Skip to main content
SpringerLink
Log in

Data Mining Based Network Intrusion Detection System: A Survey

  • Conference paper
  • First Online:
Novel Algorithms and Techniques in Telecommunications and Networking

Abstract

Significant security problem for networked systems is hostile trespass by users or software. Intruder is one of the most publicized threats to security. Network Intrusion Detection Systems (NIDS) have become a standard component in network security infrastructures. This paper presents the features of signature based NIDS in addition to the current state-of-the-art of Data Mining based NIDS approaches. Moreover, the paper provides general guidance for open research areas and future directions. The intention of this survey is to give the reader a broad overview of the work that has been done at the intersection between intrusion detection and data mining.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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. Zhu, Dan, Premkumar, G, Zhang, Xiaoning, Chu, Chao-Hsien (2001) A comparison of alternative methods. [Online] Available from: http://findarticles.com/p/articles/mi_qa3713/is_200110/ai_n8954240.

  2. Marinova V,(2007) A Short Survey of Intrusion Detection Systems*, problems of engineering cybernetics and robotics, 58.

    Google Scholar 

  3. Brugger ,T(June 9, 2004) University of California, Davis Data Mining Methods for Network Intrusion Detection 1...56.

    Google Scholar 

  4. Kuok C., Fu A., Wong M.,(2001) “Mining fuzzy association rules in databases” SIGMOD Record 17 (1) 41–46.

    Google Scholar 

  5. Julisch, K. & Dacier, M. (2002). Mining Intrusion Detection Alarms for Actionable Knowledge. Proc. of SIGKDD02, 366–375.

    Google Scholar 

  6. Dunham M (2003) Data mining Introductory and advance Topics, Pearson Education. Inc.

    Google Scholar 

  7. Forrest, S., S. A. Hofmeyr, and A. Somayaji (1997, October). Computer immunology. Communications of the ACM 40 (10), 88–96.

    Article  Google Scholar 

  8. Hofmeyr, S. A. and S. Forrest (1999). Immunizing computer networks: Getting all the machines in your network to fight the hacker disease. In Proc. of the 1999 IEEE Symp. on Security and Privacy, Oakland, CA. IEEE Computer Society Press.

    Google Scholar 

  9. Dokas P., Ertoz L.(2002), Data Mining for Network intrusion detection ,- Proc.NFS workshop on next generation data mining , csee.umbc.edu , 21–29.

    Google Scholar 

  10. Lee, W. and S. J. Stolfo (2000). A framework for constructing features and models for intrusion detection systems. Information and System Security 3 (4), 227–261.

    Article  Google Scholar 

  11. Chandola V, Eilertson E, Ertoz L, Simon G, and Kumar V,, Data Mining for Cyber Security,(2006) Data Warehousing and Data Mining Techniques for Computer Security, editor Anoop Singhal, Springer.

    Google Scholar 

  12. Lee, W. K. W. Mok, and S. J. Stolfo(1998). Mining sequential patterns: Techniques, visualization, and applications. Submitted for publication, August 1998.1–9.

    Google Scholar 

  13. Ert¨oz, L , Eilertson, E, Aleksandar Lazarevic, Pang-Ning Tan_ , Vipin Kumar (2004) MINDS - Minnesota Intrusion Detection System , Technical report at university of Minnesota 1..21.

    Google Scholar 

  14. Chittur, A. (2001). Model generation for an intrusion detection system using genetic algorithms. High School Honors Thesis, Ossining High School. In cooperation with Columbia University, 3 – 19.

    Google Scholar 

  15. Neri, F. (2000a, 16–19 July). Comparing local search with respect to genetic evolution to detect intrusion in computer networks. In Proc. of the 2000 Congress on Evolutionary Computation CEC00, La Jolla, CA, pp. 238– 243. IEEE Press.

    Google Scholar 

  16. Fan, W. (2001). Cost-Sensitive, Scalable and Adaptive Learning Using Ensemble- based Methods. Ph. D. thesis, Columbia Univ.

    Google Scholar 

  17. Yeung, D.-Y. And C. Chow (2002, 11–15 August). Parzen- window network intrusion detectors. In Proc. of the Sixteenth International Conference on Pattern Recognition, Volume 4, Quebec City, Canada, pp. 385–388. IEEE Computer Society.

    Google Scholar 

  18. Peng, †. T, Zuo, W,(February 2006) IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.2B.

    Google Scholar 

  19. Phua1C, lee1 V, Smith1 K & ross gayler2, A Comprehensive Survey of Data Mining-based Fraud Detection Research Final version 2: 9/02/2005

    Google Scholar 

  20. Mukkamala, S., A. H. Sung, and A. Abraham (2002). Identifying key variables for intrusion detection using soft computing. http://citeseer.nj.nec.com/544845.html.

  21. Cho, S. (2002). Incorporating Soft Computing Techniques into a Probabilitistic Intrusion Detection System. IEEE Transactions on Systems, Man and Cybernetics 32(2): 154–160.

