Nothing Special   »   [go: up one dir, main page]

Skip to main content

Computational Intelligence for Network Intrusion Detection: Recent Contributions

  • Conference paper
Computational Intelligence and Security (CIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3801))

Included in the following conference series:

Abstract

Computational intelligence has figured prominently in many solutions to the network intrusion detection problem since the 1990s. This prominence and popularity has continued in the contributions of the recent past. These contributions present the success and potential of computational intelligence in network intrusion detection systems for tasks such as feature selection, signature generation, anomaly detection, classification, and clustering. This paper reviews these contributions categorized in the sub-areas of soft computing, machine learning, artificial immune systems, and agent-based systems.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Hofmann, A., Horeis, T., Sick, B.: Feature Selection for Intrusion Detection: An Evolutionary Wrapper Approach. In: Proc., International Joint Conference on Neural Networks (IJCNN 2004), pp. 1563–1568 (2004)

    Google Scholar 

  2. Siraj, A., Vaughn, R.B., Bridges, S.M.: Intrusion Sensor Data Fusion in an Intelligent Intrusion Detection System Architecture. In: Proc. Hawaii International Conference on System Sciences, pp. 902–911 (2004)

    Google Scholar 

  3. Valdes, A.: Detecting Novel Scans Through Pattern Anomaly Detection. In: Proc. DARPA Information Survivability Conference and Exhibition (DICEX 2003), pp. 140–151 (2003)

    Google Scholar 

  4. Sung, A.H., Mukkamala, S.: Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. In: Proc. Symposium on Applications and the Internet (SAINT 2003), pp. 209–216 (2003)

    Google Scholar 

  5. Dasgupta, D., Gonzalez, F.: An Immunity-based Technique to Characterize Intrusions in Computer Networks. IEEE Transactions on Evolutionary Computing 6(3), 281–291 (2002)

    Article  Google Scholar 

  6. Dasgupta, D., Brian, H.: Mobile Security Agents for Network Traffic Analysis. In: Proc. DARPA Information Survivability Conference and Exhibition, pp. 332–340 (2001)

    Google Scholar 

  7. Song, D., Haywood, M.I., Zincir-Heywood, A.N.: Training Genetic Programming on Half a Million Patterns: An Example from Anomaly Detection. IEEE Transactions on Evolutionary Computation 9(3), 225–239 (2005)

    Article  Google Scholar 

  8. Esponda, F., Forrest, S., Helman, P.: A Formal Framework for Positive and Negative Decision Schemes. IEEE Transactions on Systems, Man, and Cybernetics – Part B (Cybernetics) 34(1), 357–373 (2004)

    Article  Google Scholar 

  9. Gonzalez, F., Gomez, J., Kaniganti, M., Dasgupta, D.: An Evolutionary Approach to Generate Anomaly (Attack) Signatures. In: Proc. IEEE International Workshop on Information Assurance (IWIA 2003), pp. 251–259 (2003)

    Google Scholar 

  10. Seredynski, F.: Some Issues in Solving the Anomaly Detection Problem Using the Immunological Approach. In: Proc. IEEE International Parallel and Distributed Processing Symposium (IPDPS 2005), pp. 188–195 (2005)

    Google Scholar 

  11. Florez, G., Bridges, S.M., Vaughn, R.B.: An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection. Proc. North American Fuzzy Processing Society, 457–462 (2002)

    Google Scholar 

  12. Adeli, H., Karim, A.: Wavelets in Intelligent Transportation Systems. John Wiley & Sons, UK (2005)

    MATH  Google Scholar 

  13. Shah, H., Undercoffer, J., Joshi, A.: Fuzzy Clustering for Intrusion Detection. In: Proc. IEEE International Conference on Fuzzy Systems, pp. 1274–1278 (2003)

    Google Scholar 

  14. Gomez, J., Gonzalez, F., Dasgupta, D.: An Immuno-Fuzzy Approach to Intrusion Detection. In: Proc. IEEE International Conference on Fuzzy Systems, pp. 1219–1224 (2003)

    Google Scholar 

  15. Kim, J., Bentley, P.J.: Towards an Artificial Immune System for Network Intrusion Detection: An Investigation of Clonal Selection with a Negative Selection Operator. Proc. Congress on Evolutionary Computing, 1244–1252 (2001)

    Google Scholar 

  16. Li, J., Manikopoulos, C.: Early Statistical Anomaly Intrusion Detection of DoS Attacks Using MIB Traffic Parameters. In: Proc. IEEE International Workshop on Information Assurance (IWIA 2003), pp. 53–59 (2003)

    Google Scholar 

  17. Xin, J., Dickerson, J.E., Dickerson, J.A.: Fuzzy Feature Extraction and Visualization for Intrusion Detection. In: Proc. IEEE International Conference on Fuzzy Systems, pp. 1249–1254 (2003)

    Google Scholar 

  18. Shapiro, J.M., Lamont, G.B., Peterson, G.L.: An Evolutionary Algorithm to Generate Hyper-Ellipsoid Detectors for Negative Selection. In: Proc. GECCO 2005, pp. 337–344 (2005)

    Google Scholar 

  19. Lee, K., Mikhailov, L.: Intelligent Intrusion Detection System. In: Proc. IEEE International Conference on Intelligent Systems, pp. 497–502 (2004)

    Google Scholar 

  20. Anchor, K.P., Williams, P.D., Gunsch, G.H., Lamont, G.B.: The Computer Defense Immune System: Current and Future Research in Intrusion Detection. Proc. Congress on Evolutionary Computing, 1027–1032 (2002)

