-world datasets, which show that the F1 Score and throughput of Scout Sketch(+) are about 2.02 and 5.61 times that of the compared solutions. All source codes are available at Github ( https://github.com/Aoohhh/ScoutSketch ).
{"title":"Scout Sketch+: Finding Both Promising and Damping Items Simultaneously in Data Streams","authors":"Guoju Gao;Tianyu Ma;He Huang;Yu-E Sun;Haibo Wang;Yang Du;Shigang Chen","doi":"10.1109/TNET.2024.3469196","DOIUrl":"https://doi.org/10.1109/TNET.2024.3469196","url":null,"abstract":"Data stream processing holds great potential value in lots of practical application scenarios. This paper studies two new but important patterns for items in data streams, called promising and damping items. The promising items mean that the frequencies of an item in multiple continuous time windows show an upward trend overall, while a slight decrease in some of these windows is allowed. In contrast to promising items exhibiting an increasing trend, the definition of damping items indicates a decreasing trend. Many applications can benefit from the property of promising or damping items, e.g., monitoring latent attacks in computer networks, pre-adjusting bandwidth allocation in communication channels, detecting potential hot events/news, or finding topics that gradually lose momentum in social networks. We first introduce how to accurately find promising items in data streams in real-time under limited memory space. To this end, we propose a novel structure named Scout Sketch, which consists of Filter and Finder. Filter is devised based on the Bloom filter to eliminate the ungratified items with less memory overload; Finder records some necessary information about the potential items and detects the promising items at the end of each time window, where we propose some tailor-made detection operations. We then enhance Scout Sketch (called Scout Sketch+) to adaptively detect both types of promising and damping items simultaneously. Finally, we conducted extensive experiments on four real-world datasets, which show that the F1 Score and throughput of Scout Sketch(+) are about 2.02 and 5.61 times that of the compared solutions. All source codes are available at Github (\u0000<uri>https://github.com/Aoohhh/ScoutSketch</uri>\u0000).","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5491-5506"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/TNET.2024.3469386
Kun Wang;Chengcheng Zhao;Jinpei Chu;Yiping Shi;Jianyuan Lu;Biao Lyu;Shunmin Zhu;Peng Cheng;Jiming Chen
The Virtual Private Cloud (VPC) service enables users to configure shared resources within public clouds on demand, providing isolation between users. However, configuring the VPC network is a complex and error-prone task, and misconfiguration has been the leading cause of cloud network security issues. The large number of complex network components and configurations makes it difficult to perform scalable, efficient, and accurate fault verification of the network behavior. To address this issue, we design a comprehensive and automated fault diagnosis and localization tool, called LFVeri , which is built upon an innovative modular network model that accurately captures the logic functions of real components within VPC networks, and propose eleven functions to verify network reachability and security requirements. We conduct performance testing of LFVeri on various datasets and compared it with other verification tools. The experiments show that LFVeri outperforms in modeling and analyzing real VPC scenarios while also possessing the fastest verification speed. It can model and analyze large VPC networks with tens of thousands of components and millions of configuration rules in less than half an hour.
{"title":"LFVeri: Network Configuration Verification for Virtual Private Cloud Networks","authors":"Kun Wang;Chengcheng Zhao;Jinpei Chu;Yiping Shi;Jianyuan Lu;Biao Lyu;Shunmin Zhu;Peng Cheng;Jiming Chen","doi":"10.1109/TNET.2024.3469386","DOIUrl":"https://doi.org/10.1109/TNET.2024.3469386","url":null,"abstract":"The Virtual Private Cloud (VPC) service enables users to configure shared resources within public clouds on demand, providing isolation between users. However, configuring the VPC network is a complex and error-prone task, and misconfiguration has been the leading cause of cloud network security issues. The large number of complex network components and configurations makes it difficult to perform scalable, efficient, and accurate fault verification of the network behavior. To address this issue, we design a comprehensive and automated fault diagnosis and localization tool, called \u0000<monospace>LFVeri</monospace>\u0000, which is built upon an innovative modular network model that accurately captures the logic functions of real components within VPC networks, and propose eleven functions to verify network reachability and security requirements. We conduct performance testing of \u0000<monospace>LFVeri</monospace>\u0000 on various datasets and compared it with other verification tools. The experiments show that \u0000<monospace>LFVeri</monospace>\u0000 outperforms in modeling and analyzing real VPC scenarios while also possessing the fastest verification speed. It can model and analyze large VPC networks with tens of thousands of components and millions of configuration rules in less than half an hour.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5475-5490"},"PeriodicalIF":3.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/TNET.2024.3379439
Yuntao Wang;Zhou Su;Yanghe Pan;Tom H. Luan;Ruidong Li;Shui Yu
A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. As such, SCFL considerably enhances model utility without sacrificing privacy in a low-cost and highly feasible manner. We unfold the design of SCFL in three steps. i) Stable social cluster formation. Considering users’ heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants’ model updates depending on social trust degrees. iii) Distributed convergence. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.
{"title":"Social-Aware Clustered Federated Learning With Customized Privacy Preservation","authors":"Yuntao Wang;Zhou Su;Yanghe Pan;Tom H. Luan;Ruidong Li;Shui Yu","doi":"10.1109/TNET.2024.3379439","DOIUrl":"https://doi.org/10.1109/TNET.2024.3379439","url":null,"abstract":"A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. As such, SCFL considerably enhances model utility without sacrificing privacy in a low-cost and highly feasible manner. We unfold the design of SCFL in three steps. i) Stable social cluster formation. Considering users’ heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants’ model updates depending on social trust degrees. iii) Distributed convergence. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 5","pages":"3654-3668"},"PeriodicalIF":3.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1109/TNET.2024.3466522
Peng Zhang
Computer networks are often multi-layered. For simplicity, let us focus on two-layered networks with logical layer and physical layer. Such a network can be modeled as a labeled graph $G = (V, E)$ with a label set $L = {ell _{1}, ell _{2}, {dots }, ell _{q} }$ , in which each edge (denotes logical connection) $e in E$ has a label (denotes physical link) $ell (e)$ from L. The key issue is that different edges may have the same label. In the weighted minimum Label s-t Cut problem, we are given a labeled graph $G=(V,E)$ with label set L, where each label $ell $ has a nonnegative weight $w_{ell } $ , a source $s in V$ and a sink $t in V$ . The problem asks to find a minimum weight label subset $L'$ (called a label s-t cut) such that the removal of all edges with labels in $L'$ disconnects s and t. Label s-t cut depicts the connectivity bottleneck of a layered network. It is a natural generalization of the edge connectivity of a graph. In this paper, we provide an approximation algorithm for the weighted Label s-t Cut problem with ratio $O(n^{2/3})$ , where n is the number of vertices. This is the first approximation algorithm for the problem whose ratio is given in terms of n. The key point of the algorithm is a mechanism to interpret label weight on an edge as both its length (as in the Shortest s-t Path problem) and capacity (as in the Min s-t Cut problem). Experiments on random graphs show that the algorithm has also good practical performance.
{"title":"Combining Capacity and Length: Finding Connectivity Bottleneck in a Layered Network","authors":"Peng Zhang","doi":"10.1109/TNET.2024.3466522","DOIUrl":"https://doi.org/10.1109/TNET.2024.3466522","url":null,"abstract":"Computer networks are often multi-layered. For simplicity, let us focus on two-layered networks with logical layer and physical layer. Such a network can be modeled as a labeled graph \u0000<inline-formula> <tex-math>$G = (V, E)$ </tex-math></inline-formula>\u0000 with a label set \u0000<inline-formula> <tex-math>$L = {ell _{1}, ell _{2}, {dots }, ell _{q} }$ </tex-math></inline-formula>\u0000, in which each edge (denotes logical connection) \u0000<inline-formula> <tex-math>$e in E$ </tex-math></inline-formula>\u0000 has a label (denotes physical link) \u0000<inline-formula> <tex-math>$ell (e)$ </tex-math></inline-formula>\u0000 from L. The key issue is that different edges may have the same label. In the weighted minimum Label s-t Cut problem, we are given a labeled graph \u0000<inline-formula> <tex-math>$G=(V,E)$ </tex-math></inline-formula>\u0000 with label set L, where each label \u0000<inline-formula> <tex-math>$ell $ </tex-math></inline-formula>\u0000 has a nonnegative weight \u0000<inline-formula> <tex-math>$w_{ell } $ </tex-math></inline-formula>\u0000, a source \u0000<inline-formula> <tex-math>$s in V$ </tex-math></inline-formula>\u0000 and a sink \u0000<inline-formula> <tex-math>$t in V$ </tex-math></inline-formula>\u0000. The problem asks to find a minimum weight label subset \u0000<inline-formula> <tex-math>$L'$ </tex-math></inline-formula>\u0000 (called a label s-t cut) such that the removal of all edges with labels in \u0000<inline-formula> <tex-math>$L'$ </tex-math></inline-formula>\u0000 disconnects s and t. Label s-t cut depicts the connectivity bottleneck of a layered network. It is a natural generalization of the edge connectivity of a graph. In this paper, we provide an approximation algorithm for the weighted Label s-t Cut problem with ratio \u0000<inline-formula> <tex-math>$O(n^{2/3})$ </tex-math></inline-formula>\u0000, where n is the number of vertices. This is the first approximation algorithm for the problem whose ratio is given in terms of n. The key point of the algorithm is a mechanism to interpret label weight on an edge as both its length (as in the Shortest s-t Path problem) and capacity (as in the Min s-t Cut problem). Experiments on random graphs show that the algorithm has also good practical performance.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5430-5439"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose De-RPOTA, a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a contraction region defined by a scaled errors version. Remarkably, De-RPOTA achieves a convergence rate of $mathcal {O}left ({{frac {1}{sqrt {nT}}}}right)$ in scenarios devoid of errors, matching the state-of-the-arts. Additionally, we tackle a power control challenge, breaking it down into transmitter and receiver sub-problems to hasten the De-RPOTA algorithm’s convergence. We also offer a quantifiable privacy assurance for our over-the-air computation methodology. Intriguingly, our findings suggest that network noise can actually strengthen the privacy of aggregated information, with over-the-air computation providing extra security for individual updates. Comprehensive experimental validation confirms De-RPOTA’s efficacy in communication resources limited environments. Specifically, the results on the CIFAR-10 dataset reveal nearly 30% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.
在本文中，我们提出了一种为分散学习而设计的新算法De-RPOTA，该算法通过无线计算配备了资源适应和隐私保护机制。我们从理论上分析了有限资源和有损通信对分散学习的综合影响，表明它收敛于一个由比例误差版本定义的收缩区域。值得注意的是，在没有错误的情况下，De-RPOTA的收敛速率为$mathcal {O}left ({{frac {1}{sqrt {nT}}}}right)$，与最先进的技术相匹配。此外，我们还解决了功率控制问题，将其分解为发送和接收子问题，以加速De-RPOTA算法的收敛。我们还为我们的无线计算方法提供了可量化的隐私保证。有趣的是，我们的研究结果表明，网络噪声实际上可以增强聚合信息的隐私性，无线计算为个人更新提供了额外的安全性。综合实验验证证实了De-RPOTA在通信资源有限环境下的有效性。具体来说，CIFAR-10数据集的结果揭示了近30个% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.
{"title":"De-RPOTA: Decentralized Learning With Resource Adaptation and Privacy Preservation Through Over-the-Air Computation","authors":"Jing Qiao;Shikun Shen;Shuzhen Chen;Xiao Zhang;Tian Lan;Xiuzhen Cheng;Dongxiao Yu","doi":"10.1109/TNET.2024.3438462","DOIUrl":"https://doi.org/10.1109/TNET.2024.3438462","url":null,"abstract":"In this paper, we propose De-RPOTA, a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a contraction region defined by a scaled errors version. Remarkably, De-RPOTA achieves a convergence rate of \u0000<inline-formula> <tex-math>$mathcal {O}left ({{frac {1}{sqrt {nT}}}}right)$ </tex-math></inline-formula>\u0000 in scenarios devoid of errors, matching the state-of-the-arts. Additionally, we tackle a power control challenge, breaking it down into transmitter and receiver sub-problems to hasten the De-RPOTA algorithm’s convergence. We also offer a quantifiable privacy assurance for our over-the-air computation methodology. Intriguingly, our findings suggest that network noise can actually strengthen the privacy of aggregated information, with over-the-air computation providing extra security for individual updates. Comprehensive experimental validation confirms De-RPOTA’s efficacy in communication resources limited environments. Specifically, the results on the CIFAR-10 dataset reveal nearly 30% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"4931-4943"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1109/TNET.2024.3453759
François Michel;Olivier Bonaventure
A growing number of Internet applications require low latency. Unfortunately, most of these applications cannot use the rich features of the QUIC protocol since it only uses retransmissions to cope with packet losses. We propose, implement and evaluate QUIRL, a revisit of the QUIC loss recovery mechanism. QUIRL relies on Forward Erasure Correction (FEC) only if it is needed by the application’s latency requirements and uses classical retransmissions otherwise. We implement QUIRL and evaluate its performance for real-time video and HTTP/3. Compared to previous works adding FEC to QUIC, QUIRL is the first to be evaluated with and obtain significant performance improvements for popular applications over real lossy networks. Our evaluation shows that for video QUIRL improves the video quality while meeting strict delay requirements. For HTTP/3 transfers, QUIRL efficiently reduces the tail latency when packet losses occur without causing harm when there are no losses. We confirm these results using emulation over a wide ranges of bandwidth, delays and loss scenarios. We release our QUIRL implementation to encourage other researchers and industry to explore in more details the use of FEC in QUIC.
{"title":"QUIRL: Flexible QUIC Loss Recovery for Low Latency Applications","authors":"François Michel;Olivier Bonaventure","doi":"10.1109/TNET.2024.3453759","DOIUrl":"https://doi.org/10.1109/TNET.2024.3453759","url":null,"abstract":"A growing number of Internet applications require low latency. Unfortunately, most of these applications cannot use the rich features of the QUIC protocol since it only uses retransmissions to cope with packet losses. We propose, implement and evaluate QUIRL, a revisit of the QUIC loss recovery mechanism. QUIRL relies on Forward Erasure Correction (FEC) only if it is needed by the application’s latency requirements and uses classical retransmissions otherwise. We implement QUIRL and evaluate its performance for real-time video and HTTP/3. Compared to previous works adding FEC to QUIC, QUIRL is the first to be evaluated with and obtain significant performance improvements for popular applications over real lossy networks. Our evaluation shows that for video QUIRL improves the video quality while meeting strict delay requirements. For HTTP/3 transfers, QUIRL efficiently reduces the tail latency when packet losses occur without causing harm when there are no losses. We confirm these results using emulation over a wide ranges of bandwidth, delays and loss scenarios. We release our QUIRL implementation to encourage other researchers and industry to explore in more details the use of FEC in QUIC.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5204-5215"},"PeriodicalIF":3.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1109/TNET.2024.3463609
Zhiyuan Wang;Xin Lai;Shan Zhang;Qingkai Meng;Hongbin Luo
Low-Earth-Orbit (LEO) satellite constellations are becoming the necessary infrastructure in the future. However, the secure operation of LEO constellations is faced with severe risks. Specifically, LEO satellites are constantly orbiting and their channel interfaces are open. The adversary in hostile regions can leverage the global footprint to inject malicious traffic via access satellites. That is, LEO satellites are susceptible to physical and cyber attacks. Therefore, access authentication regarding terrestrial users (TUs) is crucial to ensure the secure operation of LEO constellations. The traditional on-orbit authentication frameworks usually presume that satellites are reliable and mutually trusted, thus one could rely on access satellites to perform authentication. In practice, however, physical and cyber attacks could bring down the satellites (causing fail-stop fault) or even hijack the satellites (causing Byzantine fault). This fact requires that the access authentication framework installed on LEO constellations should be fault-tolerant. In this paper, we aim to achieve Byzantine fault tolerance in access authentication for LEO satellite networks by properly integrating PBFT consensus protocol with traditional on-orbit authentication. Based on the topology characteristics of LEO constellations, we analytically derive the consensus probability, authentication accuracy, and communication overhead under PBFT-based authentication. To reduce the communication overhead, we propose to partition the constellation into multiple consensus groups, and devise a hierarchical PBFT (HPBFT) protocol. Simulation results based on Starlink Shell-I constellation indicate that HPBFT-based authentication could reduce the communication overhead (by an order of magnitude) and maintain almost the same authentication accuracy compared to PBFT-based authentication.
