一种基于AdaBoost模型的区块链异常交易检测方案

doi:10.3969/j.issn.1671-1122.2024.01.003

信息网络安全 ›› 2024, Vol. 24 ›› Issue (1): 24-35.doi: 10.3969/j.issn.1671-1122.2024.01.003

一种基于AdaBoost模型的区块链异常交易检测方案

宋玉涵(), 祝跃飞, 魏福山

中国人民解放军信息工程大学网络空间安全学院，郑州 450001

收稿日期:2023-08-02 出版日期:2024-01-10 发布日期:2024-01-24
通讯作者: 宋玉涵 E-mail:lynn.redhead@hotmail.com
作者简介:宋玉涵（1989—），女，安徽，博士研究生，主要研究方向为隐私计算和区块链|祝跃飞（1962—），男，浙江，教授，博士，主要研究方向为网络空间安全|魏福山（1983—），男，甘肃，副教授，博士，主要研究方向为密码学
基金资助:
国家自然科学基金(61772548);国家重点研发计划(2019QY1300);河南省优秀青年基金(222300420099);河南省重大公益项目(201300210200)

An Anomaly Detection Scheme for Blockchain Transactions Based on AdaBoost Model

SONG Yuhan(), ZHU Yuefei, WEI Fushan

Institute of Cyberspace Security, Information Engineering University of PLA, Zhengzhou 450001, China

Received:2023-08-02 Online:2024-01-10 Published:2024-01-24
Contact: SONG Yuhan E-mail:lynn.redhead@hotmail.com

摘要/Abstract

摘要：

针对区块链系统加密货币交易记录中存在的盗币异常行为，文章基于AdaBoost模型提出一种具有隐私保护功能的异常交易检测方案。该方案采用加法同态加密和矩阵混淆技术，在有效识别并预测异常交易的同时，保证交易数据的隐私性。此外，在云外包环境中设计实现方案的底层协议，并证明了方案的正确性和隐私保护性质。与同类协议相比，该方案在保证隐私性的同时，具有较高的检测准确率和召回率，平均每条记录的检测时间为毫秒级，适用于真实加密货币交易的检测场景。

关键词: 隐私保护, 机器学习, 异常检测, 同态加密

Abstract:

In response to potential anomalous behaviors, such as coin theft in the transaction records of the blockchain-based cryptocurrency, a detection scheme with privacy protection function based on the adaptive boosting (AdaBoost) model was proposed. This scheme integrated additive homomorphic encryption and matrix perturbation techniques, ensuring the preservation of transaction data privacy while effectively identifying and predicting anomalies. The scheme’s underlying protocol was designed and implemented in a cloud outsourcing environment, and its correctness and privacy protection properties were proven. Compared with similar protocols, this scheme has high detection accuracy and recall while ensuring privacy. The detection time for each record was at the millisecond level, making it suitable for real cryptocurrency transaction detection scenarios.

Key words: privacy protection, machine learning, anomaly detection, homomorphic encryption

中图分类号:

TP309

宋玉涵, 祝跃飞, 魏福山. 一种基于AdaBoost模型的区块链异常交易检测方案[J]. 信息网络安全, 2024, 24(1): 24-35.

SONG Yuhan, ZHU Yuefei, WEI Fushan. An Anomaly Detection Scheme for Blockchain Transactions Based on AdaBoost Model[J]. Netinfo Security, 2024, 24(1): 24-35.

