INTRUSION RECOGNITION USING NEURAL NETWORKS

Authors

  • Vladimir Golovko
  • Pavel Kochurko

DOI:

https://doi.org/10.47839/ijc.4.3.360

Keywords:

Neural networks, intrusion detection systems, network attacks, attack recognition

Abstract

Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.

References

. M. Bishop, S. Cheung, C. Wee, J. Frank, J. Hoagland and S. Samorodin. The treat from the Net, IEEE Spectrum, 34(8), pp. 56-53, 1993.

. D. Anderson, T. Frivold & A. Valdes. Next-generation Intrusion Detection Expert Systems (NIDES): A Summary, SRI International Technical Report SRI-CSL-95-07, 1995.

. E. Spafford and D. Zamboni. Data collection mechanisms for intrusion detection systems, CERIAS Technical Report 2000-08, CERIAS, Pardue University, 1315 Recitation Building, West Lafayette, IN, 2000.

. H. Debar, M. Becke & D.Simboni. A Neural Network Component for an Intrusion Detection System, In proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, (1992).

. C. Jirapummin and N. Wattanapongsakorn. Visual Intrusion Detection using Self-Organizing Maps, Proc. of Electrical and Electronic Conference (EECON-24), Thailand, Vol. 2, pp. 1343-1349, 2001.

. S.C. Lee and D.V. Heinbuch. Training a Neural Network Based Intrusion Detector to Recognize Novel Attacks, Information Assistance and Security, pp. 40-46, 2000.

. S. C. Lee, D. V. Heinbuch, Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks, IEEE Trans. on Systems, Man, and Cybernetics, Part A, 31, 2001, pp. 294-299.

. A. K. Ghosh and A. Schwartzbard, A Study in Using Neural Networks for Anomaly and Misuse Detection, In Proc. of the USENIX Security Symposium, August 23-26, 1999, Washington, USA.

. J. Cannady, An adaptive neural network approach to intrusion detection and response, PhD Thesis, School of Comp. and Inf. Sci., Nova Southeastern University,

. J. M. Bonifacio et al., Neural Networks applied in intrusion detection systems, Proc. of the IEEE World congress on Comp. Intell. (WCCI’98), 1998.

. K. Hornik, M. Stinchcombe, H. White, Multy-layer feedforward networks are universal approximators, Neural Networks, 2 pp. 359-366, 1989.

. A. Lukatsky. Intrusion detection. Saint-Petersburg: BHV-Peterburg, 2003.

. MIT Lincoln Laboratory -DARPA Intrusion Detection Evaluation Web Page Template. Training Data Attack Descriptions http://www.ll.mit.edu/IST/ideval/docs/1998/attacks.html

. 1999 KDD Cup Competition. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

. W. Lee and S. J. Stolfo. A framework for constructing features and models for intrusion detection systems, ACM Trans. on Inform. and System Security, 3(4), 200, 227-261.

. J. Cannady, Applying Neural Networks to Misuse Detection, In Proceedings of the 21st National Information Systems Security Conference.

. WinPcap: the Free Packet Capture Architecture for Windows, NetGroup, Politecnico di Torino (Italy), http://winpcap.polito.it, 1999-2004.

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Published

2014-08-01

How to Cite

Golovko, V., & Kochurko, P. (2014). INTRUSION RECOGNITION USING NEURAL NETWORKS. International Journal of Computing, 4(3), 37-42. https://doi.org/10.47839/ijc.4.3.360

Issue

2005, Volume 4, Issue 3

Section

Articles

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