References

Ali, M.H. and M.F. Zolkipli, 2016. Review on hybrid extreme learning machine and genetic algorithm to work as intrusion detection system in cloud computing. ARPN. J. Eng. Appl. Sci., 11: 460-464.
Direct Link  |  

Armbrust, M., A.D. Joseph, R.H. Katz and D.A. Patterson, 2009. Above the clouds: A Berkeley view of cloud computing. MCs Thesis, EECS Department, University of California, Berkeley, California.

Baysa, D., R.M. Low and M. Stamp, 2013. Structural entropy and metamorphic malware. J. Comput. Virol. Hacking Tech., 9: 179-192.
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  |  

Cannady, J., 1998. Artificial neural networks for misuse detection. Proceedings of the Conference on National Information Systems Security, October 6-9, 1998, Hyatt Regency, Crystal City, Arlington, Virginia, pp: 368-381.

Chouhan, P., 2015. A survey: Analysis of current approaches in anomaly detection. Intl. J. Comput. Appl., 111: 32-36.
Direct Link  |  

Fossaceca, J.M., T.A. Mazzuchi and S. Sarkani, 2011. MARK-ELM: Application of a novel multiple kernel learning framework for improving the robustness of network intrusion detection. Expert Syst. Appl., 42: 4062-4080.
Direct Link  |  

Frank, J., 1994. Artificial intelligence and intrusion detection: Current and future directions. Proceedings of the 17th Conference on National Computer Security Conference Vol. 10, October 11-14, 1994, Baltimore Convention Center, Baltimore, Maryland, pp: 1-12.

Gong, R.H., M. Zulkernine and P. Abolmaesumi, 2005. A software implementation of a genetic algorithm based approach to network intrusion detection. Proceedings of the 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks, May 23-25, 2005, Towson, Maryland, USA., pp: 246-253.

Huang, G.B., Q.Y. Zhu and C.K. Siew, 2004. Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the IEEE International Joint Conference on Neural Networks, July 25-29, 2004, Budapest, Hungary, pp: 985-990.

Jaiganesh, V., S. Mangayarkarasi and P. Sumathi, 2013. Intrusion detection systems: A survey and analysis of classification techniques. Intl. J. Adv. Res. Comput. Commun. Eng., 2: 1629-1635.
Direct Link  |  

Kim, K.J. and S.J. Ahn, 2011. Proceedings of the International Conference on IT Convergence and Security 2011. Vol. 120, Springer, Netherlands, ISBN:978-94-007-2910-0, Pages: 642.

Kiranyaz, S., T. Ince, A. Yildirim and M. Gabbouj, 2009. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks, 22: 1448-1462.
CrossRef  |  Direct Link  |  

Kukar, M., 2012. Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining. In: Reliable Knowledge Discovery, Dai, H., J.N.K. Liu and E. Smirnov (Eds.). Springer, Berlin, Germany, pp: 3-27.

Kumar, U. and B.N. Gohil, 2015. A survey on intrusion detection systems for cloud computing environment. Intl. J. Comput. Appl., 109: 6-15.
CrossRef  |  Direct Link  |  

Li, G., P. Niu, X. Duan and X. Zhang, 2014. Fast learning network: A novel artificial neural network with a fast learning speed. Neural Comput. Appl., 24: 1683-1695.
Direct Link  |  

Madhavi, M., 2012. An approach for intrusion detection system in cloud computing. Intl. J. Comput. Sci. Inf. Technol., 3: 5219-5222.
Direct Link  |  

Mehmood, Y., U. Habiba, M.A. Shibli and R. Masood, 2013. Intrusion detection system in cloud computing: Challenges and opportunities. Proceedings of the 2nd National Conference on Information Assurance (NCIA), December 11-12, 2013, IEEE, Rawalpindi, Pakistan, ISBN:978-1-4799-1286-5, pp: 59-66.

Mishra, P., E.S. Pilli, V. Varadharajan and U. Tupakula, 2016. Author’s Accepted Manuscript. J. Netw. Comput. Appl., 77: 18-47.

Modi, C., D. Patel, B. Borisaniya, H. Patel and A. Patel et al., 2013. A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl., 36: 42-57.
CrossRef  |  Direct Link  |  

Nguyen, H.T., K. Franke and S. Petrovic, 2012. Feature Extraction Methods for Intrusion Detection Systems. In: Threats, Countermeasures and Advances in Applied Information Security, Manish, G. (Ed.). Information Science Reference Publisher, New York, USA., pp: 23-52.

Oktay, U. and O.K. Sahingoz, 2013. Attack types and intrusion detection systems in cloud computing. Proceedings of the 6th International Conference on Information Security & Cryptology, September 20-21, 2013, Gazi University, Ankara, Turkey, pp: 71-76.

Patel, A., M. Taghavi, K. Bakhtiyari and J.C. Junior, 2013. An intrusion detection and prevention system in cloud computing: A systematic review. J. Netw. Comput. Appl., 36: 25-41.
CrossRef  |  Direct Link  |  

Raghav, I., 2013. Intrusion detection and prevention in cloud environment: A systematic review. Intl. J. Comput. Appl., 68: 7-11.
Direct Link  |  

Shelke, M.P.K., M.S. Sontakke and A.D. Gawande, 2012. Intrusion detection system for cloud computing. Int. J. Sci. Technol. Res., 1: 67-71.
Direct Link  |  

Vieira, K., A. Schulter, C.B. Westphall and C.M. Westphall, 2010. Intrusion Detection for grid and cloud computing. IT Prof., 2: 38-43.
CrossRef  |  

Wang, J., W. Wu, Z. Li and L. Li, 2011. Convergence of gradient method for double parallel feedforward neural network. Intl. J. Numer. Anal. Model., 8: 484-495.
Direct Link  |  

Whitman, M.E. and H.J. Mattord, 2012. Principles of Information Security. Course Technology Publisher, Australia, Pages: 617.