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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Mosleh M. Abualhaj
Al-Ahliyya Amman University
Computer Science & IT Electrical & Electronics Engineering

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Using decision tree classifier to detect Trojan Horse based on memory data Mosleh M. Abualhaj; Sumaya N. Al-Khatib
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.25753

Abstract

Trojan Horse is a major threat that has grown with the spread of the digital world. Data gathered through the study of memory can provide valuable insights into the Trojan Horse’s behavior patterns. Because of this, memory analysis techniques are one of the topics that should be investigated in Trojan Horse detection. This study proposes the use of memory data in Trojan Horse detection. Trojan Horse detection used a decision tree (DT) classifier with memory data. Experiments were performed on the Trojan Horse samples from the CIC-MalMem-2022 dataset. The binary classification was made using DT, gradient boosted tree, Naive Bayes (NB), linear vector support machine, K-nearest neighbors (KNN), and machine learning (ML) classifiers. The comparison of the various classification methods was performed utilizing the accuracy, recall, precision, and F1-score metrics. As a result, the most successful Trojan Horse detection was gained with the DT classifier, which achieved accuracy of 99.96% using memory data. The NB classifier showed the lowest achievement in Trojan Horse detection using memory data, which achieved accuracy of 98.41%. In addition, numerous of the classifiers utilized have attained very high results. Based on the achieved results, the data from memory analysis is very valuable in detecting Trojan Horse.
Enhancing spam detection using Harris Hawks optimization algorithm Mosleh M. Abualhaj; Sumaya Nabil Alkhatib; Ahmad Adel Abu-Shareha; Adeeb M. Alsaaidah; Mohammed Anbar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 2: April 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i2.26615

Abstract

This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.
A firewall model for attack detection using machine learning and metaheuristic feature selection algorithms Mosleh M. Abualhaj; Sumaya Nabil Al-Khatib; Nida Al-Shafi; Mohammad O. Hiari; Mohammad Sh. Daoud; Mohammed Anbar; Mahran M. Al-Zyoud
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.v15i2.9887

Abstract

This research presents a firewall model designed to enhance network attack detection by integrating machine learning (ML) and advanced feature selection techniques. The study introduces a union-based (DAUBA) feature selection method that combines the exploratory capability of the Dragonfly Algorithm (DA) with the exploitation efficiency of the Bat Algorithm (BA). By combining these two bio-inspired optimizers, the method generates complementary feature subsets that enhance both accuracy and efficiency. The proposed DA?BA feature selection method is incorporated into a ML–based firewall and evaluated on the UNSW-NB15 dataset using three classifiers: adaptive boosting (AdaBoost), K-nearest neighbor (KNN), and Naïve Bayes (NB). Experimental results demonstrate that the approach achieves near-perfect accuracy (100% with AdaBoost), along with strong precision, recall, and F1-scores, while maintaining computational costs compatible with real-time deployment. These findings highlight the novelty and practical value of combining DA and BA in feature selection for next-generation firewall systems.
Co-Authors Adeeb M. Alsaaidah Ahmad Adel Abu-Shareha Mahran M. Al-Zyoud Mohammad O. Hiari Mohammad Sh. Daoud Mohammed Anbar Nida Al-Shafi Sumaya N. Al-Khatib Sumaya Nabil Al-Khatib Sumaya Nabil Alkhatib