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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Guo, Xinxiang Mu, Yifen Yang, Xiaoguang University of Chinese Academy of Sciences The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences Beijing100190 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences The Key Laboratory of Systems and Control Beijing100190 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences The Key Laboratory of Management Decision and Information System Beijing100190 China

ISBN: (纸本)9798350316339

In this paper, we consider the n × n two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. The theoretical analysis on the dynamics of such game system is still rare, which is however of promising interests. We investigate the dynamics of such Hedge-myopic system by defining a metric Q(xt), which measures the distance between the stage strategy xt and Nash Equilibrium (NE) strategy of player X. We analyze the trend of Q(xt) and prove that it is bounded and can only take finite values on the evolutionary path when payoffs are all rational numbers and the game has an interior NE. Based on this, we prove that the stage strategy sequence of both players are periodic after finite stages and the time-averaged strategy of player Y within one period is an exact NE strategy. © 2024 IEEE.

关键词： consensus algorithm

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Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection 18

Adapting to Evasive Tactics through Resilient Adversarial Ma...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Krishna, G. Bala Sravan Kumar, G. Ramachandra, Mummadi Sampurnima Pattem, K. Rani, D. Sandhya Kakarla, Geeta Cvr College of Engineering Department of Computer Science Engg. Rangareddy Ibrahimpatnam India Cvr College of Engineering Department of Computer Science Engg.-Cyber Security Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Hyderabad India Sreenidhi Institute of Science and Technology Department of Computer Science Engg. Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Block chain Blockchains Challenges of Block chain consensus algorithm Data privacy Digital signatures Distributed ledger Faces Organizations Smart contracts

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Blockchain for IoT-Based Healthcare: Background, consensus, Platforms, and Use Cases

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IEEE SYSTEMS JOURNAL 2021年第1期15卷 85-94页

作者： Ray, Partha Pratim Dash, Dinesh Salah, Khaled Kumar, Neeraj Sikkim Univ Dept Comp Applicat Gangtok 737102 India Natl Inst Technol Patna Dept Comp Sci & Engn Patna 800005 Bihar India Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi 127788 U Arab Emirates Thapar Univ Dept Comp Sci & Engn Patiala 147004 Punjab India King Abdulaziz Univ Jeddah Saudi Arabia Asia Univ Dept Software & Informat Engn Taichung Taiwan

Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth, that can be used for storing, accessing, and managing of e-healthcare data.

关键词： Blockchain Medical services consensus algorithm Internet of Things Computer architecture Organizations Computer science Blockchain e-health healthcare Internet of Things (IoT) IoT data

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Voting-Based Decentralized consensus Design for Improving the Efficiency and Security of Consortium Blockchain

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IEEE INTERNET OF THINGS JOURNAL 2021年第8期8卷 6257-6272页

作者： Sun, Gang Dai, Miao Sun, Jian Yu, Hongfang Univ Elect Sci & Technol China Key Lab Opt Fiber Sensing & Commun Minist Educ Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Agile & Intelligent Comp Key Lab Sichuan Prov Chengdu 611731 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability, and the ability to be tamper proof. These advantages help blockchain become a better option for fields, such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks, such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks, and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this article, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.

关键词： Blockchain consensus algorithm Security Internet of Things Sun Throughput Peer-to-peer computing consensus algorithm consortium blockchain deposit reputation verifiable random function (VRF) voting%

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A Comprehensive Review of Blockchain consensus Mechanisms

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IEEE ACCESS 2021年 9卷 43620-43652页

作者： Lashkari, Bahareh Musilek, Petr Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Univ Hradec Kralove Dept Appl Cybernet Hradec Kralove 50003 Czech Republic

Since the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. This article brings a comprehensive review of the fundamentals of distributed ledger and its variants. Analyzing 185 publications, ranging from academic journals to industry websites, it provides a comparative analysis of 130 consensus algorithms using a novel architectural classification. The distribution of the reviewed algorithms is analyzed in terms of the proposed classification and different application domains, along with the applicability of each class among the top 10 platforms in the most prominent blockchain application domains. Additional conclusions are drawn from the evolution of consensus mechanisms, and the analysis concludes envisaging future prospects for consensus as an important part of distributed ledger technology.

关键词： Distributed ledger Blockchain consensus algorithm Scalability Licenses Distributed databases Banking Blockchain distributed ledger technology consensus mechanisms cryptocurrency

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Proof of Previous Transactions (PoPT): An Efficient Approach to consensus for JCLedger

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2415-2424页

作者： Fu Xiang Wang Huaimin Shi Peichang Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

JCLedger is a BlockChain-based distributed ledger for JointCloud that can improve the reliability and convenience of cloud resource exchanges by empowering cooperation among multiple clouds. The biggest challenge for the implementation of JCLedger is the approach to consensus. The existing consensus algorithms for the public BlockChain, such as proof of work (PoW) or proof of stake (PoS) does not apply to the JointCloud, because they require a massive computing power with a low throughput or monopoly risk. In this paper, we propose a practical Byzantine-fault-tolerance (PBFT)-based consensus algorithm called proof of previous transactions (PoPT), in which the accountants are selected by a specific hash function from a certain number of candidates. The candidates are chosen according to the users' participation in JointCloud, and only candidates that join the PBFT-based consensus process instead of all users. We also propose a new BlockChain structure for parallel accounting to improve the scalability of JCLedger, and a consistent hashing algorithm is used to assign the transactions to different accountants. Simulation experiments show that the PoPT can shield the unequal computing power of the users to provide them equal accounting opportunities, and the parallel accounting can handle the massive and high-frequency transactions in JointCloud more efficiently.

