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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Hiari, Mohammad O.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Tuning the K value in K-nearest neighbors for malware detection M. Abualhaj, Mosleh; Abu-Shareha, Ahmad Adel; Shambour, Qusai Y.; Al-Khatib, Sumaya N.; Hiari, Mohammad O.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2275-2282

Abstract

Malicious software, also referred to as malware, poses a serious threat to computer networks, user privacy, and user systems. Effective cybersecurity depends on the correct detection and classification of malware. In order to improve its effectiveness, the K-nearest neighbors (KNN) method is applied systematically in this study to the task of malware detection. The study investigates the effect of the number of neighbors (K) parameter on the KNN's performance. MalMem-2022 malware datasets and relevant evaluation criteria like accuracy, precision, recall, and F1-score will be used to assess the efficacy of the suggested technique. The experiments evaluate how parameter tuning affects the accuracy of malware detection by comparing the performance of various parameter setups. The study findings show that careful parameter adjustment considerably boosts the KNN method's malware detection capability. The research also highlights the potential of KNN with parameter adjustment as a useful tool for malware detection in real-world circumstances, allowing for prompt and precise identification of malware.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
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.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.
Enhancing Spam Detection Using Hybrid of Harris Hawks and Firefly Optimization Algorithms Abualhaj, Mosleh M.; Shambour, Qusai Y.; Alsaaidah, Adeeb; Abu-Shareha, Ahmad; Al-Khatib, Sumaya; Hiari, Mohammad O.
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.279

Abstract

The emergence of the modern Internet has presented numerous opportunities for attackers to profit illegally by distributing spam mail. Spam refers to irrelevant or inappropriate messages that are sent on the Internet to numerous recipients. Many researchers use many classification methods in machine learning to filter spam messages. However, more research is still needed to assess using metaheuristic optimization algorithms to classify spam emails in feature selection. In this paper, we endorse fighting spam emails by employing a union of Firefly Optimization Algorithm (FOA) and Harris Hawks Optimization (HHO) algorithms to classify spam emails, along with one of the most well-known and efficient methods in this area, the Random Forest (RF) classifier. In this process, the experimental studies on the ISCX-URL2016 spam dataset yield promising results. For instance, the union of HHO and FOA, along with using an RF classifier, achieved an accuracy of 99.83% in detecting spam emails.
Comparative analysis of whale and Harris Hawks optimization for feature selection in intrusion detection Abualhaj, Mosleh M.; Hiari, Mohammad O.; Alsaaidah, Adeeb; Al-Zyoud, Mahran M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp179-185

Abstract

This research paper explores the efficacy of two nature-inspired optimization algorithms, the whale optimization algorithm (WOA) and Harris Hawks optimization (HHO), for feature selection in the context of intrusion detection and prevention systems (IDPS). Leveraging the NSL-KDD dataset as a benchmark, our study employs Python for implementation and uses decision tree (DT) as the classification model. The objective is to assess the impact of the HHO and WOA optimization techniques on the performance of IDPS through feature selection. The WOA and HHO techniques were able to lessen the features from 40 to 16 and 13, respectively. Results indicate that DT integrated with HHO achieves an impressive accuracy of 97.59%, outperforming the WOA-enhanced model, which attains an accuracy of 97.5%. This study contributes valuable insights into the comparative effectiveness of WOA and HHO optimization algorithms in enhancing the accuracy of IDPSs, shedding light on their potential applications in the realm of cybersecurity.
Co-Authors Abu-Shareha, Ahmad Abu-Shareha, Ahmad Adel Abualhaj, Mosleh M. Al-Khatib, Sumaya Al-Khatib, Sumaya N. Al-Shamayleh, Ahmad Sami Al-Zyoud, Mahran M. Alkhatib, Sumaya Nabil Alsaaidah, Adeeb Anbar, Mohammed M. Abualhaj, Mosleh Munther, Alhamza Shambour, Qusai Y.