Anomaly Detection for the Security of Cargo Shipments

  • Muriel Pellissier
  • Evangelos Kotsakis
  • Hervé Martin
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Detecting anomalies in the maritime domain is nowadays essential as the number of goods transported by maritime containers keeps increasing. An anomaly can be described in several ways depending on the application domain. For cargo shipments, an anomaly can be defined as an unexpected relationship between ports. This chapter describes a new approach for detecting anomalies in the sequential data used to describe cargo shipments. The technique is divided in two steps. First, we find the normal itineraries with a regular expression technique. Then, we compare a given itinerary with a normal itinerary using a distance-based method in order to classify the given itinerary as normal or suspicious. The first results of this method are very promising, and it can be further improved when integrated with time-based information. This chapter presents both the methodology and some results obtained using real-world data representing container movements.

Keywords

Anomaly detection Maritime security Sequence Regular expression Distance 

References

  1. Agovic A, Banerjee A, Ganguly AR, Protopopescu V (2009) Anomaly detection using manifold embedding and its applications in transportation corridors. Intell Data Anal 13(3):435–455Google Scholar
  2. Chandola V, Banerjee A, Kumar V (2012) Anomaly detection for discrete sequences: a survey. IEEE Trans Knowl Data Eng, pp 823–839Google Scholar
  3. Cook DJ, Holder LB (2000) Graph-based data mining. IEEE Intell Syst 15(2):32–41CrossRefGoogle Scholar
  4. Eberle W, Holder L (2007) Anomaly detection in data represented as graphs. J Intell Data Anal 11(6):663–689Google Scholar
  5. Eberle W, Holder L, Massengill B (2012) Graph-based anomaly detection applied to homeland security cargo screening. International conference of the florida artificial intelligence research society (FLAIRS)Google Scholar
  6. Cardoso B (2008) Standalone Multiple Anomaly Recognition Technique—SMART. http://www.sbir.gov/sbirsearch/detail/137676
  7. Kou Y, Lu C, Chen D (2006) Spatial weighted outlier detection. Proceedings of the sixth SIAM international conference on data mining, Bethesda, MD, USAGoogle Scholar
  8. Ling Y, Jin M, Hilliard MR, Usher JM (2009) A study of real-time identification and monitoring of barge-carried hazardous commodities. 17th International conference on Geoinformatics, pp 1–4Google Scholar
  9. Lu C, Chen D, Kou Y (2008) Detecting spatial outliers with multiple attributes. Fifth IEEE international conference on tools with artificial intelligence, pp 122Google Scholar
  10. Rissanen J (1989) Stochastic complexity in statistical inquiry. World Scientific Publishing CompanyGoogle Scholar
  11. Sun P, Chawla S (2004) On local spatial outliers. Fourth IEEE international conference on data mining, pp 209–216Google Scholar
  12. Swaney RE, Gianoulis ER (2008) Cargo X-ray image anomaly detection using intelligent agents—FORELL. http://www.sbir.gov/sbirsearch/detail/168550

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Muriel Pellissier
    • 1
  • Evangelos Kotsakis
    • 1
  • Hervé Martin
    • 2
  1. 1.European Commission, Joint research CentreInstitute for the Protection and Security of the CitizenIspraItaly
  2. 2.Laboratory of InformaticsUniversity Joseph FourierGrenobleFrance

Personalised recommendations