Asian Journal of Information Technology
Year:
2016
Volume:
15
Issue:
5
Page No.
891 - 895
References
Abdelhamid, N., 2015. Multi-label rules for phishing classification. Applied Comput. Inf., 11: 29-46.
CrossRef | Direct Link | Al Snousy, M.B., H.M. El-Deeb, K. Badran and I.A. Al Khlil, 2011. Suite of decision tree-based classification algorithms on cancer gene expression data. Egypt. Inf. J., 12: 73-82.
CrossRef | Direct Link | Altwaijry, H. and S. Algarny, 2012. Bayesian based intrusion detection system. J. King Saud Univ. Comput. Inf. Sci., 24: 1-6.
CrossRef | Direct Link | Barani, F., 2014. A hybrid approach for dynamic intrusion detection in ad hoc networks using genetic algorithm and artificial immune system. Proceedings of the 2014 Iranian Conference on Intelligent Systems, February 4-6, 2014, Bam, pp: 1-6.
Brown, I. and C. Mues, 2012. An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Applic., 39: 3446-3453.
CrossRef | Direct Link | Eesa, A.S., Z. Orman and A.M.A. Brifcani, 2015. A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst. Applic., 42: 2670-2679.
CrossRef | Direct Link | Ganapathy, S., K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, P. Yogesh and A. Kannan, 2013. Intelligent feature selection and classification techniques for intrusion detection in networks: A survey. EURASIP J. Wireless Commun. Network. 10.1186/1687-1499-2013-271
Ganapathy, S., P. Vijayakumar, P. Yogesh and A. Kannan, 2016. An intelligent CRF based feature selection for effective intrusion detection. Int. Arab J. Inf. Technol., 10: 44-50.
Direct Link | Ganapathy, S., P. Yogesh and A. Kannan, 2012. Intelligent agent-based intrusion detection system using enhanced multiclass SVM. Comput. Intell. Neurosci. 10.1155/2012/850259
Ghosh, S., S. Biswas, D. Sarkar and P.P. Sarkar, 2014. A novel neuro-fuzzy classification technique for data mining. Egypt. Inf. J., 15: 129-147.
CrossRef | Direct Link | Gupta, K.K., B. Nath and R. Kotagiri, 2010. Layered approach using conditional random fields for intrusion detection. IEEE Trans. Dependable Secure Comput., 7: 35-49.
CrossRef | Juhola, M., H. Joutsijoki, H. Aalto and T.P. Hirvonen, 2014. On classification in the case of a medical data set with a complicated distribution. Applied Comput. Inf., 10: 52-67.
CrossRef | Direct Link | Laurentys, C.A., R.M. Palhares and W.M. Caminhas, 2011. A novel artificial immune system for fault behavior detection. Expert Syst. Applic., 38: 6957-6966.
CrossRef | Direct Link | Nadiammai, G.V. and M. Hemalatha, 2014. Effective approach toward intrusion detection system using data mining techniques. Egypt. Inf. J., 15: 37-50.
CrossRef | Direct Link | Shakshuki, E.M., N. Kang and T.R. Sheltami, 2013. EAACK-A secure intrusion-detection system for MANETs. IEEE Trans. Ind. Electron., 60: 1089-1098.
CrossRef | Direct Link | Wang, J.J.Y., J.Z. Huang, Y. Sun and X. Gao, 2015. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization. Expert Syst. Applic., 42: 1278-1286.
CrossRef | Direct Link |