Anomaly Detection Using the Poisson Process Limit for Extremes

Abstract

Anomaly detection starts from a model of normal behavior and classifies departures from this model as anomalies. This paper introduces a statistical non-parametric approach for anomaly detection that is based on a multivariate extension of the Poisson point process model for univariate extremes. The method is demonstrated on both a synthetic and a real-world data set, the latter being an unbalanced data set of acceleration data collected from movements of 7 pediatric patients suffering from epilepsy that is previously studied in [1]. The positive predictive values could be improved with an increase up to 12.9% (and a mean of 7%) while the sensitivity scores stayed unaltered. The proposed method was also shown to outperform an one-class SVM classifier. Because the Poisson point process model of extremes is able to combine information on the number of excesses over a fixed threshold with that on the excess values, a powerful model to detect anomalies is obtained that can be of high value in many application.

A Content Level Based Deduplication on Streaming Data using Poisson Process Filter Technique (PPFT)

“…It's kind of anomaly detection to avoid duplicates and classification algorithm like Support Vector Machine (SVM) helps to classify data for detecting the anomaly in the deduplication process [8]. The Poisson process is accountable for finding anomalous event occurrences at a certain period and it can track the activity of each event at a time [9].…”

Machine Learning for Healthcare Technologies

“…The use of EVT enables to fit a model on this class even when examples are completely absent circumventing the optimization procedure which is commonly used in SVMs. In this section we review the recent methodologies of the use of EVT for novelty detection and illustrate the methods on the detection of epileptic seizures [5,12].…”