图/表 4

参考文献 23

[1]	KUMAR N, SINGH A, SHUKLA S K. Detecting Malicious Accounts on the Ethereum Blockchain with Supervised Learning[C]// Springer. Cyber Security Cryptography and Machine Learning- Fourth International Symposium. Heidelberg: Springer, 2020: 94-109.
[2]	BARTOLETTI M, LANDE S, POMPIANU L, et al. A General Framework for Blockchain Analytics[C]// ACM. The 1st Workshop on Scalable and Resilient Infrastructures for Distributed Ledgers. New York: ACM, 2017: 1-6.
[3]	KHALILOV M C K, LEVI A. A Survey on Anonymity and Privacy in Bitcoin-Like Digital Cash Systems[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2543-2585.
[4]	JIA Jinyuan, SALEM A, BACKES M, et al. Memguard: Defending Against Black-Box Membership Inference Attacks via Adversarial Examples[C]// ACM.The 26th ACM Conference on Computer and Communications Security (CCS 2019). New York: ACM, 2019: 259-274.
[5]	TRAMER F, ZHANG Fan, JUELS A, et al. Stealing Machine Learning Models via Prediction Apis[C]// USENIX. 25th USENIX Security Symposium 2016. Berkeley: USENIX, 2016: 601-618.
[6]	HIRSHMAN J, HUANG Yifei, MACKE S. Unsupervised Approaches to Detecting Anomalous Behavior in the Bitcoin Transaction Network[EB/OL]. (2013-09-23) [2023-08-01]. https://cs229.stanford.edu/proj2013/HirshmanHuangMacke-UnsupervisedApproachesToDetectingAnomalousBehaviorInTheBitcoinTransactionNetwork.pdf.
[7]	PHAM T, LEE S. Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods[EB/OL].(2016-11-12) [2023-08-01]. http://arxiv.org/abs/1611.03941.
[8]	PHAM T, LEE S. Anomaly Detection in the Bitcoin System-A Network Perspective[EB/OL]. (2016-11-13) [2023-08-01]. http://arxiv.org/abs/1611.03942.
[9]	MONAMO P M, MARIVATE V, TWALA B. A Multifaceted Approach to Bitcoin Fraud Detection: Global and Local Outliers[C]// IEEE. The 15th IEEE International Conference on Machine Learning and Applications. New York: IEEE, 2016: 188-194.
[10]	AWAN M K, CORTESI A. Blockchain Transaction Analysis Using Dominant Sets[C]// Springer. The 16th IFIP TC8 International Conference. Heidelberg: Springer, 2017: 229-239.
[11]	KIM J, NAKASHIMA M, FAN Wenjun, et al. Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking[C]// IEEE. IEEE International Conference on Blockchain and Cryptocurrency (ICBC 2021). New York: IEEE, 2021: 1-9.
[12]	SONG Yuhan, WEI Fushan, ZHU Kaijie, et al. Anomaly Detection as a Service: An Outsourced Anomaly Detection Scheme for Blockchain in a Privacy Preserving Manner[J]. IEEE Transactions on Network and Service Management, 2022, 19(4): 3794-3809. doi: 10.1109/TNSM.2022.3215006 URL
[13]	LI Qinbin, WU Zhaomin, WEN Zeyi, et al. Privacy-Preserving Gradient Boosting Decision Trees[EB/OL]. (2020-10-29) [2023-08-01]. https://arxiv.org/abs/1911.04209.
[14]	YANG Mengwei, SONG Linqi, XU Jie, et al. The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost[EB/OL]. (2019-10-14) [2023-08-01]. https://arxiv.org/abs/1907.07157.
[15]	CHENG Kewei, FAN Tao, JIN Yilun, et al. SecureBoost: A Lossless Federated Learning Framework[EB/OL].(2021-04-07) [2023-08-01]. https://arxiv.org/abs/1901.08755.
[16]	CHEN Weijing, MA Guoqiang, FAN Tao, et al. SecureBoost+: A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning[EB/OL]. (2021-12-23) [2023-08-01]. https://arxiv.org/abs/2110.10927.
[17]	FREUND Y, SCHAPIRE R E. A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139. doi: 10.1006/jcss.1997.1504 URL
[18]	MIVULE K, TURNER C, JI S. Towards A Differential Privacy and Utility Preserving Machine Learning Classifier[C]// Elsevier. The Complex Adaptive Systems 2012 Conference. Amsterdam: Elsevier, 2012: 176-181.
[19]	SHEN Siqian. Research on Differential Privacy Protection Classification Algorithm[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017.
	沈思倩. 关于差分隐私保护分类算法的研究[D]. 南京: 南京航空航天大学, 2017.
[20]	MA Zhuo, LIU Yang, LIU Ximeng, et al. Lightweight Privacy-Preserving Ensemble Classification for Face Recognition[J]. IEEE Internet of Things Journal, 2019, 6(3): 5778-5790. doi: 10.1109/JIOT.2019.2905555
[21]	DAVOUDI M. Efficient and Privacy-Preserving Adaboost Classification Framework for Mining Healthcare Data over Outsourced Cloud[D]. New Jersey: University of New Brunswick, 2020.
[22]	PAILLIER P. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes[C]// Springer. EUROCRYPT’ 99. Heidelberg:Springer, 1999: 223-238.
[23]	CHEN Binjie. Research on Blockchain Abnormal Transaction Detection for Privacy Protection[D]. Zhengzhou: Information Engineering University of PLA, 2022.
	陈彬杰. 隐私保护的区块链异常交易检测研究[D]. 郑州: 战略支援部队信息工程大学, 2022.

方法	特征维度	隐私保护	通信带宽	通信轮数	计算复杂度	准确率	精度	召回率	F1 分数
LOF^[9]	3	否	$O({{n}^{2}})$	1	$O({{n}^{2}}\log n)$	89.5%	0	0	—
MDB^[8]	3	否	$O({{n}^{2}})$	1	$O({{n}^{2}})$	89.6%	0.5%	0.6%	0.006
OCSVM^[8]	3	否	$O(nd)$	1	$O({{n}^{3}})$	82.8%	0.5%	0.2%	0.003
ADaaS^[12]	9	是	$O(nd)$	2	${{E}_{v}}+nI{{P}_{v}}$	97.8%	85.8%	87.5%	0.867
本文方法	9	是	$O({{d}^{2}})$	2	$d({{E}_{p}}+{{D}_{p}})$	97.3%	97.1%	100%	0.985

一种基于AdaBoost模型的区块链异常交易检测方案

An Anomaly Detection Scheme for Blockchain Transactions Based on AdaBoost Model

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