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Volume 29 Issue 6
Jan.  2011
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Xiang Kui, Jiang Jing-ping. An Anomaly Detection Algorithm Based on Hidden Pattern[J]. Journal of Electronics & Information Technology, 2007, 29(6): 1487-1491. doi: 10.3724/SP.J.1146.2005.01392
Citation: Xiang Kui, Jiang Jing-ping. An Anomaly Detection Algorithm Based on Hidden Pattern[J]. Journal of Electronics & Information Technology, 2007, 29(6): 1487-1491. doi: 10.3724/SP.J.1146.2005.01392

An Anomaly Detection Algorithm Based on Hidden Pattern

doi: 10.3724/SP.J.1146.2005.01392
  • Received Date: 2005-10-31
  • Rev Recd Date: 2006-05-15
  • Publish Date: 2007-06-19
  • It is a difficult problem how to detect such accident of a system. This paper presents a new algorithm, an anomaly detection algorithm based on hidden pattern. Epsilon machine, a new computational mechanics, can discover hidden pattern from the response time series. Causal State Splitting Reconstruction (CSSR), one algorithm of epsilon machine, can infer a set of causal states, which has an analogy to hidden Markov chain. Based on this set, an anomaly measure can be defined, which is the distance of two characteristic vectors. Computing all parts of the time series, an anomaly evolution curve can be got. In simulation analysis of Duffing equation, step changes appear in the anomaly curve, before Duffing oscillator begin to bifurcate. The algorithm proves to be effective in anomaly detection and warning.
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  • Shalizi C and Crutchfield J. Computational mechanics: Pattern and prediction, structure and simplicity[J].Journal of Statistical Physics.2001, 104(3):817-879[2]Shalizi C, Shalizi K, and Crutchfield J. An algorithm for pattern discovery in time series. SFI Working Paper, 2002: 02-10-060.[3]Crutchfield J and Young K. Inferring statistical complexity[J].Physical Review Letters.1989, 63(2):105-108[4]Chin S. Real time anomaly detection in complex dynamic systems. [PhD thesis], The Pennsylvania State University, 2004.[5]Ray A. Symbolic dynamic analysis of complex systems for anomaly detection[J].Signal Processing.2004, 84(7):1115-1130[6]Daw C, Finney C, and Tracy C. A review of symbolization analysis of experimental data[J].Review of Scientific Instruments.2003, 74(2):1-18[7]Kurths J, Schwarz U, and Witt A, et al.. Measures of complexity in signal analysis. In: Chaotic, Fractal, and Nonlinear Signal Processing, AIP Conference Proceedings, Woodbury, New York, 1996: 33-54.[8]庄楚强, 吴亚森. 应用数理统计基础. 广州: 华南理工大学出版社, 1999: 259-265.
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