Abstract

One of the major security challenges in cloud computing is the detection and prevention of intrusions and attacks. In order to detect and prevent malicious activities at the network layer, we propose a security framework which integrates a network intrusion detection system (NIDS) in the Cloud infrastructure. We use snort and Bayesian classifier machine learning based techniques to implement this framework. To validate our approach, we evaluate the performance and detection efficiency of our NIDS by using KDD experimental intrusion datasets. The results show that the proposed model has a higher detection rate with low false positives at an affordable computational cost

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