Open Access

ARTICLE

Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

by Abdullah M. Basahel¹, Mohammad Yamin¹, Sulafah M. Basahel², E. Laxmi Lydia^3,*

1 Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia
2 E-commerce Department, College of Administrative and Financial Sciences, Saudi Electronic University, Jeddah, Saudi Arabia
3 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India

* Corresponding Author: E. Laxmi Lydia. Email:

Computers, Materials & Continua 2023, 74(2), 4319-4336. https://doi.org/10.32604/cmc.2023.033497

Received 18 June 2022; Accepted 19 August 2022; Issue published 31 October 2022

Abstract

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The current mainstream IDS are assisted with machine learning (ML) but have issues of low detection rates and demanded wide feature engineering. This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security (ECODL-IDSCS) model. The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic (ADASYN) technique. For detecting and classification of intrusions, long short term memory (LSTM) model is exploited. In addition, ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment. Once the presented ECODL-IDSCS model is tested on benchmark dataset, the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

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Keywords

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