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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Abualhaj, Mosleh M.
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : Journal of Applied Data Sciences

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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.
Performance Comparison of Whale and Harris Hawks Optimizers with Network Intrusion Prevention Systems Abualhaj, Mosleh M.; Al-Khatib, Sumaya N; Alsharaiah, Mohammad A; Hiari, Mohammad O
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.323

Abstract

Digital technology has permeated every aspect of our daily lives. Processing and evaluating information are highly demanding in all fields, including cybersecurity. Cybersecurity engineers widely use the Network Intrusion Prevention System (NIPS) to safeguard against cyberattacks. To avoid cyberattacks, the NIPS must deal with a large amount of data, which degrades its performance. This paper uses the whale optimization algorithm (WOA) and the Harris Hawks optimization method (HHO) to diminish the large amount of data that the NIPS needs to deal with. Subsequently, the Gradient Boosting Machine (GBM) is employed to determine the accuracy achieved when employing WOA and HHO. The GBM classifier is widely regarded as a sophisticated and straightforward classifier in data mining. Regardless of the premise of feature independence, it outperforms all other classification algorithms by delivering excellent performance. When using GBM, the findings indicate that the accuracy achieved with HHO is 89.81%, but the accuracy attained with WOA is 94.3%.
Co-Authors Abu-Shareha, Ahmad Abu-Shareha, Ahmad A. Abu-Shareha, Ahmad Adel Al-Khatib, Sumaya Al-Khatib, Sumaya N Al-Khatib, Sumaya N. Al-Khatib, Sumaya Nabil Al-Shamayleh, Ahmad Sami Al-Zyoud, Mahran M. Alalousi, Alabass Alkhatib, Sumaya Nabil Alsaaidah, Adeeb Alsaaidah, Adeeb M. Alsharaiah, Mohammad A Amer, Amal Anbar, Mohammed Fadhil, Hilal A. Hiari, Mohammad O Hiari, Mohammad O. Munther, Alhamza Nabil Alkhatib, Sumaya Rateb, Roqia Shambour, Qusai Y.