关键词: 数据挖掘, 集成学习, 分布式, 异常检测

Abstract: Detecting anomalous behavior from terabytes of collected record data has emerged as a crucial component for many systems for data mining system.Very often，processing record data collected from various locations or providers cannot be directly aggregated for anomaly analysis due to the proprietary nature of the data.This paper proposes a novel general framework for anomaly detection from distributed data sources that cannot be directly merged.In the proposed method，anomaly detection algorithm is firstly applied to data from individual provider and then their results are combined.It investigates ten semi-supervised anomaly detection algorithms，as well as four methods for combining anomaly detection results.The experiments performed on simulated data have shown that particular anomaly detection algorithms and combining methods are more suitable for the task of distributed anomaly detection than others.

Key words: data mining, ensemble learning, distributed, anomaly detection