Adaptive Model to Detect Anomaly and Real-Time Attacks in Cloud environment

Abstract

The potential benefits and enhancement of services have made cloud computing an attractive field in the current era. Cloud computing is widely utilized for the services and provisions provided by this environment, which entirely functions over the internet. It has various benefits and also grabbed a focal point among the researchers. Cloud service providers pose numerous security challenges and are highly susceptible to attacks. In the context of cloud computing, the anomalies and insiders attacks will deactivate the service providers, which results in the malfunctioning of the entire system. Traditional defense systems in the network are not efficient in handling insider attacks and intrusion. In this work, the anomaly identification technique is developed to identify the attack incidence, and the proposed approach uses the fuzzy min-max neural network (FMM-NN). The classification accuracy is enhanced by the effective identification of features using a neural network. The performance investigation and outcome of the FMM-NN identify and classify the real-time attacks in the cloud environment with high identification accuracy.