References

Amrita and P. Ahmed, 2012. A study of feature selection methods in intrusion detection system: A survey. Int. J. Comput. Sci. Eng. Inf. Technol. Res., 2: 1-25.

Araujo, N., D.R. Oliveira, A.A. Shinoda and B. Bhargava, 2010. Identifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach. Proceedings of the IEEE 17th International Conference on Telecommunications (ICT), April 4-7, 2010, IEEE, Cuiaba, Brazil, ISBN:978-1-4244-5246-0, pp: 552-558.

Bhaya, W. and M.E. Manaa, 2014. A proactive DDoS attack detection approach using data mining cluster analysis. J. Next Gener. Inf. Technol., 5: 36-47.
Direct Link  |  

Bhuyan, M.H., D.K. Bhattacharyya and J.K. Kalita, 2014. Network anomaly detection: methods, systems and tools. IEEE. Commun. Surv. Tutorials, 16: 303-336.
CrossRef  |  Direct Link  |  

Bjerkestrand, T., D. Tsaptsinos and E. Pfluegel, 2015. An evaluation of feature selection and reduction algorithms for network IDS data. Proceedings of the International Conference on Cyber Situational Awareness Data Analytics and Assessment (CyberSA), June 8-9, 2015, IEEE, London, UK., ISBN:978-0-9932-3380-7, pp: 1-2.

Chebrolu, S., A. Abraham and J.P. Thomas, 2005. Feature deduction and ensemble design of intrusion detection systems. Comput. Secur., 24: 295-307.
CrossRef  |  Direct Link  |  

Chen, Y., K. Hwang and W.S. Ku, 2007. Collaborative detection of DDoS attacks over multiple network domains. IEEE Trans. Parallel Distrib. Syst., 18: 1649-1662.
CrossRef  |  

Chen, Y., Y. Li, X.Q. Cheng and L. Guo, 2006. Survey and taxonomy of feature selection algorithms in intrusion detection system. Proceedings of the International Conference on Information Security and Cryptology, November 29- December 1, 2006, Springer, Berlin, Germany, ISBN:978-3-540-49608-3, pp: 153-167.

Cheng, J., J. Yin, Y. Liu, Z. Cai and M. Li, 2009. DDoS attack detection algorithm using IP address features. Proceedings of the International Workshop on Frontiers in Algorithmics, June 20-23, 2009, Springer, Berlin, Germany, ISBN:978-3-642-02269-2, pp: 207-215.

Chou, T.S., K.K. Yen and J. Lou, 2008. Network intrusion detection design using feature selection of soft computing paradigms. Int. J. Comput. Intell., 4: 196-200.

Depren, O., M. Topallar, E. Anarim and M.K. Ciliz, 2005. An intelligent Intrusion Detection System (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl., 29: 713-722.
CrossRef  |  

Eid, H.F., A.E. Hassanien, T.H. Kim and S. Banerjee, 2013. Linear Correlation-Based Feature Selection for Network Intrusion Detection Model. In: Advances in Security of Information and Communication Networks, Ismail, A.A., E.H. Aboul and B. Kensuke (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-40596-9, pp: 240-248.

Garg, T. and S.S. Khurana, 2014. Comparison of classification techniques for intrusion detection dataset using WEKA. Proceedings of the IEEE Conference on Recent Advances and Innovations in Engineering (ICRAIE), May 9-11, 2014, IEEE, Bathinda, India, ISBN:978-1-4799-4040-0, pp: 1-5.

Garg, T. and Y. Kumar, 2014. Combinational feature selection approach for network intrusion detection system. Proceedings of the 2014 International Conference on Parallel Distributed and Grid Computing (PDGC), December 11-13, 2014, IEEE, Kapurthala, India, ISBN:978-1-4799-7682-9, pp: 82-87.

Gavrilis, D. and E. Dermatas, 2005. Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features. Comput. Networks, 48: 235-245.
CrossRef  |  

Ghali, N.I., 2009. Feature selection for effective anomaly-based intrusion detection. Int. J. Comput. Sci. Network Security, 9: 285-289.
Direct Link  |  

Heady, R., G. Luger, A. Maccabe and M. Servilla, 1990. The architecture of a network level intrusion detection system. Technical Report, Computer Science Department, University of New Mexico.

Kim, D.S., S.M. Lee and J.S. Park, 2006. Building lightweight intrusion detection system based on random forest. Proceedings of the International Symposium on Neural Networks, May 28-June 1, Springer, Berlin, Germany, ISBN:978-3-540-34482-7, pp: 224-230.

Kumar, M.S., 2016. A survey on improving classification performance using data pre processing and machine learning methods on NSL-KDD data. Int. J. Eng. Comput. Sci., 5: 16156-16161.
Direct Link  |  

Lakhina, A., M. Crovella and C. Diot, 2005. Mining anomalies using traffic feature distributions. ACM Sigcomm Comput. Commun. Rev., 35: 217-228.
CrossRef  |  Direct Link  |  

Lee, W. and S. Stolfo, 1998. Data mining approaches for intrusion detection. Proceedings of the 7th USENIX Security Symposium, January 26-29, 1998, USENIX Association, Berkeley, CA., USA., pp: 79-94.

