Abstract
There is a constant rise in the number of hacking and intrusion incidents day by day due to the alarming growth of internet. Intrusion Detection Systems (IDS) monitors network activities to protect the system from cyber attacks. Anomaly detection models identify the deviations from normal behavior and classify them as anomalies. In this paper we propose an intrusion detection model using Linear Discriminant Analysis (LDA), chi square feature selection and modified Naïve Bayesian classification. LDA is one of the extensively used dimensionality reduction technique to remove noisy attributes from the network dataset. As there are many attributes in the network data, chi square feature selection is deployed in an efficient manner to identify the optimal feature set that increases the accuracy of the model. The optimal subset is then used by the modified Naïve Bayesian classifier for identifying the normal traffic and different attacks in the data set. Experimental analysis have been performed on the widely used NSL-KDD datasets. The results indicate that they hybrid model produces better accuracy and lower false alarm rate in comparison to the traditional approaches.
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References
Hussein, S.M., Ali, F.H.M., Kasiran, Z.: Evaluation effectiveness of hybrid IDS using snort with Naïve Bayes to detect attacks. In: 2012 Second International Conference on Digital Information and Communication Technology and its applications (DICTAP), pp. 256–260, May 2012
Pu, W., Jun-qing, W.: Intrusion detection system with the data mining technologies. In: 2011 IEEE 3rd International Conference on Communication Software and Networks, pp. 490–492
Muda, Z., Yassin, W., Sulaiman, M.N., Udzir, N.I.: Intrusion detection based on K-Means clustering and Naïve Bayes classification. In: 2011 7th IEEE International Conference on IT in Asia (CITA), pp. 278–284
Panda, M., Patra, M.: Ensemble rule based classifiers for detecting network intrusion detection. In: 2009 International Conference on Advances in Recent Technology in Communication and Computing, ArtCom ’09, pp. 19–22
Karthick, R.R., Hattiwale, V.P., Ravindran, B.: Adaptive network intrusion detection system using a hybrid approach. 2012 IEEE Fourth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–7, Jan 2012
Muda, Z., Yassin, W., Sulaiman, M.N., Udzir, N.I.: A K-mean and Naïve Bayes learning approach for better intrusion detection. Inf. Technol. J. 10(3), 648–655 (2011)
Barot, V., Toshniwal, D.: A new data mining based hybrid network intrusion detection model. In: 2012 IEEE International Conference on Data Science & Engineering (ICDSE), pp. 52–57
Ferid, D.M.D., Harbi, N.: Combining Naïve Bayes and decision tree for adaptive intrusion detection. Int. J. Netw. Secur. Appl. (IJNSA) 2, 189–196 (2010)
Kasliwal, B., Bhatia, S., Saini, S., Thaseen, I.S., Kumar, C.A.: A hybrid anomaly detection model using G-LDA. In: 2014 IEEE International Advance Computing Conference, pp. 288–293
Mukherjee, S., Sharma, N.: Intrusion detection using Naïve Bayes classifier with feature reduction. C3IT-2012 Procedia Technol 4, 119–128 (2012)
Thaseen, I.S., Kumar, Ch.A.: Improving accuracy of intrusion detection model using PCA and optimized SVM. CIT J. Comput. Inf. Technol. (Accepted manuscript, 2015)
Thaseen, I.S., Kumar, Ch.A.: Intrusion detection model using fusion of PCA and optimized SVM. In: 2014 International Conference on Computing and Informatics (IC3I), pp. 879-884, 27–29 Nov 2014
Martinez, A., Kak, A.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
Baek, K., Draper, B., Beveridge, J.R., She, K.: PCA vs. ICA: A comparison on the FERET data set. In: Proceedings of the fourth international conference on computer vision, pattern recognition and image processing, Durham, NC, USA, pp. 824–827, 8–14 Mar 2002
Delac, Kresimir, Grgic, Mislav, Grgic, Sonja: Independent comparative study of PCA, ICA and LDA on the FERET data set. Int. J. Imaging Syst. Technol. 15(5), 252–260 (2005)
Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: Proceedings of IEEE 7th International Conference on Tools with Artificial Intelligence, pp. 358–391, 1995
Panda, M., Patra, M.R.: Network intrusion detection using Naïve Bayes. Int. J. Comput. Sci. Netw. Secur.7(12), (2007)
KDD. (1999). Available at http://kdd.ics.uci.edu/databases/-kddcup99/kddcup99.html
Panda, M., Abraham, A., Patra, M.R.: Discriminative multi nomial Naïve Bayes for network intrusion detection. In: 2010 Sixth International Conference on Information Assurance and Security, pp. 5–10
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDDCUP’1999 dataset. In: IEEE Symposium on Computational Intelligence in Security and Defence Application, 2009
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Thaseen, I.S., Kumar, C.A. (2016). Intrusion Detection Model Using Chi Square Feature Selection and Modified Naïve Bayes Classifier. In: Vijayakumar, V., Neelanarayanan, V. (eds) Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC – 16’). Smart Innovation, Systems and Technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-30348-2_7
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DOI: https://doi.org/10.1007/978-3-319-30348-2_7
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