National Journal of System and Information Technology

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Improved Detection of Dos Attacks Using Intelligent Computation Techniques


     

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IDSs play a principal role in pro-actively detecting intrusions into enterprise-level computer networks, therefore the accuracy with which it performs this vital function is of paramount importance. Many studies have previously been conducted to improve upon proper classification of detections using neural networks and machine learning algorithms. We try to compare the performance of various intelligent computation techniques like Bayesian networks, Naive Bayesian, Logistic regression, RBF networks, Multi-Layer perception, SVMs with the SMO model, Kth nearest neighbour and Random forest in detecting DoS attack patterns. The data that was used to train and validate these techniques was obtained from the MIT Lincoln lab study into IDSs. The results obtained provide a clear comparison of the individual intelligent computation techniques ability in identifying and classifying attack patterns.

Keywords

Networks, Intrusion Detection, Denial of Service, Datasets, Data Mining, Bayesian Networks, Naive Bayesian, Logistic Regression, RBF Networks, Multi-layer Perception, Support Vector Machines, Sequential Minimal Optimization, Kth Nearest Neighbor, Random Forest
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  • Kruegal C., Valeur F., Vigna G., Kemmerer R., “Statefull intrusion detection for high speed networks”, In proceedings of IEEE Symposium on Security and Privacy, pp 285-294, May 2002

  • Mukkamala S., and Sung. A. H. (2003) A Comparative Study of Techniques for Intrusion Detection. Proceedings of 15th IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society Press, pp 570-579

  • K. Park, and H. Lee, “On the Effectiveness of Router-Based Packet Filtering for Distributed DoS attack and Prevention in Power-Law Internets”, Proc. of the SGICOMM, pp. 15-26, 2001

  • S. E. Webster, “The Development and Analysis of Intrusion Detection Algorithms”, S.M. Thesis, Massachusetts Institute of Technology, 1998

  • K. Kendall, “A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems”, Master's Thesis, Massachusetts Institute of Technology, 1998.

  • “Internet Protocol Specification”, IETF, RFC 791, September 1981

  • CERT Advisory CA-1998-01 Smurf IP Denial-of-Service Attacks http://www.cert.org/advisories/CA-1998-01.html, January 5, 1998

  • Jason Anderson, “An Analysis of Fragmentation Attacks”, March 2001

  • “Statistics: Methods and Applications”, Statsoft Publications

  • Vladimir V. N. (1995) The Nature of Statistical Learning Theory. Springer

  • Tommi Jaakkola, “Machine Learning: Bayesian networks, Support Vector Machines & Model selection”, MIT, 2006

  • Jia Li, “Logistical Regression”, Department of Statistics, University of Pennsylvania, 2000~

  • Ying So, “A Tutorial on Logisitc Regression”, SAS Institute, 2001

  • [John Platt, “Fast training of support vector machines using sequential minimal optimization,” Advances in kernel methods: support vector learning, Pages: 185 – 208, 1999

  • [Harp P.E., “Nearest neighbour pattern classification”. IEEE Transactions on Information Theory 13 (1): 21-27 (1967)

  • Leo Breiman, “Random Forests”, Machine Learning, pp5-32, Kluwer Academic Publishing, 2001.


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  • Improved Detection of Dos Attacks Using Intelligent Computation Techniques

Abstract Views: 243  |  PDF Views: 4

Authors

Abstract


IDSs play a principal role in pro-actively detecting intrusions into enterprise-level computer networks, therefore the accuracy with which it performs this vital function is of paramount importance. Many studies have previously been conducted to improve upon proper classification of detections using neural networks and machine learning algorithms. We try to compare the performance of various intelligent computation techniques like Bayesian networks, Naive Bayesian, Logistic regression, RBF networks, Multi-Layer perception, SVMs with the SMO model, Kth nearest neighbour and Random forest in detecting DoS attack patterns. The data that was used to train and validate these techniques was obtained from the MIT Lincoln lab study into IDSs. The results obtained provide a clear comparison of the individual intelligent computation techniques ability in identifying and classifying attack patterns.

Keywords


Networks, Intrusion Detection, Denial of Service, Datasets, Data Mining, Bayesian Networks, Naive Bayesian, Logistic Regression, RBF Networks, Multi-layer Perception, Support Vector Machines, Sequential Minimal Optimization, Kth Nearest Neighbor, Random Forest

References