PROBABILISTIC REASONING FOR STREAMING ANOMALY DETECTION

被引：0

作者：

Carter, Kevin M. ^{[1
]}

Streilein, William W. ^{[1
]}

机构：

[1] MIT, Lincoln Lab, Lexington, MA 02420 USA

来源：

2012 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2012年

关键词：

Anomaly detection; predictive models; time series analysis; statistical learning; information security; CHART;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In many applications it is necessary to determine whether an observation from an incoming high-volume data stream matches expectations or is anomalous. A common method for performing this task is to use an Exponentially Weighted Moving Average (EWMA), which smooths out the minor variations of the data stream. While EWMA is efficient at processing high-rate streams, it can be very volatile to abrupt transient changes in the data, losing utility for appropriately detecting anomalies. In this paper we present a probabilistic approach to EWMA which dynamically adapts the weighting based on the observation probability. This results in robustness to data anomalies yet quick adaptability to distributional data shifts.

引用

页码：377 / 380

页数：4

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