Intrusion Classification for Cloud Computing Network: A Step Towards an Intelligent Classification System

Abstract

One of the most rapidly spreading areas of infor-mation technology is cloud computing. However, this raises sig-nificant security issues that entice burglars. This paper presents a machine learning-based framework for intrusion classification for cloud computing networks. It offers new capabilities derived from cloud network flow. By dividing the flow into windows of time, a method known as the Riemann Chunking Scheme computes these features. After experimenting with this dataset, we have extracted 40 features that best describe the problem of anomaly classification and improve the accuracy of the study on multilayer perceptron for anomaly classification in cloud network traffic