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All Journal International Journal of Electrical and Computer Engineering
Alhalhouli, Zaid
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Computer Science & IT Electrical & Electronics Engineering

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A comparative study of machine learning tools for detecting Trojan horse infections in cloud computing environments Kanaker, Hasan; Tarawneh, Monther; Karim, Nader Abdel; Alsaaidah, Adeeb; Abuhamdeh, Maher; Qtaish, Osama; Alhroob, Essam; Alhalhouli, Zaid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6642-6655

Abstract

Cloud computing offers several advantages, including cost savings and easy access to resources, it is also could be vulnerable to serious security attacks such as cloud Trojan horse infection attacks. To address this issue, machine learning is a promising approach for detecting these threats. Thus, different machine learning tools and models have been employed to detect Trojan horse infection such as Weka and Python Colab. This study aims to compare the performance of Weka and Python Colab, as popular tools for building machine learning models. This study evaluates the recall, accuracy, and F1-score of machine learning models built with Weka and Python Colab and compares their computational resources required employing several machine learning algorithms. The dataset collected and analyzed using dynamic analysis of Trojan horse infection in control lab environment. The findings of this study can help determine the decision about which tool to use to detect Trojan horse infections and provide insights into the strengths and limitations of Weka and Python Colab for building machine-learning models in general.
Co-Authors Abuhamdeh, Maher Alhroob, Essam Alsaaidah, Adeeb Kanaker, Hasan Karim, Nader Abdel Qtaish, Osama Tarawneh, Monther