TY - JOUR
T1 - Anomaly Detection Service for Blockchain Transactions Using Minimal Substitution-Based Label Propagation
AU - Wang, Ranran
AU - Zhang, Yin
AU - Peng, Limei
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - Supervising illicit activities on blockchain networks, such as money laundering, fraud, extortion, Ponzi schemes, and funding for terrorist organizations, presents significant challenges. Emerging machine learning methods for detecting abnormal transactions face hurdles due to high labeling costs, limited labeled data, and data imbalance. To address this, this article proposes a Minimal Substitution-based Label Propagation (MSLP) model to provide more labeled data to balance the graph data and complement the sample for anomalous transaction detection service in the blockchain networks. As far as we know, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more abnormal transactions with under-labeling budget constraints. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improved the F1 score of illegal transaction node detection by 2.6% to 8.2%.
AB - Supervising illicit activities on blockchain networks, such as money laundering, fraud, extortion, Ponzi schemes, and funding for terrorist organizations, presents significant challenges. Emerging machine learning methods for detecting abnormal transactions face hurdles due to high labeling costs, limited labeled data, and data imbalance. To address this, this article proposes a Minimal Substitution-based Label Propagation (MSLP) model to provide more labeled data to balance the graph data and complement the sample for anomalous transaction detection service in the blockchain networks. As far as we know, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more abnormal transactions with under-labeling budget constraints. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improved the F1 score of illegal transaction node detection by 2.6% to 8.2%.
KW - Minimal substitution model
KW - anomaly detection service
KW - blockchain network
KW - imbalanced class
KW - label propagation
UR - http://www.scopus.com/inward/record.url?scp=85194818210&partnerID=8YFLogxK
U2 - 10.1109/TSC.2024.3407601
DO - 10.1109/TSC.2024.3407601
M3 - Article
AN - SCOPUS:85194818210
SN - 1939-1374
VL - 17
SP - 2054
EP - 2066
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 5
ER -