Evolutionary Optimization Algorithm with Deep Echo State Network for Anomaly Detection on Secure Cloud Computing Environment

Abstract

Cloud Computing (CC) is undoubtedly an indispensable technology across the world. It indicates a revolution in collaborative services and data storage. Yet, security problems have increased with the move to CC, which includes intrusion detection systems (IDS). Anomaly detection (AD) is a vital method that ensures the security of CC environments. It identifies unusual behaviour that specifies a security threat. AD is important in a secure CC environment for identifying breaches or attacks and monitoring system performance. The design of potential AD in a secure CC environment needs a combination of machine learning (ML) algorithms, continuous monitoring, and data analysis. Therefore, this article introduces a new Falcon Optimization Algorithm with Deep Echo State Network for Anomaly Detection (FOADESN-AD) technique for a secure CC environment. The presented FOADESN-AD technique exploits the DL model with a metaheuristic optimizer for anomaly or intrusion detection in the cloud platform. To accomplish this, the FOADESN-AD technique initially performs a Z-score normalization process. For anomaly detection, the FOADESN-AD technique uses the DESN classifier, which accurately detects the presence of anomalies in the cloud environment. Moreover, the FOA is utilized to finetune the hyperparameter values of the DESN model, achieving superior classification results. The performance analysis of the FOADESN-AD method is implemented on the CSE-CICIDS-2018 dataset. The experimental values stated the betterment of the FOADESN-AD method over other existing approaches.