International Journal of Science and Research (IJSR)
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, the authors propose a security framework which integrates a Network Intrusion Detection System (NIDS) in the Cloud infrastructure. They use snort and Bayesian classifier machine learning based techniques to implement this framework. To validate their approach, they evaluate the performance and detection efficiency of their 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.