Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning
Dublin Core
Title
Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning
Subject
Cloud computing
trojan Horse attacks
dynamic Analysis
detection
machine learning
Description
Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area.
Creator
Kanaker, Hasan
Abdel Karim, Nader
A.B. Awwad, Samer
H.A. Ismail, Nurul
Zraqou, Jamal
M. F. Al ali, Abdulla
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 24 (2022); pp. 81-106
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2022-12-20
Rights
Copyright (c) 2022 Dr. Hasan Kanaker, Dr.Nader Salameh , Dr.Samer A. B. Awwad , Dr. Nurul H.A. Ismail , Dr. Jamal Zraqou , Dr. Abdulla M. F. Al Ali
https://creativecommons.org/licenses/by/4.0
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Hasan Kanaker et al., Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning, International Association of Online Engineering (IAOE), Vienna, Austria, 2022, accessed November 4, 2024, https://igi.indrastra.com/items/show/2431