An efficient optimal security system for intrusion detection in cloud computing environment using hybrid deep learning technique

Abstract

Users have been urged to embrace a cloud-based environment by recent technologies and advancements. Because of the dispersed nature of cloud solutions, security is a major problem. Because it is highly exposed to intruders for any kind of assault, security and privacy are major roadblocks to the on-demand service's success. A massive increase in network traffic has opened the ground for increasingly difficult and pervasive security vulnerabilities. Several intrusion detection systems (IDS) for cloud computing environments have recently been suggested. Current IDS may display over-fitting, low classification accuracy, and a high false positive rate when given with a large volume of variety of network data (FPR). We provide an effective optimal security solution for intrusion detection in a cloud computing environment using a hybrid deep learning algorithm in this study (EOS-IDS). Preprocessing is done using the improved heap optimization (IHO) technique, which assures data quality by removing unnecessary data from the dataset. Then, for optimum feature selection, we offer a chaotic red deer optimization (CRDO) technique, which is responsible for dimensionality reduction owing to large data. Then, for cloud attacks and intrusion detection and classification, a deep Kronecker neural network (DKNN) is shown. To illustrate its effectiveness, the proposed EOS-IDS strategy is evaluated against two benchmark datasets, DARPA IDS and CSE-CIC-IDS2018, and the results are compared to different existing IDS strategies.