Skip to main content
SpringerLink
Log in

Anomaly Detection for Physical Threat Intelligence

  • Conference paper
  • First Online:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

  • 802 Accesses

Abstract

Anomaly detection is a machine learning task that has been investigated within diverse research areas and application domains. In this paper, we performed anomaly detection for Physical Threat Intelligence. Specifically, we performed anomaly detection for air pollution and public transport traffic analysis for the city of Oslo, Norway. To this aim, the state-of-the-art method SparkGHSOM was considered to learn predictive models for normal (i.e. regular) scenarios of air quality and traffic jams in a distributed fashion. Furthermore, we extended the main algorithm to make the detected anomalies explainable through an instance-based feature ranking approach. The results showed that SparkGHSOM is able to detect anomalies for both the real applications considered in this study, despite the fact it was designed for different tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://api.nilu.no/.

  2. 2.

    https://api.entur.io/realtime/v1/rest/vm?datasetId=RUT.

References

  1. Corizzo, R., Ceci, M., Pio, G., Mignone, P., Japkowicz, N.: Spatially-aware autoencoders for detecting contextual anomalies in geo-distributed data. In: Soares, C., Torgo, L. (eds.) Discovery Science, pp. 461–471. Springer International Publishing, Cham (2021)

    Chapter  Google Scholar 

  2. Gonçalves, P.M., Jr., Barros, R.S.: RCD: a recurring concept drift framework. Patt. Recogn. Lett. 34(9), 1018–1025 (2013). https://doi.org/10.1016/j.patrec.2013.02.005

    Article  Google Scholar 

  3. Hsu, C.C.: Generalizing self-organizing map for categorical data. IEEE Trans. Neural Networks 17(2), 294–304 (2006). https://doi.org/10.1109/TNN.2005.863415

    Article  Google Scholar 

  4. Kader, G.D., Perry, M.: Variability for categorical variables. J. Stat. Educ. 15(2) (2007). https://doi.org/10.1080/10691898.2007.11889465

  5. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990). https://doi.org/10.1109/5.58325

    Article  Google Scholar 

  6. Malondkar, A., Corizzo, R., Kiringa, I., Ceci, M., Japkowicz, N.: Spark-GHSOM: growing hierarchical self-organizing map for large scale mixed attribute datasets. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.12.007

    Article  Google Scholar 

  7. Redavid, D., Corizzo, R., Malerba, D.: An owl ontology for supporting semantic services in big data platforms. In: 2018 IEEE International Congress on Big Data (BigData Congress), pp. 228–231 (2018). https://doi.org/10.1109/BigDataCongress.2018.00039

  8. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7

    Article  MATH  Google Scholar 

  9. Stojanova, D., Ceci, M., Appice, A., Džeroski, S.: Network regression with predictive clustering trees. Data Mining Knowl. Disc. 25(2), 378–413 (2012). https://doi.org/10.1007/s10618-012-0278-6

    Article  MATH  Google Scholar 

Download references

Acknowledgment

We acknowledge the project IMPETUS (Intelligent Management of Processes, Ethics and Technology for Urban Safety) that receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 883286. https://cordis.europa.eu/project/id/883286. Dr. Paolo Mignone acknowledges the support of Apulia Region through the REFIN project “Metodi per l’ottimizzazione delle reti di distribuzione di energia e per la pianificazione di interventi manutentivi ed evolutivi” (CUP H94I20000410008, Grant n. 7EDD092A).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bari Aldo Moro, Via Orabona, 4, 70125, Bari, Italy

    Paolo Mignone, Donato Malerba & Michelangelo Ceci

  2. Big Data Lab, National Interuniversity Consortium for Informatics (CINI), Via Ariosto, 25, 00185, Rome, Italy

    Paolo Mignone, Donato Malerba & Michelangelo Ceci

Authors
  1. Paolo Mignone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donato Malerba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michelangelo Ceci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paolo Mignone .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

    Wouter Verbeke

  21. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  22. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  23. AMD, Dublin, Ireland

    Michaela Blott

  24. UniCredit, Rome, Italy

    Ilaria Bordino

  25. UniCredit, Milan, Italy

    Ivan Luciano Danesi

  26. National Agency for New Technologies, Rome, Italy

    Giovanni Ponti

  27. Unicredit, Rome, Italy

    Lorenzo Severini

  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  30. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  31. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

    Naghmeh Ghazaleh

  34. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  35. Novartis, Basel, Switzerland

    Diego Saldana

  36. Novartis, Basel, Switzerland

    Konstantinos Sechidis

  37. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Arif Canakoglu

  38. Politecnico di Milano, Milan, Italy

    Sara Pido

  39. Politecnico di Milano, Milan, Italy

    Pietro Pinoli

  40. University of Waikato, Hamilton, New Zealand

    Albert Bifet

  41. Halmstad University, Halmstad, Sweden

    Sepideh Pashami

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mignone, P., Malerba, D., Ceci, M. (2023). Anomaly Detection for Physical Threat Intelligence. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23618-1_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23617-4

  • Online ISBN: 978-3-031-23618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation