Intrusion Detection in Cloud Computing environment using Neural Network

Zeenat Mahmood, Chetan Agrawal, Syed Shadab Hasan, Syeda Zenab


Abstract This research work proposes an approach for obtaining optimal number of features to build an efficient model for intrusion detection system (IDS). Feature reduction is commonly applied as a preprocessing step to overcome the curse of dimensionality. The cloud traffic data provided for the design of intrusion detection system always are large with ineffective information, thus there is need to remove the worthless information from the original high dimensional database. To improve the generalization ability, we usually generate a small set of features from the original input variables by feature extraction. This research work proposed a hybrid algorithm PCANNA (principal component analysis neural network algorithm) used to reduce the number of computer resources, both memory and CPU time required to detect attack. The PCA (principal component analysis) transform used to reduce the features and trained neural network is used to identify the any kinds of new attacks.

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