Incremental anomaly detection approach for characterizing unusual profiles

Yi Fang, Olufemi A. Omitaomu, Auroop R. Ganguly

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The detection of unusual profiles or anomalous behavioral characteristics from sensor data is especially complicated in security applications where the threat indicators may or may not be known in advance. Predictive modeling of massive volumes of historical data can yield insights on usual or baseline profiles, which in turn can be utilized to isolate unusual profiles when new data are observed in real-time. Thus, an incremental anomaly detection approach is proposed. This is a two-stage approach in which the first stage processes the available historical data and develops statistics that are in turn used by the second stage in characterizing the new incoming data for real-time decisions. The first stage adopts a mixture model of probabilistic principal component analyzers to quantify each historical observation by probabilistic measures. The second stage is a chi-square based anomaly detection approach that utilizes the probabilistic measures obtained in the first stage to determine if the incoming data is an anomaly. The proposed anomaly detection approach performs satisfactorily on simulated and benchmark datasets. The approach is also illustrated in the context of detecting commercial trucks that may pose safety and security risk. It is able to consistently identified trucks with anomalous features in the scenarios investigated.

Original languageEnglish
Title of host publicationKnowledge Discovery from Sensor Data - Second International Workshop, Sensor-KDD 2008, Revised Selected Papers
Pages190-202
Number of pages13
DOIs
StatePublished - 2010
Event2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5840 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period08/24/0808/27/08

Keywords

  • PPCA
  • Transportation security
  • chi-square statistics
  • incremental knowledge discovery
  • radioactive materials

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