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Title: Anomaly Detection in Transportation Corridors using Manifold Embedding

Abstract

The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.

Authors:
 [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
979070
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: ACM Workshop on Knowledge Discovery from Sensor Data: The 13th International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, 20070812, 20070812
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 97 MATHEMATICAL METHODS AND COMPUTING; TRANSPORTATION SYSTEMS; SECURITY; TRUCKS; WEIGHT; CARGO; DETECTION; DATA ANALYSIS

Citation Formats

Agovic, Amrudin, Banerjee, Arindam, Ganguly, Auroop R, and Protopopescu, Vladimir A. Anomaly Detection in Transportation Corridors using Manifold Embedding. United States: N. p., 2007. Web.
Agovic, Amrudin, Banerjee, Arindam, Ganguly, Auroop R, & Protopopescu, Vladimir A. Anomaly Detection in Transportation Corridors using Manifold Embedding. United States.
Agovic, Amrudin, Banerjee, Arindam, Ganguly, Auroop R, and Protopopescu, Vladimir A. Mon . "Anomaly Detection in Transportation Corridors using Manifold Embedding". United States. doi:.
@article{osti_979070,
title = {Anomaly Detection in Transportation Corridors using Manifold Embedding},
author = {Agovic, Amrudin and Banerjee, Arindam and Ganguly, Auroop R and Protopopescu, Vladimir A},
abstractNote = {The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}

Conference:
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