Detecting anomaly in data streams by fractal model
Detecting anomaly in data streams by fractal model
复制标题
通过分形模型检测数据流中的异常
DOI：
10.1007/s11280-014-0296-y
复制
发表时间：
2014-06
期刊：
影响因子：
--
通讯作者：
Zhou Aoying
中科院分区：
文献类型：
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作者：
Zhang Rong;Zhou Minqi;Gong Xueqing;He Xiaofeng;Qian Weining;Qin Shouke;Zhou Aoying
数据流异常检测在风险分析、网络监控、趋势分析等方面有着广泛的应用，因此受到学术界和工业界的广泛关注。1)会产生大量的假阳性结果。2)需要训练数据来建立检测模型，并且必须根据经验设置适当的时间窗口大小沿着相应的阈值。3)时间和空间开销通常都很高。为了克服这些局限性。我们提出了一种基于分形模型的方法来检测异常，改变底层数据分布在本文中。介绍了一种基于历史的算法和一种无参数算法。我们表明，后者的方法只消耗有限的内存，不涉及任何训练过程。对算法进行了理论分析。在真实的生活数据集上的实验结果表明，与现有的异常检测方法相比，该算法能够以更小的空间和时间复杂度获得更高的检测精度。
Detecting anomaly in data streams attracts great attention in both academic and industry communities due to its wide range application in venture analysis, network monitoring, trend analysis and so on. However, existing methods on anomaly detection suffer three problems. 1) A large number of false positive results are generated. 2) Training data are needed to build the detection model, and an appropriate time window size along with corresponding threshold has to be set empirically. 3) Both time and space overhead is usually very high. To address these limitations. We propose a fractal-model-based approach to detection of anomalies that change underlying data distribution in this paper. Both a history-based algorithm and a parameter-free algorithm are introduced. We show that the later method consumes only limited memory and does not involve any training process. Theoretical analyses of the algorithm are presented. The experimental results on real life data sets indicate that, compared with existing anomaly detection methods, our algorithm can achieve higher precision with less space and time complexity.
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DOI：
10.1109/ssdm.2000.869778
发表时间：
2000-07
期刊：
Proceedings. 12th International Conference on Scientific and Statistica Database Management
影响因子：
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作者：
C. Shahabi;Xiaoming Tian;Wugang Zhao
通讯作者：
C. Shahabi;Xiaoming Tian;Wugang Zhao
DOI：
10.1145/872757.872857
发表时间：
2003-06
期刊：
--
影响因子：
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作者：
Sirish Chandrasekaran;Owen Cooper;A. Deshpande;M. Franklin;J. Hellerstein;W. Hong;S. Krishnamurthy;S. Madden;Frederick Reiss;Mehul A. Shah
通讯作者：
Sirish Chandrasekaran;Owen Cooper;A. Deshpande;M. Franklin;J. Hellerstein;W. Hong;S. Krishnamurthy;S. Madden;Frederick Reiss;Mehul A. Shah
DOI：
10.1145/1007568.1007586
发表时间：
2004-06
期刊：
--
影响因子：
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作者：
M. Vlachos;Christopher Meek;Zografoula Vagena;D. Gunopulos
通讯作者：
M. Vlachos;Christopher Meek;Zografoula Vagena;D. Gunopulos
DOI：
10.1109/icde.2006.22
发表时间：
2006-04
期刊：
22nd International Conference on Data Engineering (ICDE'06)
影响因子：
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作者：
Shouke Qin;Weining Qian;Aoying Zhou
通讯作者：
Shouke Qin;Weining Qian;Aoying Zhou
DOI：
10.1145/1007568.1007695
发表时间：
2004-06
期刊：
--
影响因子：
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作者：
Y. D. Cai;David Clutter;Greg Pape;Jiawei Han;M. Welge;L. Auvil
通讯作者：
Y. D. Cai;David Clutter;Greg Pape;Jiawei Han;M. Welge;L. Auvil