Online nonparametric anomaly detection based on geometric entropy minimization
Online nonparametric anomaly detection based on geometric entropy minimization
复制标题
基于几何熵最小化的在线非参数异常检测
DOI：
10.1109/isit.2017.8007082
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Y. Yilmaz
中科院分区：
文献类型：
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作者：
Y. Yilmaz
我们考虑突然和持续异常的在线和非参数检测，例如由于异常事件而在时间实例上发生常规系统动力学的变化（例如，失败，恶意活动）。结合非参数几何熵最小化（GEM）方法的简单性与累积总和（CUSUM）算法的及时检测能力，我们提出了一种适用于高维数据集的计算有效的在线异常检测方法，并同时实现给定的错误警报约束的接近最短的平均检测延迟性能。我们为GEM和CUSUM提供了新的见解，包括针对GEM的新渐近分析，可以使异常检测的软决策以及对Cusum的新解释，从差异理论方面，可以帮助我们将其推广到非参数gem统计量。我们使用模拟和真实数据集在数值上表明，所提出的非参数算法达到了千层面的参数cusum测试的近距离性能。
We consider the online and nonparametric detection of abrupt and persistent anomalies, such as a change in the regular system dynamics at a time instance due to an anomalous event (e.g., a failure, a malicious activity). Combining the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we propose a computationally efficient online anomaly detection method that is applicable to high-dimensional datasets, and at the same time achieve a near-optimum average detection delay performance for a given false alarm constraint. We provide new insights to both GEM and CUSUM, including new asymptotic analysis for GEM, which enables soft decisions for outlier detection, and a novel interpretation of CUSUM in terms of the discrepancy theory, which helps us generalize it to the nonparametric GEM statistic. We numerically show, using both simulated and real datasets, that the proposed nonparametric algorithm attains a close performance to the clairvoyant parametric CUSUM test.