Publication: Detecting suspicious transactions on bitcoin networkusing unsupervised learning
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Issued Date
2024-07-19
Resource Type
Language
eng
File Type
application/pdf
Access Rights
Open Access
Rights
ผลงานนี้เผยแพร่ภายใต้ สัญญาอนุญาตครีเอทีฟคอมมอนส์แบบ แสดงที่มา-ไม่ใช้เพื่อการค้า-ไม่ดัดแปลง 4.0 (CC BY-NC-ND 4.0)
Rights Holder(s)
Srinakharinwirot University
Suggested Citation
Yossapol Witayanont, ยศพล วิทยานนท์ (2024). Detecting suspicious transactions on bitcoin networkusing unsupervised learning. Retrieved from: https://hdl.handle.net/20.500.14740/54280
Alternative Title(s)
การตรวจจับธุรกรรมต้องสงสัยบนเครือข่ายบิทคอยน์ด้วยการเรียนรู้แบบไม่มีผู้สอน
Author(s)
Advisor(s)
Organization
Abstract
This research is the study and development of unsupervised learning algorithms to detect suspicious entities on the Bitcoin network. The objective is to develop a practical model for detecting anomalies in the Bitcoin network. This study was divided into two tasks, which are transaction and wallet address. The statistical techniques are applied for feature engineering and a Histogram-based Outlier Score (HBOS) and Isolation Forest (IForest) algorithms are trained and evaluated. The evaluations utilized were visualization, dual, and known-thieves evaluations. The result showed a similar detection for both algorithms. While HBOS has a higher wallet visualization score at 0.423, Isolation Forest yields better scores on transaction visualization, dual, and known-thieves evaluations with scores of 0.713, 0.681, and 0.035, respectively.
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Degree Name
MASTER OF SCIENCE (M.Sc.)
Degree Discipline
Department of Computer Science
Degree Grantor(s)
Srinakharinwirot University