Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Journal of King Saud University - Computer and Information Sciences

Volumn 29, Issue 4, 2017, Pages 462-472

  Sumaiya Thaseen, Ikram ^a   Aswani Kumar, Cherukuri ^b  

^a VIT UNIVERSITY   (India)

^b VIT UNIVERSITY   (India)

Author keywords

Chi square feature selection; Cross validation; Intrusion detection; Radial basis kernel; Support vector machine; Variance

Indexed keywords

EID: 85006208829     PISSN: 13191578     EISSN: 22131248     Source Type: Journal    
DOI: 10.1016/j.jksuci.2015.12.004     Document Type: Article

References (38)

1. Amiri, Fatemeh, Mahdi, Mohammad, Yousefi, Rezaei, Mutual information based feature selection for intrusion detection systems. J. Network Comput. Appl. 34 (2011), 1184–1199.
2. Blake, C., Keogh, E., Merz, C., 1998. UCI repository of machine learning databases. Statlog Data Set, < http://www.ics.uci.edu/mlearn/MLRepository.html[online]>.
3. Chebrolu, S., Abraham, A., Thomas, P., Feature deduction and ensemble design of intrusion detection systems. Comput. Soc. 24:4 (2005), 295–307.
4. Farrahi, Vahid S., Ahmadzadeh, Marzieh, 2015. KCMC: a hybrid learning approach for network intrusion detection using K-means clustering and multiple classifiers. Int. J. Comput. Appl. 124 (9), pp. 18–23. Published by Foundation of Computer Science (FCS), NY, USA.
5. Fisch, D., Hofmann, A., Sick, B., On the versatility of radial basis function neural networks: a case study in the field of intrusion detection. Inf. Sci. 180 (2010), 2421–2439.
6. Horng, S.-J., Su, M.-Y., Chen, Y.-H., Kao, T.-W., Chen, R.-J., Lai, J.-L., Perkasa, C.D., A novel intrusion detection system using support vector machines. Expert Syst. Appl., 2010.
7. Hsu, Chih-Wei, Lin, Chih-Jen, A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks, 2002, 415–425.
8. Ilgun, K., Kemmerer, R.A., Porras, P.A., State transition analysis: a rule-based intrusion detection approach. IEEE Trans. Software Eng. 21:3 (1995), 181–199.
9. Kasliwal, Bhavesh, Bhatia, Shraey, Saini, Shubham, Sumaiya Thaseen, I., Aswani Kumar, Ch., 2014. A hybrid anomaly detection model using G-LDA. In: 2014 IEEE International Advance Computing Conference, pp. 288–293.
10. Khan, L., Awad, M., Thuraisingham, B., A new intrusion detection system using support vector machines and hierarchial clustering. J. Very Large Databases 16:4 (2007), 507–521.
11. Kim, G., Lee, S., Kim, S., A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst. Appl. 41:4 (2014), 1690–1700.
12. Kuang, Fangjun, Xu, Weihong, Zhang, Siyang, A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput., 2014.
13. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J., A comparative study of anomaly detection schemes in network intrusion detection., Proceedings of the third SIAM Conference on Data Mining, 2003.
14. Lee, W., Stolfo, S., Mok, K., A data mining framework for building intrusion detection model. Proc. IEEE Symposium on Security and Privacy, 1999, 120–132.
15. Lee, J.H., Lee, J.H., Sohn, S.G., Effective value of decision tree with KDD 99 intrusion detection datasets for intrusion detection system., 10th International Conference on Advanced Communication Technology (ICACT'08), 2008, 1170–1175.
16. Lin, W.-C., Ke, Shih-Wen, Tsai, Chih, CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-Based Syst. 78 (2015), 13–21.
17. Madzarov, Gjorgji, Gjorgjevikj, Dejan, Chorbev, Ivan, 2008. Multi class classification using support vector machines in binary tree architecture. In: International Scientific Conference.
18. Mukkamala, S., Sung, intelligent paradigms. J. Network Comput. Appl. 28:2 (2005), 167–182.
19. Nadiammai, G.V., Hemalatha, M., Effective approach toward intrusion detection system using data mining techniques. Egypt Inf. J. 15:1 (2014), 37–50.
20. Novikov, D., Yampolskiy, R.V., Reznik, L., 2006. Anomaly detection based intrusion detection. In: Proceedings of the Third International Conference on Information Technology: New Generations (Itng'06).
21. < http://nsl.cs.unb.ca/NSL-KDD/>.
22. Panda, Mrutyunjaya, Abraham, Ajith, Patra, Manas Ranjan, 2011. A hybrid intelligent approach for network intrusion detection. In: International Conference on Communication Technology and System Design, pp. 1–9.
23. Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J., Modelling intrusion detection system using hybrid intelligent systems. J. Comput. Network Appl. 30:1 (2007), 114–132.
24. Saniee Abadeh, M., Habibi, J., Lucas, C., Intrusion detection using a fuzzy genetics based learning algorithm. J. Network Comput. Appl. 30:1 (2007), 414–428.
25. Sarasamma, S.T., Zhu, Q.A., Huff, J., Hierarchical Kohonen net for anomaly detection in network security. IEEE Trans. Syst. Man Cybernetics –Part B Cybernetics 35 (2005), 302–312.
26. Saxena, Harshit, Richariya, Vineet, Intrusion detection in KDD99 dataset using SVM-PSO and feature reduction with information gain. Int. J. Comput. Appl. 98:6 (2014), 25–29.
27. Senthilnayaki, Balakrishnan, Venkatalakshmi, K., Kannan, A., Intrusion detection system using feature selection and classification technique. Int. J. Comput. Sci. Appl. 3:4 (2014), 145–151, 10.14355/ijcsa.2014.0304.02.
28. Shafi, K., Abbass, H.A., An adaptive genetic based signature learning system intrusion detection. Expert Syst. Appl. 36:10 (2009), 12036–12043.
29. Sumaiya Thaseen, I., Aswani Kumar, Ch., Intrusion detection model using fusion of PCA and optimized SVM. Proceedings of 2014 International Conference on Computing and Informatics (IC3I) held on 27–29 Mysore, India, 2014, 879–884.
30. Sumaiya Thaseen, I., Aswani Kumar, Ch., accepted for publication. Improving accuracy of intrusion detection model using PCA and optimized SVM. CIT J. Comput. Inf. Technol.
31. Sung, A., Mukkamala, S., Identifying important features for intrusion detection using support vector machines and neural networks. Proceedings of the International Symposium on Applications and the Internet (SAINT 2003), 2003, 209–217.
32. Toosi, A.N., Kahani, M., A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Comput. Commun. 30 (2007), 2201–2212.
33. Wang, G., Hao, J.X., Ma, J., Huang, L.H., A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37 (2010), 6225–6232.
34. Xuren, W., Famei, H., Rongsheng, X., Modeling intrusion detection system by discovering association rule in rough set theory framework. Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIM CA- IAWTIC06), 2006.
35. Yang, Yiming, Pedersen, Jan O., A comparative study on feature selection in text categorization. Proceedings of the 14th International Conference on Machine Learning (ICML), 1997, 412–420.
36. Ye, N., Borror, Y.Z.C.M., Robustness of the Markov chain model for cyber attack detection. IEEE Trans. Reliab., 116, 2004, 12353.
37. Zaman, Lightweight IDS based on feature selection and IDS classification scheme., Proceedings of 2009 International Conference on Computational Science and Engineering, 2009, 365–370.
38. Zhang, Z., Shen, H., Application of online-training SVMs for real-time intrusion detection with different considerations. Comput. Commun. 28:12 (2005), 1428–1442.