Skip to main content
SpringerLink
Log in

Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD

  • Conference paper
  • First Online:
Book cover Machine Learning and Intelligent Communications (MLICOM 2017)

Abstract

In the recent time a huge number of public and commercial service is used through internet so that the vulnerabilities of current security systems have become the most important issue in the society and threats from hackers have also increased. Many researchers feel intrusion detection systems can be a fundamental line of defense. Intrusion Detection System (IDS) is used against network attacks for protecting computer networks. On another hand, data mining techniques can also contribute to intrusion detection. The intrusion detection has two fundamental classes, Anomaly based and Misuse based. One of the biggest problem with the anomaly base intrusion detection is detecting a high numbers of false alarms. In this paper a solution is provided to increase the attack recognition rate and a minimal false alarm generation is achieved with the study of different Tree-based data mining techniques. KDD cup dataset is used for research purpose by using WEKA tool.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. https://www.sans.org/reading-room/whitepapers/detection/history-evolution-intrusion-detection344

  2. Denning, D.E.: An intrusion-detection model. IEEE Trans. Softw. Eng. SE-13(2), 222–232 (1987)

    Article  Google Scholar 

  3. Denning, D.E., Neumann, P.E.: Requirements and model for IDES-A real-time intrusion detection system. Technical report, Computer Science Laboratory, SRI International, Menlo Park, CA, USA (1985)

    Google Scholar 

  4. Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. J. Artif. Intell. Rev. 22, 85–126 (2004)

    Article  MATH  Google Scholar 

  5. Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches

    Google Scholar 

  6. Patcha, A., Park, J.: An overview of anomaly detection techniques. Existing solutions and latest technological trends

    Google Scholar 

  7. Bakar, Z., Mohemad, R., Ahmad, A., Deris, M.: A comparative study for outlier detection techniques in data mining

    Google Scholar 

  8. Hawkins, D.: Identification of Outliers. Monographs on Applied Probability and Statistics. Springer, Heidelberg (1980). https://doi.org/10.1007/978-94-015-3994-4

    Book  Google Scholar 

  9. Barnett, V., Lewis, T.: Outliers in Statistical Data. Wiley, Hoboken (1994)

    MATH  Google Scholar 

  10. Javits, H., Valdes, A.: “The NIDES statistical component” Description and justification. Technical report, SRI International, Computer Science Laboratory (1993)

    Google Scholar 

  11. Mahoney, M.: Network traffic anomaly detection based on packet bytes. In: Proceedings of ACMSAC (2003)

    Google Scholar 

  12. Mahoney, M., Chan, P.K.: Learning non stationary models of normal network traffic for detecting novel attacks. In: Proceedings of SIGKDD (2002)

    Google Scholar 

  13. Zhang, W., Yang, Q., Geng, Y.: A survey of anomaly detection methods in networks. In: Proceedings of International Symposium on Computer Network and Multimedia Technology, pp. 1–3, January 2009

    Google Scholar 

  14. Wu, S.X., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: a review (2010)

    Google Scholar 

  15. Dong, Y., Hsu, S., Rajput, S., Wu, B.: Experimental analysis of application level intrusion detection algorithms. Int. J. Secur. Netw. 5, 198–205 (2010)

    Article  Google Scholar 

  16. Yassin, W., Udzir, N., Abdullah, A.: Signature-based anomaly intrusion detection using integrated data mining classifiers. In: International Symposium on Biometrics and Security Technologies (ISBAST) (2014)

    Google Scholar 

  17. Liu, H., Motoda, H., Setiono, R.: Feature selection: an ever evolving frontier in data mining (2010)

    Google Scholar 

  18. Harbola, A., Harbola, J.: Improved intrusion detection in DDOS applying feature selection using rank & score of attributes in KDD-99 data set (2014)

    Google Scholar 

  19. Tavallaee, M., Baghe, E.: A detailed analysis of the KDD cup 99 data set (2009)

    Google Scholar 

  20. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html2

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, Jiangsu, China

    Mirza Khudadad & Zhiqiu Huang

Authors
  1. Mirza Khudadad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiqiu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirza Khudadad .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khudadad, M., Huang, Z. (2018). Intrusion Detection with Tree-Based Data Mining Classification Techniques by Using KDD. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73447-7_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73446-0

  • Online ISBN: 978-3-319-73447-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation