Intrusion Detection Systems using Adaptive Regression
Splines
52. Srinivas Mukkamala,
Andrew H. Sung, Ajith Abraham, Vitorino Ramos, Intrusion Detection
Systems using Adaptive Regression Splines, in Enterprise Informations
Systems VI, Seruca, I.; Cordeiro, J.; Hammoudi, S.;
Filipe, J.
(Eds.), ISBN 1-4020-3674-4, Spinger-Verlag,
2005.
Figure - Multivariate Adaptative
Regression Splines (MARS) data estimation using splines and knots
(actual data on the rigth).
PDF file: paper (394 Kb)
Abstract: Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.
º He, Junbing Long, Dongyang Chen, Chuan, "An Improved Ant-based Classifier for Intrusion Detection", in ICNC 07, Third Int. conf. on Natural Computation, IEEE Press, pp. 819-823, Vol. 4, ISBN: 978-0-7695-2875-5, Aug. 2007.
º Wen-Fu Shih, "An Adaptive Anomaly Detection Method Based on Incremental Hidden Markov Model and Windows Native API", Master's Thesis, University of Taiwan, Sept. 2007.
º Yun Wang, Inyoung Kim, Gaston Mbateng and Shih-Yieh Ho, "A Latent class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Volume 30, Issue 1, pp. 93-100, 15 Dec. 2006.
º Tich Phuoc Tran; Jan, T., "Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection", in International Joint Conference on Neural Networks - IJCNN 06, pp. 2354-2361, IEEE Press, 2006.
º A. Lazarevic, V. Kumar, J. Srivastava, "Intrusion Detection: A Survey", in Managing Cyber Threats - Issues, Approaches, and Challenges, Massive Computing Series, Vol. 5, Part I, pp. 19-78, Springer, June 2006.
º Morteza Amini, Rasool Jalilia and Hamid Reza Shahriaria, "RT-UNNID: A practical solution to real-time Network-based Intrusion Detection using Unsupervised Neural Networks", in Computers & Security Journal, Vol. 25, Issue 6 , pp. 459-468, 2006.
º
º Srilatha Chebrolu, Ajith Abraham, Johnson P. Thomasa, "Feature Deduction and Ensemble Design of Intrusion Detection Systems", in Computers & Security Journal, Elsevier, 2005.
º Yuehui Chen, A. Abraham and Bo Yang, "Hybrid Flexible Neural Tree Based Intrusion Detection Systems", in IJIS - International Journal of Information Systems, 2005.
º Ajith Abraham and Johnson Thomas, "Distributed Intrusion Detection Systems: A Computational Intelligence Approach", in Applications of Information Systems to Homeland Security and Defense, Editors: Hussein A. Abbass and Daryl Essam, Idea Group Publishing, USA, 2005.
º Sandhya Peddabachigari, Ajith Abraham, Crina Grosan and Johnson Thomas, "Modeling Intrusion Detection System Using Hybrid Intelligent Systems", Journal of Network and Computer Applications, Elsevier Science, 2005.
º INGENIO Org - Final Report, Montevideo, Uruguay, July 2005 ( in http://www.ingenio.org.uy/ doc/ InformeFinal-06-07-2005.pdf).
º Konrad Rieck, "Maschinelles Lernen in hostbasierten Intrusion-Detection- Systemen" (in German), DIPLOMVORTRAG, Freie Universit at Berlin, Fachbereich Mathematik und Informatik, Institut fur Informatik, 13-7, 2004.
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PDF file: paper (394 Kb)
Abstract: Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.
Keywords: Network security, Intrusion Detection, Adaptive Regression Splines, Neural Networks, Support Vector Machines.
Cited by:º He, Junbing Long, Dongyang Chen, Chuan, "An Improved Ant-based Classifier for Intrusion Detection", in ICNC 07, Third Int. conf. on Natural Computation, IEEE Press, pp. 819-823, Vol. 4, ISBN: 978-0-7695-2875-5, Aug. 2007.
º Wen-Fu Shih, "An Adaptive Anomaly Detection Method Based on Incremental Hidden Markov Model and Windows Native API", Master's Thesis, University of Taiwan, Sept. 2007.
º Yun Wang, Inyoung Kim, Gaston Mbateng and Shih-Yieh Ho, "A Latent class Modeling approach to Detect Network Intrusion", in Computer Communications Journal, Volume 30, Issue 1, pp. 93-100, 15 Dec. 2006.
º Tich Phuoc Tran; Jan, T., "Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection", in International Joint Conference on Neural Networks - IJCNN 06, pp. 2354-2361, IEEE Press, 2006.
º A. Lazarevic, V. Kumar, J. Srivastava, "Intrusion Detection: A Survey", in Managing Cyber Threats - Issues, Approaches, and Challenges, Massive Computing Series, Vol. 5, Part I, pp. 19-78, Springer, June 2006.
º Morteza Amini, Rasool Jalilia and Hamid Reza Shahriaria, "RT-UNNID: A practical solution to real-time Network-based Intrusion Detection using Unsupervised Neural Networks", in Computers & Security Journal, Vol. 25, Issue 6 , pp. 459-468, 2006.
º
º Srilatha Chebrolu, Ajith Abraham, Johnson P. Thomasa, "Feature Deduction and Ensemble Design of Intrusion Detection Systems", in Computers & Security Journal, Elsevier, 2005.
º Yuehui Chen, A. Abraham and Bo Yang, "Hybrid Flexible Neural Tree Based Intrusion Detection Systems", in IJIS - International Journal of Information Systems, 2005.
º Ajith Abraham and Johnson Thomas, "Distributed Intrusion Detection Systems: A Computational Intelligence Approach", in Applications of Information Systems to Homeland Security and Defense, Editors: Hussein A. Abbass and Daryl Essam, Idea Group Publishing, USA, 2005.
º Sandhya Peddabachigari, Ajith Abraham, Crina Grosan and Johnson Thomas, "Modeling Intrusion Detection System Using Hybrid Intelligent Systems", Journal of Network and Computer Applications, Elsevier Science, 2005.
º INGENIO Org - Final Report, Montevideo, Uruguay, July 2005 ( in http://www.ingenio.org.uy/ doc/ InformeFinal-06-07-2005.pdf).
º Konrad Rieck, "Maschinelles Lernen in hostbasierten Intrusion-Detection- Systemen" (in German), DIPLOMVORTRAG, Freie Universit at Berlin, Fachbereich Mathematik und Informatik, Institut fur Informatik, 13-7, 2004.
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