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Relevant bibliographies by topics / Byzantine attack

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Academic literature on the topic 'Byzantine attack'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Byzantine attack"

1

Yang, Xiong, Wang, and Zhang. "Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks." Sensors 19, no. 15 (2019): 3436. http://dx.doi.org/10.3390/s19153436.

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Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer–Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion

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Pashkin, Nikolai G. "Aims of the Byzantine Attack on Gallipoli in 1410." Античная древность и средние века 48 (2020): 156–71. http://dx.doi.org/10.15826/adsv.2020.48.010.

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This research suggests an interpretation of the reasons behind the Byzantine attack on the Turkish fort of Gallipoli in spring 1410. The citadel that controlled Dardanelles was attacked by a squadron of eight ships. This operation is considered not successful. However, there are reasons to consider that initially Greeks did not plan to take the town. The search for the proofs should be in the sphere of international relations. In the period in question, the Byzantine policy was influenced by contradictions between Venice and Hungarian king Sigismund of Luxembourg concerning Dalmatia. Their int

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Nasir, Muhammad, and Ananda Maiti. "Adaptive Sliding Mode Resilient Control of Multi-Robot Systems with a Leader–Follower Model under Byzantine Attacks in the Context of the Industrial Internet of Things." Machines 12, no. 3 (2024): 205. http://dx.doi.org/10.3390/machines12030205.

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In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique f

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Liu, Yuchen, Chen Chen, Lingjuan Lyu, Yaochu Jin, and Gang Chen. "Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 19024–32. https://doi.org/10.1609/aaai.v39i18.34094.

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Federated Learning (FL) is notorious for its vulnerability to Byzantine attacks. Most current Byzantine defenses share a common inductive bias: among all the gradients, the densely distributed ones are more likely to be honest. However, such a bias is a poison to Byzantine robustness due to a newly discovered phenomenon in this paper -- gradient skew. We discover that a group of densely distributed honest gradients skew away from the optimal gradient (the average of honest gradients) due to heterogeneous data. This gradient skew phenomenon allows Byzantine gradients to hide within the densely

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Wang, Xiaoxue, Hongqi Zhang, Anas Bilal, Haixia Long, and Xiaowen Liu. "WGM-dSAGA: Federated Learning Strategies with Byzantine Robustness Based on Weighted Geometric Median." Electronics 12, no. 5 (2023): 1190. http://dx.doi.org/10.3390/electronics12051190.

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Federated learning techniques accomplish federated modeling and share global models without sharing data. Federated learning offers a good answer to complex data and privacy security issues. Although there are many ways to target federated learning, Byzantine attacks are the ones we concentrate on. Byzantine attacks primarily impede learning by tampering with the local model parameters provided by a client to the master node throughout the federation learning process, leading to a final global model that diverges from the optimal solution. To address this problem, we combine aggregation rules

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Amir, Y., B. Coan, J. Kirsch, and J. Lane. "Prime: Byzantine Replication under Attack." IEEE Transactions on Dependable and Secure Computing 8, no. 4 (2011): 564–77. http://dx.doi.org/10.1109/tdsc.2010.70.

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Medhi, Jishu K., Rui Liu, Qianlong Wang, and Xuhui Chen. "Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks." Information 14, no. 11 (2023): 623. http://dx.doi.org/10.3390/info14110623.

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Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degr

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Wan, Fangyi, Ting Ma, Yi Hua, Bin Liao, and Xinlin Qing. "Secure distributed estimation under Byzantine attack and manipulation attack." Engineering Applications of Artificial Intelligence 116 (November 2022): 105384. http://dx.doi.org/10.1016/j.engappai.2022.105384.

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Wang, Rui, Xingkai Wang, Huanhuan Chen, et al. "MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-preserving Byzantine-robust Clustering." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 3 (2024): 1–41. https://doi.org/10.1145/3700422.

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Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ (ICML '20) do not make such an honest majority assumption but can only be applied to scenarios where the server is provided with an auxiliary dataset used to filter malicious updates. FLAME (USENIX '22) and EIFFeL (CCS '22) maintain the semi-honest majority assumption to guarantee robustness and the confidentiality of

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Wang, Rui, Xingkai Wang, Huanhuan Chen, et al. "MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-preserving Byzantine-robust Clustering." ACM SIGMETRICS Performance Evaluation Review 53, no. 1 (2025): 25–27. https://doi.org/10.1145/3744970.3727296.

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Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ (ICML '20) do not make such an honest majority assumption but can only be applied to scenarios where the server is provided with an auxiliary dataset used to filter malicious updates. FLAME (USENIX '22) and EIFFeL (CCS '22) maintain the semi-honest majority assumption to guarantee robustness and the confidentiality of

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Dissertations / Theses on the topic "Byzantine attack"

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Sironen, Erkki. "The late Roman and early Byzantine inscriptions of Athens and Attica : an edition with appendices on scripts, sepulchral formulae and occupations /." Helsinki : Hakapaino Oy, 1997. http://bibpurl.oclc.org/web/25751.

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Alam, Mohammad Rafiqul. "Detecting wormhole and Byzantine attacks in mobile ad hoc networks." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/1701.

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The recent advancements in the wireless technology and their wide-spread utilization have made tremendous enhancements in productivity in the corporate and industrial sectors. However, these recent progresses have also introduced new security vulnerabilities. Since the wireless shared medium is completely exposed to outsiders, it is susceptible to attacks that could target any of the OSI layers in the network stack. For example, jamming of the physical layer, disruption of the medium access control (MAC) layer coordination packets, attacks against the routing infrastructure, targeted attacks o

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Tzavella, Elissavet. "Urban and rural landscape in early and middle Byzantine Attica (4th-12th c. AD)." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4321/.

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The present study synthesiszes archaeological and historical evidence concerning Attica (Greece), the hinterland of Athens, in the Early and Middle Byzantine periods (4th-12th c.). Although the Byzantine monuments of Attica have been thoroughly studied, no coherent picture of how these relate to broader patterns of occupation and land usage has thus far been presented. In the main, the period under discussion is generally interpreted in three ways: Regarding Late Antiquity, research has often focused on the transition from paganism to Christianity, and to the characterisation of Attica as a ‘s

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Wang, Qianlong. "Blockchain-Empowered Secure Machine Learning and Applications." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1625183576139299.

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Han, Kai. "Scheduling Distributed Real-Time Tasks in Unreliable and Untrustworthy Systems." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26917.

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In this dissertation, we consider scheduling distributed soft real-time tasks in unreliable (e.g., those with arbitrary node and network failures) and untrustworthy systems (e.g., those with Byzantine node behaviors). We present a distributed real-time scheduling algorithm called Gamma. Gamma considers a distributed (i.e., multi-node) task model where tasks are subject to Time/Utility Function (or TUF) end-to-end time constraints, and the scheduling optimality criterion of maximizing the total accrued utility. The algorithm makes three novel contributions. First, Gamma uses gossip for reliably

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KALLAS, KASSEM. "A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks." Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1005735.

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Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a genera

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Chen, Ho-Chun, and 陳和均. "The Study of Byzantine Attack in Large Scale Wireless Sensor Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/n2y87u.

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碩士<br>國立臺灣大學<br>電信工程學研究所<br>106<br>In this thesis, we study the problem of Byzantine attacks in large scale wireless sensor networks (WSNs). WSN is a critical technology in the future Internet of Things era, supporting various applications such as smart home, environmental monitoring, Internet of vehicles, etc. A WSN generally consists of some sensors and a fusion center (FC). Each sensor makes an observation about a phenomenon of interest, and transmits local message to the FC, who then makes a final decision based on those messages. Due to the exposed nature of the sensors and the characteri

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Cheng, Sheng-Hong, and 鄭勝鴻. "Defense and Detection of Sybil Attack Using Byzantine Agreement Algorithm in Mobile Ad Hoc Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/24pgmr.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>106<br>The topology of mobile ad hoc networks (MANET) is dynamic, can be quickly configured, and lacks features such as wireless APs and adapters. Due to these benefits, a MANET system is able to be applied to specific situations, such as search and rescue operations, military use or emergency operations. Based on the routing protocol, node connections provide information to each other and can jointly establish a complete transmission path independently. But security is a major issue in MANET systems. Because MANET system are open and perform on a non-centralized inf

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(9154928), Aritra Mitra. "New Approaches to Distributed State Estimation, Inference and Learning with Extensions to Byzantine-Resilience." Thesis, 2020.

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<div>In this thesis, we focus on the problem of estimating an unknown quantity of interest, when the information required to do so is dispersed over a network of agents. In particular, each agent in the network receives sequential observations generated by the unknown quantity, and the collective goal of the network is to eventually learn this quantity by means of appropriately crafted information diffusion rules. The abstraction described above can be used to model a variety of problems ranging from environmental monitoring of a dynamical process using autonomous robot teams, to statistical

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Mapunya, Sekgoari Semaka. "The design and the implementation of the byzantine attack mitigation scheme in cognitive radio ad hoc networks." Thesis, 2019. http://hdl.handle.net/10386/3137.

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Thesis ( M.Sc. (Computer Science)) -- University of Limpopo, 2019<br>Cognitive radio network, which enables dynamic spectrum access, addresses the shortage of radio spectrum caused by ever-increasing wireless technology. This allows efficient utilisation of underutilised licenced spectrum by allowing cognitive radios to opportunistically make use of available licenced spectrum. Cognitive radios (CR), also known as secondary users, must constantly sense the spectrum band to avoid interfering with the transmission of the licenced users, known as primary users. Cognitive radios must cooperate in

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Books on the topic "Byzantine attack"

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Yarbro, Chelsea Quinn. A flame in Byzantium. Tor, 1987.

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Yarbro, Chelsea Quinn. A Flame in Byzantium. Tom Doherty Assoc Llc, 1988.

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Los dorismos del Corpus Bucolicorum. A.M. Hakkert, 1990.

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Book chapters on the topic "Byzantine attack"

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Pooja, A. Saraswathi, Rampa Theresa, Satya Govind, and Anuja Aloy Mary. "Detection of Byzantine Attack in Cognitive Radio Network." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5029-4_37.

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He, Xiaofan, and Huaiyu Dai. "Case Study II: HMM-Based Byzantine Attack Detection." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75868-8_5.

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He, Xiaofan, and Huaiyu Dai. "Case Study III: CFC-Based Byzantine Attack Detection." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75868-8_6.

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Zhang, Linyuan, Guoru Ding, and Qihui Wu. "Byzantine Attack and Defense in Wireless Data Fusion." In Encyclopedia of Wireless Networks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_301.

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Zhang, Linyuan, Guoru Ding, and Qihui Wu. "Byzantine Attack and Defense in Wireless Data Fusion." In Encyclopedia of Wireless Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_301-1.

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Hu, Chenyu, Yuhan Liu, Mingyue Zhang, and Zheng Yang. "Defense-Guided Adaptive Attack on Byzantine-Robust Federated Learning." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-0151-6_7.

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Bing, Yuchen, Long Wang, and Zesheng Chen. "A Spectrum Sensing Method for UAV Swarms Under Byzantine Attack." In Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9492-9_172.

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Guo, Lan, Weifeng Chen, Yang Cong, and Xuechun Yan. "A Robust MOR-Based Secure Fusion Strategy Against Byzantine Attack in Cooperative Spectrum Sensing." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3581-9_6.

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Hancock, James F. "Spice trade in the dark ages of Europe." In Spices, scents and silk: catalysts of world trade. CABI, 2021. http://dx.doi.org/10.1079/9781789249743.0012.

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Abstract This chapter narrates the state of world trade during the fall of the Western Roman Empire under waves of Germanic tribe movements during the 'Völkerwanderung' or Migration Period. It contains nine subchapters that are about the collapse of the Western Roman Empire, spice use in Europe during the dark ages, the level of western trade in the early medieval age, Mediterranean trade in the early medieval period, early medieval trade in Europe, the Radhanites: medieval tycoons, the rise of the Gotlanders, Rus' trade with the Muslims and Byzantines through Khazaria, and lastly, Rus' attack

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Fiat, Amos, Jared Saia, and Maxwell Young. "Making Chord Robust to Byzantine Attacks." In Algorithms – ESA 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11561071_71.

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Conference papers on the topic "Byzantine attack"

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Alyahya, Aren, David Tipper, and Amy Babay. "Availability Analysis of Network-Attack-Resilient Byzantine Fault Tolerant Systems." In 2024 43rd International Symposium on Reliable Distributed Systems (SRDS). IEEE, 2024. https://doi.org/10.1109/srds64841.2024.00014.

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Shen, Gang, Zhiqiang Fu, and Shaohua Liu. "EFLSS: An Efficient Federated Learning Security Scheme in Byzantine Attack Scenarios." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00148.

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Dewi, Ayu Oktaviani, Dyonisius Dony Ariananda, and Sigit Basuki Wibowo. "Byzantine Attack Identification using Graph Signal Processing in Cooperative Spectrum Sensing." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862364.

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Alyahya, Aren. "PhD Forum: Challenges of Availability and Cost Assessments for Network-Attack-Resilient Byzantine Systems." In 2024 43rd International Symposium on Reliable Distributed Systems (SRDS). IEEE, 2024. https://doi.org/10.1109/srds64841.2024.00040.

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D, Gopinath, N. Karthikeyan, Yashoda M B, and K. K. Savitha. "Machine Learning Based Secured Reliable Routing Protocol to Predict and Prevent Byzantine Attack in MANET." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10690060.

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Jahan Mouri, Israt, Muhammad Ridowant, and Muhammad Abdullah Adnan. "XGDFed: Exposing Vulnerability in Byzantine Robust Federated Binary Classifiers with Novel Aggregation-Agnostic Model Poisoning Attack." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00138.

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Li, Yunlong, Jun Wu, Zhaoyang Qiu, et al. "Cross-Validation-Based SVM for Cooperative Spectrum Sensing Against Probabilistic Byzantine Attack in Cognitive Wireless Sensor Networks." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069740.

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Lou, YiFan, Jun Wu, Yunlong Li, Jiabao Yu, Zhiguang Yang, and Ye Jin. "Parameter Selection of Support Vector Machines for Cooperative Spectrum Sensing Against Probabilistic Byzantine Attack in Cognitive Wireless Sensor Networks." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069750.

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Yuan, Liwei, Hideaki Ishii, and Yaonan Wang. "Robust Average Consensus under Byzantine Attacks." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886555.

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Pavloff, Ulysse, Yackolley Amoussou-Guenou, and Sara Tucci-Piergiovanni. "Byzantine Attacks Exploiting Penalties in Ethereum PoS." In 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). IEEE, 2024. http://dx.doi.org/10.1109/dsn58291.2024.00020.

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