Abstract
In federation of cloud, multiple cloud service providers share their resources based on certain assumptions and trust. Most of the times, identity of a user and permission to give access are shared using service access requirements. In intercloud security, building mutual trust relationship between multiple cloud federated identities is the main goal. Securing the components involved in cloud federation can be helpful in building mutual trust relationship between federated identities. Intrusion Detection and Prevention techniques helped a lot in detecting the malicious activities performed by the intruders. In this domain of securing data, a lot of research is being done. Recently, artificial intelligence and machine learning have greatly attracted the attention of researchers to integrate the concepts of network security with artificial intelligence. In this research, we have studied artificial intelligence techniques and finalized artificial neural network (ANN) model to detect the intrusions based on anomalies. In previous studies, it was discussed that major challenges in anomaly based intrusion detection systems is to lessen the false positive rate (FPR). So, in this research the main emphasis is done on reducing the failure rate of FPR of the intrusions done to the system while maintaining the overall perfromance and accuracy of the proposed model.