Hypervisor Based Anomaly Detection System in Cloud Computing Using ANFIS

Abstract

Cloud computing enables lots of users to make use of highly scalable, reliable large pool of computing and improved storage resources on demand through Internet. With uninterrupted improvement of cloud environment, the security measures against abnormal activities in public cloud are desirable to be exposed. This emerges significances to construct a component to discover anomalies in cloud. Hence the proposed scheme develops anomaly revealing system named Hypervisor spectator to detect irregularities on virtual network. The Hypervisor spectator is developed with Adaptive Neuro-Fuzzy Inference System (ANFIS) and accomplished using back propagation gradient descent technique in combination with least square method. This component has been trained and examined by using DARPA's KDD cup data set. The result of this work is considered according to training and testing performance. The performance comparisons in terms of false alarm rate and detection accuracy exhibit that proposed model is well designed to detect irregularities in cloud with least error rate and minimum overhead for very large datasets.