{"title":"Enabling Byzantine Fault Tolerance in Access Authentication for Mega-Constellations","authors":"Zhiyuan Wang;Xin Lai;Shan Zhang;Qingkai Meng;Hongbin Luo","doi":"10.1109/TNET.2024.3463609","DOIUrl":"https://doi.org/10.1109/TNET.2024.3463609","url":null,"abstract":"Low-Earth-Orbit (LEO) satellite constellations are becoming the necessary infrastructure in the future. However, the secure operation of LEO constellations is faced with severe risks. Specifically, LEO satellites are constantly orbiting and their channel interfaces are open. The adversary in hostile regions can leverage the global footprint to inject malicious traffic via access satellites. That is, LEO satellites are susceptible to physical and cyber attacks. Therefore, access authentication regarding terrestrial users (TUs) is crucial to ensure the secure operation of LEO constellations. The traditional on-orbit authentication frameworks usually presume that satellites are reliable and mutually trusted, thus one could rely on access satellites to perform authentication. In practice, however, physical and cyber attacks could bring down the satellites (causing fail-stop fault) or even hijack the satellites (causing Byzantine fault). This fact requires that the access authentication framework installed on LEO constellations should be fault-tolerant. In this paper, we aim to achieve Byzantine fault tolerance in access authentication for LEO satellite networks by properly integrating PBFT consensus protocol with traditional on-orbit authentication. Based on the topology characteristics of LEO constellations, we analytically derive the consensus probability, authentication accuracy, and communication overhead under PBFT-based authentication. To reduce the communication overhead, we propose to partition the constellation into multiple consensus groups, and devise a hierarchical PBFT (HPBFT) protocol. Simulation results based on Starlink Shell-I constellation indicate that HPBFT-based authentication could reduce the communication overhead (by an order of magnitude) and maintain almost the same authentication accuracy compared to PBFT-based authentication.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5341-5355"},"PeriodicalIF":3.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of communication links in wireless networks. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. Energy-intensive blockchain functions can quickly drain the power of nodes, thus degrading consensus performance. Fortunately, a symbiotic radio (SR) system enabled by cognitive backscatter communications can solve the above problems. In SR, the secondary transmitter (STx) transmits messages over the radio frequency (RF) signal emitted from a primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. Such an approach is useful for almost all vote-based CMs, such as the Practical Byzantine Fault-tolerant (PBFT)-like and the RAFT-like CMs. This paper proposes symbiotic blockchain consensus (SBC) by transforming 6 PBFT-like and 4 RAFT-like state-of-the-art (SOTA) CMs to demonstrate universality. These new CMs will benefit from mutualistic transmission relationships in SR, making full use of the limited spectrum resources in WBN. Simulation results show that SBC can increase the consensus success rate of PBFT-like and RAFT- like by 54.1% and 5.8%, respectively, and reduce energy consumption by 9.2% and 23.7%, respectively.
{"title":"Symbiotic Blockchain Consensus: Cognitive Backscatter Communications-Enabled Wireless Blockchain Consensus","authors":"Haoxiang Luo;Qianqian Zhang;Gang Sun;Hongfang Yu;Dusit Niyato","doi":"10.1109/TNET.2024.3462539","DOIUrl":"https://doi.org/10.1109/TNET.2024.3462539","url":null,"abstract":"The wireless blockchain network (WBN) concept, born from the blockchain deployed in wireless networks, has appealed to many network scenarios. Blockchain consensus mechanisms (CMs) are key to enabling nodes in a wireless network to achieve consistency without any trusted entity. However, consensus reliability will be seriously affected by the instability of communication links in wireless networks. Meanwhile, it is difficult for nodes in wireless scenarios to obtain a timely energy supply. Energy-intensive blockchain functions can quickly drain the power of nodes, thus degrading consensus performance. Fortunately, a symbiotic radio (SR) system enabled by cognitive backscatter communications can solve the above problems. In SR, the secondary transmitter (STx) transmits messages over the radio frequency (RF) signal emitted from a primary transmitter (PTx) with extremely low energy consumption, and the STx can provide multipath gain to the PTx in return. Such an approach is useful for almost all vote-based CMs, such as the Practical Byzantine Fault-tolerant (PBFT)-like and the RAFT-like CMs. This paper proposes symbiotic blockchain consensus (SBC) by transforming 6 PBFT-like and 4 RAFT-like state-of-the-art (SOTA) CMs to demonstrate universality. These new CMs will benefit from mutualistic transmission relationships in SR, making full use of the limited spectrum resources in WBN. Simulation results show that SBC can increase the consensus success rate of PBFT-like and RAFT- like by 54.1% and 5.8%, respectively, and reduce energy consumption by 9.2% and 23.7%, respectively.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5372-5387"},"PeriodicalIF":3.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1109/TNET.2024.3463186
Wei Xing;Xudong Zhao
In this paper, a dynamic zero-sum game is formulated to describe the power decision-making process of the sensor/relay and the DoS attacker in cyber-physical systems. The sensor and the relay cooperate with each other to transmit the state estimation to the remote estimator, when the attacker, on the contrary, aims to disturb the wireless communication channels strategically for deterioration of the system performance but can do this taking into account its limited energy. Different from conventional battery-powered nodes, the sensor and the relay can harvest energy from the external environment and store it in their batteries for data transmission. We model the external environment state as a Markov chain to overcome the randomness of the harvested energy. In addition, to tackle the computation complexity of the Nash equilibrium (NE), we restrict our attention to a special case, i.e., the DoS attacker can only launch interference on one of the two communication channels over an infinite time horizon, and provide the corresponding NE strategy of the game using the Markov decision process and the multi-agent reinforcement learning algorithm. Finally, simulation examples are given to illustrate the theoretical findings of the paper.
本文提出了一种动态零和博弈，以描述网络物理系统中传感器/中继器和 DoS 攻击者的权力决策过程。传感器和中继器相互合作，向远程估计器传输状态估计，而攻击者则旨在战略性地干扰无线通信信道，从而导致系统性能下降，但在这样做时要考虑到其有限的能量。与传统的电池供电节点不同，传感器和中继器可以从外部环境中获取能量，并将其储存在电池中用于数据传输。我们将外部环境状态建模为马尔可夫链，以克服能量采集的随机性。此外，为了解决纳什均衡（NE）的计算复杂性问题，我们将注意力限制在一种特殊情况上，即 DoS 攻击者只能在无限时间范围内对两个通信信道中的一个信道发起干扰，并利用马尔可夫决策过程和多代理强化学习算法提供了相应的博弈 NE 策略。最后，本文给出了仿真实例，以说明本文的理论发现。
{"title":"Transmission Scheduling for Remote State Estimation in CPSs With Two-Hop Networks Subject to DoS Attacks","authors":"Wei Xing;Xudong Zhao","doi":"10.1109/TNET.2024.3463186","DOIUrl":"https://doi.org/10.1109/TNET.2024.3463186","url":null,"abstract":"In this paper, a dynamic zero-sum game is formulated to describe the power decision-making process of the sensor/relay and the DoS attacker in cyber-physical systems. The sensor and the relay cooperate with each other to transmit the state estimation to the remote estimator, when the attacker, on the contrary, aims to disturb the wireless communication channels strategically for deterioration of the system performance but can do this taking into account its limited energy. Different from conventional battery-powered nodes, the sensor and the relay can harvest energy from the external environment and store it in their batteries for data transmission. We model the external environment state as a Markov chain to overcome the randomness of the harvested energy. In addition, to tackle the computation complexity of the Nash equilibrium (NE), we restrict our attention to a special case, i.e., the DoS attacker can only launch interference on one of the two communication channels over an infinite time horizon, and provide the corresponding NE strategy of the game using the Markov decision process and the multi-agent reinforcement learning algorithm. Finally, simulation examples are given to illustrate the theoretical findings of the paper.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5388-5398"},"PeriodicalIF":3.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/TNET.2024.3464540
Ronghao Gao;Yunlai Xu;Han Li;Qinyu Zhang;Zhihua Yang
In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called Semantic Adaptive Coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware Semantic Adaptive Coding Hybrid ARQ (SAC-HARQ) mechanism and a Semantic Coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source Coding (DL-JSCC), Deep learning-based Semantic Communication system (DeepSC), and Semantic Coding HARQ (SCHARQ), respectively.
{"title":"Semantic-Aware Jointed Coding and Routing Design in Large-Scale Satellite Networks: A Deep Learning Approach","authors":"Ronghao Gao;Yunlai Xu;Han Li;Qinyu Zhang;Zhihua Yang","doi":"10.1109/TNET.2024.3464540","DOIUrl":"https://doi.org/10.1109/TNET.2024.3464540","url":null,"abstract":"In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called Semantic Adaptive Coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware Semantic Adaptive Coding Hybrid ARQ (SAC-HARQ) mechanism and a Semantic Coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source Coding (DL-JSCC), Deep learning-based Semantic Communication system (DeepSC), and Semantic Coding HARQ (SCHARQ), respectively.","PeriodicalId":13443,"journal":{"name":"IEEE/ACM Transactions on Networking","volume":"32 6","pages":"5415-5429"},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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