Skip to main content

Towards Self-defending Mechanisms Using Data Mining in the EFIPSANS Framework

  • Chapter
Advances in Multimedia and Network Information System Technologies

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 80))

Abstract

In currently used networks there are no self-protection or autonomous defending mechanisms. This situation leads to the spread of self-propagating malware, which causes even more dangerous, and significant threats i.e. Botnets. In the EFIPSANS project a new architecture that includes self-* functionalities is introduced. Self-defending functionality, using data mining approach detects and reacts to some of network threats.

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

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. http://www.cisco.com/en/US/solutions/collateral/ns340/ns394/ns171/ns441/networking_solutions_whitepaper0900aecd8072a537.html

  2. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of 1995 Int. Conf. Data Engineering (ICDE 1995), Taipei, Taiwan, pp. 3–14 (1995)

    Google Scholar 

  3. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering Frequent Episodes in Sequence. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, Montreal, Quebec, pp. 144–155 (1995)

    Google Scholar 

  4. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of ACM SIGMOD Int. Conf. Management of Data (1993)

    Google Scholar 

  5. Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings 20th International Conference on Very Large Databases, pp. 487–499 (1994)

    Google Scholar 

  6. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Dallas, Texas, United States (2000)

    Google Scholar 

  7. Cheung, W., Zaïane, O.: Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint. In: 7th International Database Engineering and Applications Symposium (IDEAS 2003), Hong Kong, China. IEEE Computer Society, Los Alamitos (2003)

    Google Scholar 

  8. http://nmap.org/book/man.html#man-description

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cabaj, K., Szczypiorski, K., Becker, S. (2010). Towards Self-defending Mechanisms Using Data Mining in the EFIPSANS Framework. In: Nguyen, N.T., Zgrzywa, A., Czyżewski, A. (eds) Advances in Multimedia and Network Information System Technologies. Advances in Intelligent and Soft Computing, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14989-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14989-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14988-7

  • Online ISBN: 978-3-642-14989-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics