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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
Alauthman, Mohammad
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Computer Science & IT Electrical & Electronics Engineering

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Evaluation of machine learning and deep learning methods for early detection of internet of things botnets Mashaleh, Ashraf S.; Ibrahim, Noor Farizah; Alauthman, Mohammad; Al-karaki, Jamal; Almomani, Ammar; Atalla, Shadi; Gawanmeh, Amjad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4732-4744

Abstract

The internet of things (IoT) represents a rapidly expanding sector within computing, facilitating the interconnection of myriad smart devices autonomously. However, the complex interplay of IoT systems and their interdisciplinary nature has presented novel security concerns (e.g. privacy risks, device vulnerabilities, Botnets). In response, there has been a growing reliance on machine learning and deep learning methodologies to transition from conventional connectivity-centric IoT security paradigms to intelligence-driven security frameworks. This paper undertakes a comprehensive comparative analysis of recent advancements in the creation of IoT botnets. It introduces a novel taxonomy of attacks structured around the attack life-cycle, aiming to enhance the understanding and mitigation of IoT botnet threats. Furthermore, the paper surveys contemporary techniques employed for early-stage detection of IoT botnets, with a primary emphasis on machine learning and deep learning approaches. This elucidates the current landscape of the issue, existing mitigation strategies, and potential avenues for future research.
A classification model for predicting course outcomes using ensemble methods Al-Momani, Emad; Shatnawi, Ala'a; Almomani, Mohammad; Almomani, Ammar; Alauthman, Mohammad
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.pp7090-7102

Abstract

Educational data mining has sparked a lot of attention in latest years. Many machine learning methods have been suggested to discover hidden information from educational data. The extracted knowledge assists institutions in enhancing the effectiveness of teaching tactics and the quality of education. As a result, it improves students' performance and educational outputs overall. In this paper, a classification model was built to classify students' grades in a specific course into different categories (binary and multi-level classification tasks). The dataset contains features related to academic and non-academic information. The models were built using a variety of machine learning algorithms: decision tree (J48), support vector machine (SVM), and k-nearest neighbor (K-NN). Furthermore, ensemble methods (bagging, boosting, random subspace, and random forest) which combined multiple decision tree classifiers were implemented to improve the models' performance. The data set was modified under two stages: features selection method and data augmentation using a method called synthetic minority over sampling technique (SMOTE). Based on the results of the experiments, it is possible to predict the students' performance successfully by using machine learning algorithms and ensemble methods. Random subspace obtained the best accuracy at two-level classification task with modified data with 91.20%. At the three-level classification task, the best accuracy was obtained by random forest with 87.18%.
A stacked ensemble approach to identify internet of things network attacks through traffic analysis Rawashdeh, Adnan; Alkasassbeh, Mouhammd; Alauthman, Mohammad; Almseidin, Mohammad
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.v13i6.7811

Abstract

The internet of things (IoT) has increased exponentially in connected devices worldwide in recent years. However, this rapid growth also introduces significant security challenges since many IoT devices have vulnerabilities that can be exploited for cyber-attacks. Anomaly detection using machine learning algorithms shows promise for identifying abnormal network traffic indicative of IoT attacks. This paper proposes an ensemble learning framework for anomaly detection in IoT networks. A systematic literature review analyzes recent research applying machine learning for IoT security. Subsequently, a novel stacked ensemble model is presented, combining multiple base classifiers (random forest, neural network, support vector machine (SVM)) and meta-classifiers (gradient boosting) for improved performance. The model is evaluated on the IoTID20 dataset, using network traffic features to detect anomalies across binary, multi-class, and multi-label classifications. Experimental results demonstrate that the ensemble model achieved 99.7% accuracy and F1 score for binary classification, 99.5% accuracy for multi-class, and 91.2% accuracy for multi-label classification, outperforming previous methods. The model provides an effective anomaly detection approach to identify malicious activities and mitigate IoT security threats.
A general framework for metaverse based on parallel computing and HPC Al Khaldy, Mohammad Ali; Al-Qerem, Ahmad; Aldweesh, Amjad; Alauthman, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1895-1905

Abstract

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multi-client settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration.
Forecasting research influence: a recurrent neural network approach to citation prediction Jamal, Naser; Alauthman, Mohammad; Malhis, Muhannad; Ishtaiwi, Abdelraouf M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1070-1082

Abstract

As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Co-Authors Al Khaldy, Mohammad Ali Al-karaki, Jamal Al-Momani, Emad Al-Qerem, Ahmad Aldweesh, Amjad AlKasassbeh, Mouhammd Almomani, Ammar Almomani, Mohammad Almseidin, Mohammad Atalla, Shadi Gawanmeh, Amjad Ibrahim, Noor Farizah Ishtaiwi, Abdelraouf M. Jamal, Naser Malhis, Muhannad Mashaleh, Ashraf S. Rawashdeh, Adnan Shatnawi, Ala'a