关键词： Blockchain consensus algorithm Peer-to-peer computing Cloud computing Bitcoin Scalability BlockChain consensus algorithm JointCloud parallel accounting

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consensus-Based Distributed Quickest Detection of Attacks With Unknown Parameters

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第3期67卷 1864-1885页

作者： Zhang, Jiangfan Wang, Xiaodong Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Columbia Univ Dept Elect Engn New York NY 10027 USA

Sequential attack detection in a distributed sensor network is considered, where each sensor successively produces one-bit quantized samples of a desired deterministic scalar parameter corrupted by additive noise. The unknown parameters in the pre-attack and post-attack models, namely the desired parameter to be estimated and the injected malicious data at the attacked sensors pose a significant challenge for designing a computationally efficient scheme for each sensor to detect the occurrence of attacks by only using local communication with neighboring sensors. The generalized Cumulative Sum (GCUSUM) algorithm is considered, which replaces the unknown parameters with their maximum likelihood estimates in the CUSUM test statistic. For the problem under consideration, a sufficient condition is provided under which the expected false alarm period of the GCUSUM can be guaranteed to be larger than any given value. Next, we consider the distributed implementation of the GCUSUM. We first propose an alternative test statistic which is asymptotically equivalent to that of GCUSUM. Then based on the proposed alternative test statistic and running consensus algorithms, we propose a distributed approximate GCUSUM algorithm which significantly reduce the prohibitively high computational complexity of the centralized GCUSUM. Numerical results show that the proposed distributed approximate GCUSUM algorithm can provide a performance that is comparable to the centralized GCUSUM.

关键词： Distributed databases consensus algorithm Quantization (signal) Data models Security Approximation algorithms Power demand Cyberattack generalized CUSUM running consensus algorithms distributed quickest detection one-bit quantization

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Finite Rate Distributed Weight-Balancing and Average consensus Over Digraphs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4530-4545页

作者： Lee, Chang-Shen Michelusi, Nicolo Scutari, Gesualdo Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA

This article proposes the first distributed algorithm that solves the weight-balancing problem using only finite rate and simplex communications among nodes, compliant with the directed nature of the graph edges. It is proved that the algorithm converges to a weight-balanced solution at sublinear rate. The analysis builds upon a new metric inspired by positional system representations, which characterizes the dynamics of information exchange over the network, and on a novel step-size rule. Building on this result, a novel distributed algorithm is proposed that solves the average consensus problem over digraphs, using, at each timeslot, finite rate simplex communications between adjacent nodes-some bits for the weight-balancing problem and others for the average consensus. Convergence of the proposed quantized consensus algorithm to the average of the node's unquantized initial values is established, both almost surely and in the moment generating function of the error;and a sublinear convergence rate is proved for sufficiently large step-sizes. Numerical results validate our theoretical findings.

关键词： Quantization (signal) Convergence Probabilistic logic Distributed algorithms consensus algorithm Measurement Buildings Data rate directed graph distributed average consensus distributed weight-balancing quantization

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Scalable Average consensus with Compressed Communications

Scalable Average Consensus with Compressed Communications

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American Control Conference (ACC)

作者： Toghani, Mohammad Taha Uribe, Cesar A. Rice Univ Dept Elect & Comp Engn 6100 Main St Houston TX 77005 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

We propose a new decentralized average consensus algorithm with compressed communication that scales linearly with the network size n. We prove that the proposed method converges to the average of the initial values held locally by the agents of a network when agents are allowed to communicate with compressed messages. The proposed algorithm works for a broad class of compression operators (possibly biased), where agents interact over arbitrary static, undirected, and connected networks. We further present numerical experiments that confirm our theoretical results and illustrate the scalability and communication efficiency of our algorithm.

关键词： Scalability consensus algorithm Optimization

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Term Dictionary Automatic Extraction algorithm based on BiLSTM 1

Term Dictionary Automatic Extraction Algorithm based on BiLS...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Liu, Fangting Bohai University College of Foreign Languages Jinzhou China

ISBN: (纸本)9798331529246

With the increasing importance of machine translation research in the era of artificial intelligence, which are widely used in translation practice, contrastive analysis, language learning and other fields. As a basic resource, terminology dictionary plays an important role in cross-language natural language processing. To solve the problems in the field of cross-language natural language processing, this paper takes parallel corpus as the basis, studies the automatic extraction algorithm of term dictionary based on BiLSTM, combines BiLSTM, HMM, Viterbi and T-test and other methods to build a mathematical model of automatic extraction of term dictionary based on parallel corpus. Solve key problems such as sentence alignment, part-of-speech tagging and Chinese word segmentation. Through simulation experiments, the BiLSTM algorithm model in this paper is compared with RNN, CRF, LSTM and other network algorithm models, and the superiority of BiLSTM algorithm model is verified. © 2025 IEEE.

关键词： consensus algorithm

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