    Google Scholar 

  21. Zhou, L., Liu, F., Wu, J.: Research on Co-operative Computer Network Security Technologies. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics, pp. 1164–1168 (2004)

    Google Scholar 

  22. Middlemiss, M.J., Dick, G.: Weighted Feature Extraction Using a Genetic Algorithm for Intrusion Detection. Proc. Congress on Evolutionary Computing, 1669–1675 (2003)

    Google Scholar 

  23. Pillai, M.M., Eloff, J.H.P., Venter, H.S.: An Approach to Implement an Intrusion Detection System Using Genetic Algorithms. In: Proc. SAICSIT 2004, pp. 228–235 (2004)

    Google Scholar 

  24. Mahoney, M.V., Chan, P.K.: Learning Rules for Anomaly Detection of Hostile Network Traffic. In: Proc. IEEE International Conference on Data Mining (ICDM 2003), pp. 601–604 (2003)

    Google Scholar 

  25. Ye, N., Zhang, Y., Borror, C.M.: Robustness of the Markov Chain Model for Cyber-Attack Detection. IEEE Transactions on Reliability 53(1), 116–123 (2004)

    Article  Google Scholar 

  26. Amor, N.B., Benferhat, S., Elouedi, Z.: Naïve Bayes vs Decision Trees in Intrusion Detectin Systems. In: Handschuh, H., Hasan, M.A. (eds.) SAC 2004. LNCS, vol. 3357, pp. 420–424. Springer, Heidelberg (2004)

    Google Scholar 

  27. Miller, P., Inoue, A.: Collaborative Intrusion Detection System. Proc. North American Fuzzy Information Processing Society, 519–524 (2003)

    Google Scholar 

  28. Diaz-Gomez, P.A., Hougen, D.F.: Analysis and Mathematical Justification of a Fitness Function Used in an Intrusion Detection System. In: Proc. GECCO 2005, pp. 1591–1592 (2005)

    Google Scholar 

  29. Xue, Q., Guo, L., Sun, J.: The Design of a Distributed Network Intrusion Detection System IA-NIDS. In: Proc. International Conference on Machine Learning and Cybernetics, pp. 2305–2308 (2003)

    Google Scholar 

  30. Kemmerer, R.A., Vigna, G.: Intrusion Detection: A Brief History and Overview. IEEE Computer, 27–30 (2002)

    Google Scholar 

  31. Gong, R.H., Zulkernine, M., Abolmaesumi, P.: A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. In: Proc. International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing, pp. 246–253 (2005)

    Google Scholar 

  32. Chavan, S., Shah, K., Dave, N., Mukherjee, S.: Adaptive Neuro-Fuzzy Intrusion Detection Systems. In: Proc. International Conference on Information Technology: Coding and Computing (ITCC 2004), pp. 70–74 (2004)

    Google Scholar 

  33. Cho, S.: Incorporating Soft Computing Techniques into a Probabilistic Intrusion Detection System. IEEE Transactions on Systems, Man, and Cybernetics – Part C (Applications and Reviews) 32(2), 154–160 (2002)

    Article  Google Scholar 

  34. Makkamala, S., Sung, A.H.: Detecting Denial of Service Attacks Using Support Vector Machines. In: Proc. IEEE International Conference on Fuzzy Systems, pp. 1231–1236 (2003)

    Google Scholar 

  35. Sarasamma, S.T., Zhu, Q.A., Huff, J.: Hierarchical Kohonen Net for Anomaly Detection in Network Security. IEEE Transactions on Systems, Man, and Cybernetics – Part B (Cybernetics) 35(2), 302–312 (2005)

    Article  Google Scholar 

  36. Abbes, T., Bouhoula, A.A., Rusinowitch, M.: Protocol Analysis in Intrusion Detection using Decision Tree. In: Proc. International Conference on Information Technology, Coding, and Computing (ITCC 2004), pp. 404–408 (2004)

    Google Scholar 

  37. Ng, W., Chang, R., Yeung, D.: Dimensionality Reduction for Denial of Service Detection Problems Using RBFNN Output Sensitivity. In: Proc. International Conference on Machine Learning and Cybernetics, pp. 1293–1298 (2003)

    Google Scholar 

  38. Hang, X., Dai, H.: Applying Both Positive and Negative Selection to Supervised Learning for Anomaly Detection. In: Proc. GECCO 2005, pp. 345–352 (2005)

    Google Scholar 

  39. Zhang, X., Zhu, Z.: Combining the HMM and the Neural Network Models to Recognize Intrusions. In: Proc. International Conference on Machine Learning and Cybernetics, pp. 956–961 (2004)

    Google Scholar 

  40. Liu, Y., Tian, D., Wang, A.: ANNIDS: Intrusion Detection System Based on Artificial Neural Network. In: Proc. International Conference on Machine Learning and Cybernetics, pp. 1337–1342 (2003)

    Google Scholar 

  41. Xiaoping, Y., Yu, D.: An Auto-Configuration Cooperative Distributed Intrusion Detection System. Proc. World Congress on Intelligent Control and Automation, 279–283 (2004)

    Google Scholar 

  42. Anming, Z., Chunfu, J.: Study on the Applications of Hidden Markov Models to Computer Intrusion Detection. Proc. World Congress on Intelligent Control and Automation, 256–260 (2004)

    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-Verlag Berlin Heidelberg

About this paper

Cite this paper

Karim, A. (2005). Computational Intelligence for Network Intrusion Detection: Recent Contributions. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_25

Download citation

  • DOI: https://doi.org/10.1007/11596448_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30818-8

  • Online ISBN: 978-3-540-31599-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics