计算机工程与应用2011，Vol.47Issue(18)：9-12,4.DOI:10.3778/j.issn.1002-8331.2011.18.003
集成学习分布式异常检测方法
Distributed anomaly detection based on ensemble learning
摘要
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.关键词
教据挖掘/集成学习/分布式/异常检测Key words
data mining/ensemble learning/distributed/anomaly detection分类
信息技术与安全科学引用本文复制引用
周绪川,钟勇..集成学习分布式异常检测方法[J].计算机工程与应用,2011,47(18):9-12,4.基金项目
四川省科技攻关计划(the Key Technologies R&D Program of Sichuan Province,China under Grant No.2008GZ0003) （the Key Technologies R&D Program of Sichuan Province,China under Grant No.2008GZ0003）
中央高校基本业务费专项基金(No.09NZYZJ02) （No.09NZYZJ02）
西南民族大学自然科学基金(No.10NYB003). （No.10NYB003）
