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Enhancing Cloud Security: A Novel Intrusion Detection System Using Deep Learning Algorithms

by Vijay Kumar Gandam, E. Aravind
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 44
Year of Publication: 2024
Authors: Vijay Kumar Gandam, E. Aravind
10.5120/ijca2024924070

Vijay Kumar Gandam, E. Aravind . Enhancing Cloud Security: A Novel Intrusion Detection System Using Deep Learning Algorithms. International Journal of Computer Applications. 186, 44 ( Oct 2024), 36-42. DOI=10.5120/ijca2024924070

@article{ 10.5120/ijca2024924070,
author = { Vijay Kumar Gandam, E. Aravind },
title = { Enhancing Cloud Security: A Novel Intrusion Detection System Using Deep Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 44 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 36-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number44/enhancing-cloud-security-a-novel-intrusion-detection-system-using-deep-learning-algorithms/ },
doi = { 10.5120/ijca2024924070 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:35.937971+05:30
%A Vijay Kumar Gandam
%A E. Aravind
%T Enhancing Cloud Security: A Novel Intrusion Detection System Using Deep Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 44
%P 36-42
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The intrinsic qualities of Cloud Computing (CC), including scalability and adaptability, have led to its adoption by several sectors. Nevertheless, cloud providers continue to face substantial challenges related to security, even though these benefits are undeniable. Unauthorized entrée, data breaches, and insider threats are some of the new dangers that CC introduces. Attackers find cloud systems appealing due to their common infrastructure. Tackling these security concerns requires the inclusion of strong security systems. Intrusion Detection Systems (IDS) are one such method that is essential for protecting cloud environments and networks. IDS keep tabs on every system and network activity. A lot of people have been looking at ways to improve IDS performance using ML and DL techniques as of late. Machine learning and deep learning algorithms have proven themselves capable of sifting through mountains of data and producing reliable forecasts. Using these methods, IDS can adjust to new threats, find past attacks, and cut down on false positives. This paper presents a new intrusion detection system (IDS) model that incorporates DL methods such as the Morlet Wavelet Kernel Function. An MLSTM classifier is suggested for the purpose of identifying breaches in the IoT-Cloud setting. Jarratt-Butterfly optimization algorithm (JBOA) selects the relevant features to increase classification accuracy. The suggested model is tested using known methodologies in terms of various parameters using the comprehensive intrusion dataset BoT-IoT. Through the use of simulations, the results prove that the suggested research classical outperforms the state-of-the-art models.

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System; Morlet Wavelet Kernel Function Long Short-Term Memory; Jarratt-Butterfly optimization algorithm; Intrusion Detection System; Cloud Computing; Deep Learning.

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