基于LSCP算法的比特币网络异常交易检测

异常检测是比特币交易数据分析的研究热点之一。针对现有的基于机器学习的异常交易检测方法难以对多种异常类型进行准确概括、泛化能力不足的问题，对比特币交易数据构建网络结构并提取异常行为模式相关特征，应用基于局部动态选择组合的并行集成算法（LSCP）构建检测模型，并在算法中融入7种经典的异常检测算法，利用基学习器对不同异常类型的敏感性，提升检测模型的可靠性和稳定性。实验结果表明，与传统的检测方法相比，结合异构基学习器的LSCP算法在整体检测性能上具有更好的效果。

Bitcoin Network Abnormal Transaction Detection Based on LSCP Algorithm

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the problems that the existing abnormal transaction detection methods based on machine learning are difficult to accurately summarize various abnormal types, and the generalization ability is insufficient, the network structure of Bitcoin transaction data is constructed and the related features of abnormal behavior patterns are extracted, and the detection model is constructed by using the parallel integration algorithm（LSCP） based on local dynamic selection and combination, and seven classical abnormal detection algorithms are incorporated into the algorithm, so as to improve the reliability and stability of the detection model by using the sensitivity of the base learner to different abnormal types. The experimental results show that, compared with the traditional detection method, the LSCP algorithm combined with the heterogeneous learner has a better effect on the overall detection performance.