面向异常检测的时间序列树突状细胞算法

doi:10.3969/j.issn.1001-2400.2014.04.025

Abstract

Abstract:

Aiming at the fact that the high randomness existing in definitions of signals and the antigen results in the lower detection rate used by the Dendritic Cell Algorithm, the Dendritic Cell Algorithm for anomaly detection based on time series is proposed. The underlying methodology is to perform antigen detection via the change point detection and multiple data streams correlation analysis, and the change point data reflecting the mutation status as the candidate solution of the abnormal is selected. Features are extracted based on the subspace tracker algorithm and various input signal subspaces are obtained and classified precisely. A dynamic migration threshold is incorporated into the context evaluation of the algorithm. The accumulation of the antigen assessment in a certain window time decreases the false positive rate effectively. Simulation demonstrates that the algorithm shows a better performance on the detection rate, accuracy rate and stability with less memory space and computing resource.

Key words: dendritic cell algorithm, anomaly detection, time series, signal processing, subspace tracker, change point detecting

CLC Number:

TIAN Yuling. Dendritic cell algorithm for time series oriented anomaly detection[J].J4, 2014, 41(4): 144-150.

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Dendritic cell algorithm for time series oriented anomaly detection

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