Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Kanaker, Hasan
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
Anonymization techniques for privacy-preserving data publishing: a comprehensive survey Smadi, Sami; Karim, Nader Abdel; Kanaker, Hasan; Abdulraheem, Waleed K.
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11224

Abstract

Data-driven innovation in healthcare, finance, and smart cities increasingly depends on sharing rich datasets, but such sharing raises severe privacy risks and regulatory challenges. Privacy-preserving data publishing (PPDP) seeks to release useful data while preventing re-identification and inference attacks. This paper presents a comprehensive survey of anonymization techniques for PPDP, spanning traditional models (k-anonymity, l-diversity, t-closeness, and pseudonymization) and modern approaches (differential privacy (DP), synthetic data generation, federated learning (FL), secure multi-party computation (SMPC), homomorphic encryption (HE), blockchain-based schemes, and quantum-safe cryptography). We propose a taxonomy that organizes these methods by privacy guarantees, data utility, scalability, and computational cost, and we provide a comparative analysis of their strengths, limitations, and typical application domains. The survey also reviews legal and ethical frameworks, with particular attention to general data protection regulation GDPR, health insurance portability and accountability act (HIPAA), and related regulations, and highlights emerging trends such as artificial intelligence (AI-driven) anonymization and privacy risks from large language models (LLMs) and quantum computing. Overall, the study shows that various techniques fail to protect all data scenarios so we need to create hybrid systems which will provide explainable anonymization solutions at different scales to protect privacy and maintain useful data utility.
Co-Authors Abdulraheem, Waleed K. Abuhamdeh, Maher Alhalhouli, Zaid Alhroob, Essam Alsaaidah, Adeeb Karim, Nader Abdel Qtaish, Osama Smadi, Sami Tarawneh, Monther