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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Abu-Khadrah, Ahmed
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Computer Science & IT Electrical & Electronics Engineering

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Windows operating system malware detection using machine learning Hilabi, Rawabi; Abu-Khadrah, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8018

Abstract

Over the years, cybercriminals have become more sophisticated in manipulating network users. Malware is a popular tool they use to exploit victims, targeting valuable assets such as identities and credit cards in the realm of digital technology. Cybersecurity professionals are consistently innovating to detect malicious activities. Machine learning (ML) algorithms are now a leading method for rapidly identifying unseen malware, offering efficiency and intelligence beyond traditional approaches. In fact, attackers like to see the victims suffer from damage caused by malware. Malware can destroy devices and networks. Additionally, hackers can blackmail individuals and organizations to obtain money through ransomware. Therefore, the aim of this research is developing a new model that has the capability of detecting malwares that are targeting Windows operating systems (OS) through enhancing an existing model by deploying several ML algorithms which are extreme gradient boosting (XGB) and random forest (RF). In addition, the swarm optimization and ML applied to portable executable (SOMLAP) dataset applied in the portable executable (PE) is used for training data and testing these learning algorithms. The result achieved by XGB and RF hybrid technique accuracy was 0.966, precision 0.990 and recall was 0.918.
Enhance the accuracy of malicious uniform resource locator detection based on effective machine learning approach Alqahtani, Haifa; Abu-Khadrah, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7797

Abstract

Phishing attacks are increasing with the rise in web users. Addressing them requires understanding the techniques and employing effective response strategies. Phishing websites mimic authentic ones to deceive users into divulging personal information like bank account details, national insurance numbers, and passwords. Therefore, victims face financial loss from breached information security, constituting high-level internet fraud. Detecting phishing websites necessitates an intelligent model capable of recognizing suspicious features. To that purpose, this paper examines three classification methods for detecting phishing website attacks. This analysis allows to reconsider our awareness of phishing attacks and prevent the damage caused by phishing attempts in advance. Phishing website detection algorithm using three classification algorithms is proposed in this paper. It achieves high phishing website detecting accuracy, because three classification algorithms random forest (RF), support vector machine (SVM), and Bagging are combined in one system. The result of this research is found accuracy on validation set is 92.33%, the precision on validation set is 92.13%, the recall is 92.09% and F1 score is 92.10%. That prove that the result obtained in this research is more accurate than all the results of all the algorithms were applied in the same dataset that was train the proposed algorithm on it.
Cyber-fraud detection methodology by using machine learning algorithms Abu-Khadrah, Ahmed; Al-Washmi, Sahar; Mohd Ali, Ali; Jarrah, Muath
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3949-3956

Abstract

Cybercrime covers a wide array of illegal online activities such as hacking and identity theft, while cyber fraud specifically involves deceptive practices like phishing and fraudulent financial transactions. The rise in technology and digital communication has exacerbated cyber fraud. Although prevention technologies are advancing, fraudsters continually adapt, making effective detection methods essential for identifying and addressing fraud when prevention fails. The proposed model aims to reduce online fraud through new detection algorithms. It utilizes statistical and machine learning techniques, including logistic regression, random forest, and naïve Bayes, to identify non-transactional fraud behaviors. By analyzing a meticulously collected and fine-tuned dataset, the study enhances detection capabilities beyond traditional transaction-focused approaches. The algorithms monitor user interactions and device characteristics to create profiles of normal behaviors and detect deviations indicative of fraud. The evaluation of proposed model showed 100% accuracy. A unified model incorporating all decision-making processes was used, leading to a voting phase and accuracy assessment. This approach consolidates multiple algorithms into a single framework, proving highly effective for comprehensive fraud detection. The research demonstrates the value of integrating machine learning techniques with real-world data to advance fraud detection and emphasizes the importance of continual adaptation to address evolving cyber threats.
A hybrid machine learning approach for malicious website detection and accuracy enhancement Abu-Khadrah, Ahmed; Alkhamis, Shayma; Ali, Ali Mohd; Jarrah, Muath
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1027-1034

Abstract

Malicious URLs are web addresses purposely generated for a user’s detriment. Some examples include phishing scams in which the victim is fooled into logging into a fake site or portals for downloading malware where any click on a link invites a hostile program to the user’s device. The damage done to an individual’s finances, confidential information, and even reputation due to malicious URLs makes it crucial to devise means of countering these threats. This can be achieved by creating an intelligent model that identifies suspicious characteristics common to these websites. The objective of this research is to design a novel hybrid machine learning algorithm-based model for detecting malicious websites. A random forest, decision tree, and extreme gradient boosting (XGBoost) are the three hybrid classification algorithms proposed for the study. Accuracy in detection will help prevent and reduce the effects of such websites. The accuracy rate in this research is 98.7%, precision is at 98.9%, and recall at 98.5%. With these results, it follows that the hybrid model is more effective than training any individual algorithm with the given dataset.
Co-Authors Al-Washmi, Sahar Ali, Ali Mohd Alkhamis, Shayma Alqahtani, Haifa Hilabi, Rawabi Jarrah, Muath Mohd Ali, Ali