An Improved Intrusion Detection System to Preserve Security in Cloud Environment

Cloud computing, also known as on-demand computing, provides different kinds of services for the users. As the name suggests, its increasing demand makes it prone to various intruders affecting the privacy and integrity of the data stored in the cloud. To cope with this situation, intrusion detection systems (IDS) are implemented in the cloud. An effective IDS constitutes of less time-consuming algorithm with less space complexity and higher accuracy. To do so, the number of features are reduced while maintaining minimal loss of information. In this paper, the authors have proposed a model by which the features are selected on the basis of mutual information gain among correlated features. To achieve this, they first group the features according to the correlativity. Then from each group, the features with the highest mutual information gain in their respective groups are selected. This led them to a reduced feature set which provides quick learning and thus produces a better IDS that would secure the data in the cloud.

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