Journal of Engineering and Applied Sciences

Year: 2016
Volume: 11
Issue: 14
Page No. 3254 - 3264

Optimize Machine Learning Based Intrusion Detection for Cloud Computing: Review Paper

Authors : Mohammed Hasan Ali, Mohamad Fadli Zolkipli and Mohammed Abdulameer Mohammed

References

Alexandre, E., L. Cuadra, S. Salcedo-Sanz, A. Pastor-Sanchez and C. Casanova-Mateo, 2015. Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications. Neurocomputing, 152: 58-68.
Direct Link  |  

Alharkan, T. and P. Martin, 2012. IDSaaS: Intrusion detection system as a service in public clouds. Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), May 13-16, 2012, IEEE, New York, USA., ISBN:978-0-7695-4691-9, pp: 686-687.

Aslahi-Shahri, B.M., R. Rahmani, M. Chizari, A. Maralani and M. Eslami et al., 2016. A hybrid method consisting of GA and SVM for intrusion detection system. Neural Comput. Appl., 27: 1669-1676.

Bartlett, P.L., 1998. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE. Transac. Inf. Theor., 44: 525-536.

Cao, J., Z. Lin, G.B. Huang and N. Liu, 2012. Voting based extreme learning machine. Inf. Sci., 185: 66-77.
Direct Link  |  

Cao, J.J., S. Kwong, R. Wang and K. Li, 2012. A weighted voting method using minimum square error based on Extreme Learning Machine. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), July 15-17, 2012, IEEE, New York, USA., ISBN:978-1-4673-1484-8, pp: 411-414.

Castano, A., F.F. Navarro, A. Riccardi and C.H. Martinez, 2016. Enforcement of the principal component analysis-extreme learning machine algorithm by linear discriminant analysis. Neural Comput. Appl., 27: 1749-1760.
CrossRef  |  Direct Link  |  

Choo, K.K.R., 2011. The cyber threat landscape: Challenges and future research directions. Comput. Secur., 30: 719-731.
Direct Link  |  

Chorowski, J., J. Wang and J.M. Zurada, 2014. Review and performance comparison of SVM-and ELM-based classifiers. Neurocomputing, 128: 507-516.
Direct Link  |  

Ding, S., X. Xu and R. Nie, 2014. Extreme learning machine and its applications. Neural Comput. Appl., 25: 549-556.
CrossRef  |  Direct Link  |  

Egele, M., T. Scholte, E. Kirda and C. Kruegel, 2012. A survey on automated dynamic malware-analysis techniques and tools. ACM Comput. Surveys, Vol. 44. 10.1145/2089125.2089126

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  |  

Fu, H., C.M. Vong, P.K. Wong and Z. Yang, 2016. Fast detection of impact location using kernel extreme learning machine. Neural Comput. Appl., 27: 121-130.
CrossRef  |  Direct Link  |  

Huang, G.B., H. Zhou, X. Ding and R. Zhang, 2012. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42: 513-529.
CrossRef  |  Direct Link  |  

Huang, W., N. Li, Z. Lin, G.B. Huang and W. Zong et al., 2013. Liver tumor detection and segmentation using kernel-based extreme learning machine. Proceedings of the 35th Annual International Conference on Engineering in Medicine and Biology Society (EMBC), July 3-7, 2013, IEEE, New York, USA., ISBN:978-1-4577-0215-0, pp: 3662-3665.

Jaiganesh, V. and P. Sumathi, 2012. Kernelized extreme learning machine with levenberg-marquardt learning approach towards intrusion detection. Intl. J. Comput. Appl., 54: 38-44.
Direct Link  |  

Jang, J.S.R. and E. Mizutani, 1996. Levenberg-Marquardt method for ANFIS learning. Proceedings of the Conference on North American Fuzzy Information Processing Society, June 19-22, 1996, IEEE, New York, USA., ISBN:0-7803-3225-3, pp: 87-91.

Lee, W. and S. Stolfo, 2000. A framework for constructing features and models for intrusion detection systems. ACM Trans. Inform. Syst. Secur. J., 3: 227-261.
CrossRef  |  Direct Link  |  

Liang, N.Y., G.B. Huang, P. Saratchandran and N. Sundararajan, 2006. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE. Trans. Neural Netw., 17: 1411-1423.
CrossRef  |  Direct Link  |  

Man, Z., K. Lee, D. Wang, Z. Cao and S. Khoo, 2012. Robust single-hidden layer feedforward network-based pattern classifier. IEEE. Trans. Neural Netw. Learn. Syst., 23: 1974-1986.
CrossRef  |  Direct Link  |  

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  |  

Silva, D.N., L.D. Pacifico and T.B. Ludermir, 2011. An evolutionary extreme learning machine based on group search optimization. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), June 5-8, 2011, IEEE, New York, USA., ISBN:978-1-4244-7834-7, pp: 574-580.

Singh, R., H. Kumar and R.K. Singla, 2015. An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Syst. Appl., 42: 8609-8624.
Direct Link  |  

Tavallaee, M., E. Bagheri, W. Lu and A.A. Ghorbani, 2009. A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2nd IEEE Symposium on Computational Intelligence for Security and Defence Applications, July 8-10, 2009, IEEE, Ottawa, Ontario, pp: 1-7.

Tsai, C.F., Y.F. Hsu, C.Y. Lin and W.Y. Lin, 2009. Intrusion detection by machine learning: A review. Exp. Syst. Applic., 36: 11994-12000.
CrossRef  |  Direct Link  |  

Wang, Q., C. Wang, J. Li, K. Ren and W. Lou, 2009. Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing. In: Computer Security. Backes, M. and P. Ning (Eds.). Springer Berlin Heidelberg, Berlin, Germany, ISBN: 978-3-642-04443-4, pp: 355-370.

Wang, Y., F. Cao and Y. Yuan, 2011. A study on effectiveness of extreme learning machine. Neurocomputing, 74: 2483-2490.
Direct Link  |  

Wozniak, M., M. Grana and E. Corchado, 2014. A survey of multiple classifier systems as hybrid systems. Inf. Fusion, 16: 3-17.
Direct Link  |  

Xue, X., M. Yao, Z. Wu and J. Yang, 2014. Genetic ensemble of extreme learning machine. Neurocomputing, 129: 175-184.
Direct Link  |  

Zhai, J., Q. Shao and X. Wang, 2016. Architecture selection of ELM networks based on sensitivity of hidden nodes. Neural Process. Lett., 44: 471-489.
CrossRef  |  Direct Link  |  

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