    Article  Google Scholar 

  22. Lee, W., S. J. Stolfo, P. K. Chan, E. Eskin, W. Fan, M. Miller, S. Hershkop, and J. Zhang (2001, June). Real time data mining- based intrusion detection. In Proc. Second DARPA Information Survivability Conference and Exposition, Anaheim, CA, pp. 85–100. IEEE Computer Society.

    Google Scholar 

  23. Jiawei, H, and Micheline Kamber(2001). Data Mining:Concepts and Techniques. Higher Educa-tion Press,3–10

    Google Scholar 

  24. Sequeira, K. & Zaki, M. (2002). ADMIT: Anomaly-based Data Mining for Intrusions. Proc. of SIGKDD02, 386–395.

    Google Scholar 

  25. Fortuna c, Fortuna b, mohorčič m,(2007) anomaly detection in computer networks using linear svms

    Google Scholar 

  26. Hawkins, S., He, H., Williams, G. & Baxter, R. (2002). Outlier Detection Using Replicator Neural Networks. Proc. of DaWaK2002, 170–180.

    Google Scholar 

  27. Williams, G., Baxter, R., He, H. & Hawkins, S. (2002). A Comparative Study of RNN for Outlier Detection in Data Mining. Proc. of ICDM02, 709–712.

    Google Scholar 

  28. Lee W. Salvatore J. Stolfo Kui W. Mok.(1999) A Data Mining Framework for Building Intrusion Detection Models (1This research is supported in part by grants from DARPA (F30602–96-1–0311) and NSF (IRI-96–32225 and CDA-96–25374).submitted to the 1999 IEEE Symposium on Security and Privacy.

    Google Scholar 

  29. Lane, T. & Brodley, C. (2003). An Empirical Study of Two Approaches to Sequence Learning for Anomaly Detection. Machine Learning 51:73–107.

    Article  MATH  Google Scholar 

  30. Didaci, L., G. Giacinto, and F. Roli (2002). Ensemble learning for intrusion detection in computer networks. http://citeseer.nj.nec.com/533620.html.

  31. Bloedorn E, Alan D. Christiansen, William Hill, Clement Skorupka, Lisa M. Talbot, and Jonathan Tivel(2002). Data mining for network intrusion detection: How to get started. Technical report, The MITRE Corporation, 2001. 1–9.

    Google Scholar 

  32. Minnesota university, Minnesota Intrusion Detection System.[Online] Available from: www.cs.umn.edu/research/MINDS [Accessed 15 November 2007] .

  33. Rajeswari, L. Prema; Kannan, A., (4–6 Jan. 2008) An Intrusion Detection System Based on Multiple Level Hybrid Classifier using Enhanced C4.5 , Communications and Networking, 2008. ICSCN apos, International Conference, Page(s):75 – 79.

    Google Scholar 

  34. Idris , N, Shanmugam ,B, (2006) Novel Attack Detection Using Fuzzy Logic and Data Mining. Security and Management: 26–31.

    Google Scholar 

  35. Prasad G, Dhanalakshmi Y, Dr.Vijaya V Kumar Dr Babu R, Modeling An Intrusion Detection System Using Data Mining And Genetic Algorithms Based On Fuzzy Logic, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.7, July 2008.

    Google Scholar 

  36. Dhanalakshmi and Babu,(February 2008) Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.2.

    Google Scholar 

  37. Cheung-Leung Lui   Tak-Chung Fu  Ting-Yee Cheung Agent-based network intrusion detection system using data mining approaches, Information Technology and Applications, 2005. ICITA 2005.Publication Date: 4–7 July 2005: 131- 136 vol.1, ISBN: 0–7695-2316–1.

    Google Scholar 

  38. Bartoš K, Grill M, Krmíˇcek V, Rehák M, Celeda P,(July 2008) Flow Based Network Intrusion Detection System using Hardware-Accelerated NetFlow Probes, abriela Krˇcmaˇrová, Petr Sojka (Eds.): CESNET Conference 2008, Proceedings, pp. 49–56.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Information Technology, Sudan University of Science and Technology, Morgan, Khartoum, Sudan

    Rasha G. Mohammed Helali

Authors
  1. Rasha G. Mohammed Helali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rasha G. Mohammed Helali .

Editor information

Editors and Affiliations

  1. School of Engineering, University of Bridgeport, University Avenue 221, Bridgeport, 06604, USA

    Tarek Sobh

  2. School of Engineering, University of Bridgeport, University Avenue 221, Bridgeport, 06604, USA

    Khaled Elleithy

  3. School of Engineering, University of Bridgeport, University Avenue 221, Bridgeport, 06604, USA

    Ausif Mahmood

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this paper

Cite this paper

Helali, R.G.M. (2010). Data Mining Based Network Intrusion Detection System: A Survey. In: Sobh, T., Elleithy, K., Mahmood, A. (eds) Novel Algorithms and Techniques in Telecommunications and Networking. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3662-9_86

Download citation

  • DOI: https://doi.org/10.1007/978-90-481-3662-9_86

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3661-2

  • Online ISBN: 978-90-481-3662-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation