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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖世偉 | zh_TW |
| dc.contributor.advisor | Shi-Wei Liao | en |
| dc.contributor.author | 蕭年葳 | zh_TW |
| dc.contributor.author | Nien-Wei Hsiao | en |
| dc.date.accessioned | 2024-10-15T16:05:28Z | - |
| dc.date.available | 2024-10-16 | - |
| dc.date.copyright | 2024-10-15 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-10-08 | - |
| dc.identifier.citation | [1] Bzx - rekt, Feb 2020. https://rekt.news/bzx-rekt/.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96104 | - |
| dc.description.abstract | 自從 Aave 在 2018 年引入閃電貸功能後，盡管提高了 DeFi 上資產的流動性，但也吸引了許多駭客利用此功能進行價格操縱並套利。到了 2024 年，這類攻擊仍屢見不鮮。本文設計了 ATR_AWMA(Average True Range_Adaptive Weighted Moving Average) 演算法，旨在為項目方在報價時提供額外參考和保護。我們對該算法進行了模擬回測，使用了過去（2020 年）的數據，因為當時發生過一起重大的操作價格攻擊，和近期數據，因為近期也同樣發生了重大的操作價格攻擊，並和前人的論文結果做比較，取得了顯著成果。結果表明，ATR_AWMA 能穩定預測趨勢，防止價格被不當操縱。 | zh_TW |
| dc.description.abstract | Since Aave introduced flash loans in 2018, while increasing asset liquidity in DeFi, it has also attracted hackers exploiting this feature for price manipulation and arbitrage. By 2024, such attacks remain prevalent. This paper presents the ATR_AWMA(Average True Range_Adaptive Weighted Moving Average) algorithm, designed to provide additional reference and protection for projects during price quotation. We conducted backtesting using both historical (2020), because a significant price manipulation attack occurred at that time and recent data, because there is also a significant price manipulation attack occurred recently, comparing our results with previous studies. The findings indicate that ATR_AWMA can reliably predict trends and prevent improper price manipulation caomparing to some traditional methods | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-10-15T16:05:28Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-10-15T16:05:28Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 i Abstract ii Contents iii List of Figures vi List of Tables vii Chapter 1 Introduction 1 1.1 Research Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Chapter 2 Background 4 2.1 DeFi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Constant Product Market Maker Model . . . . . . . . . . . . . . . . 5 2.3 Oracle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Flash Loan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Time-Weighted Average Price (TWAP) and Related Models . . . . . 8 2.6 WMA_KAI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.7 VWAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.8 Comparison of TWAP and VWAP in DeFi Security Research . . . . . 12 Chapter 3 Design and Experiment 15 3.1 Detailed Analysis of the UwU Attack Event . . . . . . . . . . . . . . 15 3.1.1 Attack Steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.1.2 Attack Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Introduction to ATR_AWMA Algorithm . . . . . . . . . . . . . . . . 17 3.2.1 ATR_AWMA Price Processing Formula Design . . . . . . . . . . . 17 3.2.2 Design Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.3 Calculation Formula . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.4 Dynamic Weight Adjustment Mechanism . . . . . . . . . . . . . . 23 3.2.5 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.6 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Introduction to ATR Algorithm . . . . . . . . . . . . . . . . . . . . 25 3.3.1 Design Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.2 Calculation Formula . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.3 ATR_AWMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.4 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Chapter 4 Evaluation and Discussion 29 4.1 Evaluation of ATR_AWMA Algorithm . . . . . . . . . . . . . . . . 29 4.2 Results Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 Data Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2.1.1 Using data from 2020/01~2020/04 . . . . . . . . . . . 31 4.2.1.2 Using data from 2024/04 ~2024/06 . . . . . . . . . . . 38 4.3 Resistance to Price Manipulation during Flash Loan Attacks . . . . . 46 Chapter 5 Conclusion and Future Work 48 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 References 51 | - |
| dc.language.iso | en | - |
| dc.title | 使用時間序列方法提升對預言機操作價格攻擊的抵抗力 | zh_TW |
| dc.title | Enhancing Resistance to Oracle Price Manipulation Attacks Using Time Series Methods | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 傅楸善;黃敬群;盧瑞山;李逸元 | zh_TW |
| dc.contributor.oralexamcommittee | Chiou-Shann Fuh;Ching-Chun Huang;Ruei-Shan Lu;Yi-Yuan Lee | en |
| dc.subject.keyword | 閃電貸攻擊,去中心化金融（DeFi）,價格預言機,自動化做市商（AMM）,時間加權平均價格, | zh_TW |
| dc.subject.keyword | Flash Loan Attack,Decentralized Finance (DeFi),Price Oracle,Automated Market Maker (AMM),Time Weighted Average Price Model(TWMA), | en |
| dc.relation.page | 54 | - |
| dc.identifier.doi | 10.6342/NTU202404456 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-10-08 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| dc.date.embargo-lift | 2029-10-08 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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