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研究生: 吳旻豪
Wu, Min-Hao
論文名稱: 分析多層感知器應用於區塊鏈共識機制之效能
Analysis on the performance of blockchain consensus mechanism by using multilayer perceptrons
指導教授: 林清河
Lin, Chin-Ho
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 52
中文關鍵詞: 區塊鏈共識機制雜湊函式機器學習多層感知器
外文關鍵詞: Blockchain, Consensus mechanism, Hash function, Machine learning, Multilayer perceptron
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  • 區塊鏈概念是來自2008年中本聰發表的論文,全新的去中心化區塊鏈就此誕生,底層區塊鏈技術瞬間成為學術界與商業界熱門的開發主題。區塊鏈的核心技術包含密碼學、共識機制、分散式系統、點對點網路通訊和其他知名技術,並提供一個安全可信的分散式架構以及分散式帳本,隨著區塊鏈的出現引起不少學者的關注,區塊鏈具有去中心化、永久性、匿名性以及可驗證性的特性,已應用於眾多產業上,區塊鏈運作的機制中作為主要的核心技術——共識機制,其能有效的維護分散式系統,不僅能提高區塊鏈的一致性,也能有效防止區塊資料被竄改,提高區塊的真實性與可靠性;然而,區塊鏈中採用的工作量證明共識機制需要耗費大量能源來維護分散式系統,平均每秒驗證交易量只有5-7筆交易。因此,為了提升驗證效率以及能源消耗問題,本研究提出一個結合多層感知器與投票制度的共識機制,將採用機器學習中的多層感知器選擇超級節點,透過超級節點之間的投票來達成一致性的共識。最終實驗結果證明,本研究中提出的共識機制平均每秒驗證交易量14-15筆交易、平均共識時間落在40.127秒遠高於工作量證明每秒驗證7筆交易以及10分鐘的達成共識時間,不僅如此也採用DPOS選舉機制取代工作量證明雜湊競賽減少能源消耗的問題。

    The concept of blockchain is derived from a paper published by Satoshi Nakamoto in 2008. A new decentralized blockchain was born. The underlayer technology of blockchain became a popular development theme in academic and business fields. The kernel technology of blockchain includes cryptography, consensus mechanism, decentralized system, peer-to-peer network and other well-known technologies. And blockchain provides a secure and reliable decentralized architecture and decentralized ledger. Blockchain has the characteristics of decentralization, permanence, anonymity and verifiability, and has been applied to many industries. The consensus mechanism is the core of blockchain technology. It can effectively maintain the decentralized system. It can not only improve the consistency of the blockchain, the authenticity and reliability of the block, but also effectively prevent the block data from being tampered.
    However, the proof of work consensus mechanism used in blockchain technology required a lot of electric energy to maintain the decentralized system. The average verification transaction volume of proof of work was only 5-7 transactions per second. Therefore, in order to enhance verification efficiency and solve energy consumption issues, this study proposes a consensus mechanism that combines multilayer perceptrons and voting systems. Multilayer perceptrons will be used to select super nodes, and the consistency consensus will be achieved through voting between super nodes. The final experimental results prove that the consensus mechanism proposed in this study verifies 14-15 transactions per second on average, and the average of block verification time falls at 40.127 seconds. Compare to the proof of work system, which can verify only 7 transactions per second and spend 10 minutes for block verification, the consensus mechanism proposed in this study is quicker and more efficient. Besides, the adoption of DPOS election mechanism replacing the proof of work competition can help reduce energy consumption.

    目錄 中英摘要 I 圖目錄 XI 表目錄 XII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 研究流程 4 第二章 文獻探討 5 2.1 區塊鏈 5 2.1.1 區塊鏈與分散式帳本技術(Distributed Ledger Technology, DLT) 5 2.1.2 區塊鏈技術架構 6 2.1.3 雜湊函式(Hash Function) 8 2.1.4 默克爾樹(Merkle Tree) 9 2.2 共識機制 10 2.2.1 工作量證明(Proof Of Work, POW) 10 2.2.2 權益證明(Proof of Stake, POS) 11 2.2.3 委託權益證明(Delegate Proof of Stake, DPOS) 11 2.2.4 燃燒證明(Proof Of Burn, POB) 12 2.2.5 權威證明(Proof Of Authority, POA) 12 2.2.6 學習證明(Proof Of Learning, POL) 13 2.2.7 AI證明(Proof Of AI, POAI) 13 2.3 機器學習 14 2.3.1 神經網路(Neural Network, NN) 14 2.3.2 多層感知器(Multilayer Perceptron, MLP) 15 2.3.3 遞歸神經網路(Recurrent Neural Network, RNN) 15 2.4 本章總結 16 第三章 研究方法 18 3.1 區塊鏈結構 18 3.2 區塊格式定義 19 3.2.1 投票競賽Root 21 3.2.2 機器學習模型與權重的雜湊 21 3.2.3 區塊體格式 21 3.3 共識機制流程 22 3.3.1投票演算法 25 3.3.2 MLP模型選擇節點與學習訓練方式 25 3.3.3 機器學習模型評估 26 第四章 研究成果 29 4.1 共識機制開發環境 29 4.1.1 開發工具與資料格式 29 4.1.2 硬體設備與作業系統 30 4.1.3 點對點架構與加密框架 30 4.2 雛型開發 31 4.2.1 區塊鏈架設 31 4.2.2 共識機制之機器學習模型訓練 32 4.3 共識機制評估標準 33 4.3.1 故障容許度( Fault Tolerance) 34 4.3.2 區塊容量(Block Size) 35 4.3.3 共識驗證時間(Block Verification Time) 36 4.3.4 每秒驗證交易量(Transaction per second, TPS) 37 4.3.5 共識比較 39 4.4 安全性分析 40 4.4.1 雙花攻擊 (Double spending attack) 41 4.4.2 51%攻擊 (51% attack) 41 4.4.3 女巫攻擊 (Sybil attack) 41 4.4.4 小結 41 第五章 結論與未來研究發展 43 5.1 研究結論 43 5.2 研究限制與未來發展 44 參考文獻 46

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