Skip to main content

Alert Management Systems: A Quick Introduction

  • Chapter
Managing Cyber Threats

Part of the book series: Massive Computing ((MACO,volume 5))

Abstract

We describe a type of data mining system designed to screen events, build profiles associated with the events, and send alerts based upon the profiles and events. These types of systems are becoming known as alert management systems (AMS). We give some examples of alert management systems and give a quick introduction to their architecture and functionality.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dean W. Abbott, I. Phillip Matkovsky, and John F. Elder IV. An evaluation of highend data mining tools for fraud detection. In IEEE International Conference on Systems, Man and Cybernetics, 1998.

    Google Scholar 

  2. C. Cortes, K. Fisher, D. Pregibon, and A. Rogers. Hancock: A Language for Extracting Signatures from Data Streams. In Proceedings of the Association for Computing Machinery Sixth International Conference on Knowledge Discovery and Data Mining, pages 9–17, 2000.

    Google Scholar 

  3. C. Cortes and D. Pregibon, Signature-based methods for data streams, Data Mining and Knowledge Discovery, 2001.

    Google Scholar 

  4. T. Fawcett and F. Provost, Adaptive Fraud Detection, Data Mining and Knowledge Discovery, Volume 1, Number 3, 1997, pages 291–316.

    Article  Google Scholar 

  5. T. Fawcett, and F. Provost, Activity monitoring: Noticing interesting changes in behavior, Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, 1999, pages 53–62.

    Google Scholar 

  6. R. L. Grossman, H. Bodek, D. Northcutt, and H. V. Poor, Data Mining and Tree-based Optimization, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, E. Simoudis, J. Han and U. Fayyad, editors, AAAI Press, Menlo Park, California, 1996, pp 323–326.

    Google Scholar 

  7. PATTERN Data Mining System, Version 1.2, Magnify, Inc., 1997.

    Google Scholar 

  8. PATTERN Data Mining System, Version 3.1, Magnify, Inc. 2000.

    Google Scholar 

  9. R. L. Grossman and R. G. Larson, An Algebraic Approach to Data Mining: Some Examples, Proceedings of the 2002 IEEE International Conference on Data Mining, IEEE Computer Society, Los Alamitos, California, 2002, pages 613–616.

    Google Scholar 

  10. HNC Software, a division of Fair Isaac Corporation, retrieved from http://www.fairisaac.com/fairisaac on August 20, 2003.

    Google Scholar 

  11. Daryl Pregibon, Graph Mining: Discovery in Large Networks, CCR/DIMACS Workshop on Mining Massive Data Sets and Streams: Mathematical Methods and Algorithms for Homeland Defense, June 2002.

    Google Scholar 

  12. Snort(tm), The Open Source Network Intrusion Detection System, retrieved from http://www.snort.org on August 20, 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Grossman, R.L. (2005). Alert Management Systems: A Quick Introduction. In: Kumar, V., Srivastava, J., Lazarevic, A. (eds) Managing Cyber Threats. Massive Computing, vol 5. Springer, Boston, MA. https://doi.org/10.1007/0-387-24230-9_11

Download citation

  • DOI: https://doi.org/10.1007/0-387-24230-9_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24226-2

  • Online ISBN: 978-0-387-24230-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics