A Cloud based Anomaly Detection Model using Machine Learning Algorithm
Cloud Computing has developed as a recent technology in IT business as a major segment, whereas security concerns remain the essential hindrance to full-scale appropriation. Introduction of cloud based architectures reducing the computing cost has led to the usage of cloud computing. As a result, individual users to large scale organizations, they are turning their attention towards the cloud to store the huge volume of data. But security is one of the major challenges in cloud data storage. In this paper, we have proposed an optimal classification algorithm to classify the anomalies in the data. For optimal classification, we have used Random Forest (RF) classification algorithm with feature selection by Chi-square algorithm. To improve the performance of the classification task, we have proposed a node selection algorithm through a load balancing technique. The experimental validation was carried out with four publicly available sensor datasets from Intel lab. The results showed that the proposed system has high accuracy in anomaly detection.