Li, Y., J. Xia, S. Zhang, J. Yan, X. Ai and K. Dai, 2012. An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst. Appl., 39: 424-430.
CrossRef  |  Direct Link  |  

Lin, S.W., K.C. Ying, C.Y. Lee and Z.J. Lee, 2012. An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl. Soft Comput., 12: 3285-3290.
CrossRef  |  Direct Link  |  

Mamory, S.O.A. and F.S. Jassim, 2015. On the designing of two grains levels network intrusion detection system. Karbala Int. J. Mod. Sci., 1: 15-25.
Direct Link  |  

Moustafa, N. and J. Slay, 2015. Creating novel features to anomaly network detection using DARPA-2009 data set. Proceedings of the 14th European Conference on Cyber Warfare and Security, July 2-3, 2015, ACPI Publisher, Hatfield, England, UK., ISBN:978-1-910810-28-6, pp: 204-207.

Oyebode, E.O., S.G. Fashoto, O.A. Ojesanmi, O.E. Makinde and O. State, 2011. Intrusion detection system for computer network security 1. Aust. J. Basic Appl. Sci., 5: 1317-1320.

Patcha, A. and J.M. Park, 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Networks, 51: 3448-3470.
CrossRef  |  

Peng, T., C. Leckie and K. Ramamohanarao, 2004. Proactively detecting distributed denial of service attacks using source IP address monitoring. Proceedings of the 3rd International IFIP-TC6 Networking Conference on Networking Technologies, Services and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications, May 9-14, 2004, Athens, Greece, pp: 771-782.

Rahmani, H., N. Sahli and F. Kammoun, 2009. Joint entropy analysis model for DDoS attack detection. Proceedings of the 5th International Conference on Information Assurance and Security, Volume 2, August 18-20, 2009, Xian, China, pp: 267-271.

Ranjbar, L. and S. Khorsandi, 2011. A collaborative intrusion detection system against ddos attack in peer to peer network. Proceedings of the International Conference on Software Engineering and Computer Systems, June 27-29, 2011, Springer, Berlin, Germany, ISBN:978-3-642-22202-3, pp: 353-367.

Saad, R., N.F. Abdesselam and A. Serhrouchni, 2008. A collaborative peer-to-peer architecture to defend against DDoS attacks. Proceedings of the 2008 33rd IEEE Conference on Local Computer Networks (LCN), October 14-17, 2008, IEEE, Lille, France, ISBN:978-1-4244-2412-2, pp: 427-434.

Sengar, H., X. Wang, H. Wang, D. Wijesekera and S. Jajodia, 2009. Online detection of network traffic anomalies using behavioral distance. Proceedings of the 17th International Workshop on Quality of Service (IWQoS), July 13-15, 2009, IEEE, Fairfax, Virginia, ISBN: 978-1-4244-3875-4, pp: 1-9.

Sheen, S. and R. Rajesh, 2008. Network intrusion detection using feature selection and decision tree classifier. Proceedings of the IEEE Region 10 Conference TENCON, November 19-21, 2008, Hyderabad, pp: 1-4.

Sindhu, S.S.S., S. Geetha and A. Kannan, 2012. Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst. Applic., 39: 129-141.
CrossRef  |  Direct Link  |  

Singh, H. and D. Kumar, 2015. A study on performance analysis of various feature selection techniques in intrusion detection system. Int. J., 3: 50-54.
Direct Link  |  

Song, J., Z. Zhu, P. Scully and C. Price, 2013. Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification. In: Cyberspace Safety and Security, Guojun, W. I. Ray, D. Feng and M. Rajarajan (Eds.). Springer, Berlin, Germany, ISBN:978-3-319-03583-3, pp: 299-307.

Srihari, V. and R. Anitha, 2014. DDoS detection system using wavelet features and semi-supervised learning. Proceedings of the International Symposium on Security in Computing and Communication, September 24-27, 2014, Springer, Berlin, Germany, ISBN:978-3-662-44965-3, pp: 291-303.

Tang, D., K. Chen, X. Chen, H. Liu and X. Li, 2014. A new collaborative detection method for LDoS attacks. J. Networks, 9: 2674-2681.

Wang, W., Y. He, J. Liu and S. Gombault, 2015. Constructing important features from massive network traffic for lightweight intrusion detection. IET. Inf. Secur., 9: 374-379.
CrossRef  |  Direct Link  |  

Yao, D., M. Yin, J. Luo and S. Zhang, 2012. Network anomaly detection using random forests and entropy of traffic features. Proceedings of the 2012 Fourth International Conference on Multimedia Information Networking and Security, November 2-4, 2012, IEEE, Zhengzhou, China, ISBN:978-1-4673-3093-0, pp: 926-929.

Zaina, A., M.A. Maarof and S.M. Shamsuddin, 2006. Feature selection using rough set in intrusion detection. Proceedings of the TENCON 2006 IEEE Region of 10 Conference, November 14-17, 2006, Teknologi Malaysia, Johor, pp: 1-4.

Zhang, J., M. Zulkernine and A. Haque, 2008. Random-forests-based network intrusion detection systems. IEEE. Trans. Syst. Man Cybernetics Part C Appl. Rev., 38: 649-659.
CrossRef  |  Direct Link  |  

Zhong, R. and G. Yue, 2010. DDoS detection system based on data mining. Proceedings of the 2nd International Symposium on Networking and Network Security, April 2-4, 2010, Jinggangshan, China, pp: 62-65.