A new Deep Learning Based Intrusion Detection System for Cloud Security

Abstract

Cloud computing is used in many different research areas thanks to its high computing power and network capacity. Data security, cost-effectiveness, and flexibility of working options for remote workers have made this technology even more attractive today. Today, servers in cloud computing should protect themselves from threats more intelligently and provide security by preventing a new threat. A new deep learning model based on convolutional neural networks and recurrent neural networks for intrusion detection has been developed for cloud security in this study. The proposed model was trained and tested using NSL-KDD train dataset. With our deep learning model, any detected and not approved traffic is prevented from reaching the server in the cloud. The proposed system has 99.86% accuracy for five-class classification, which is the best result comparative to studies in the literature.