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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Al-Khatib, Sumaya Nabil
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An innovative approach for enhancing capacity utilization in point-to-point voice over internet protocol calls M. Abualhaj, Mosleh; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; O. Hiari, Mohammad; Al-Mahadeen, Layth
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp488-496

Abstract

Voice over internet protocol (VoIP) calls are increasingly transported over computer-based networking due to several factors, such as low call rates. However, point-to-point (P-P) calls, as a division of VoIP, are encountering a capacity utilization issue. The main reason for that is the giant packet header, especially when compared to the runt P-P calls packet payload. Therefore, this research article introduced a method to solve the liability of the giant packet header of the P-P calls. The introduced method is named voice segment compaction (VSC). The VSC method employs the unneeded P-P calls packet header elements to carry the voice packet payload. This, in turn, reduces the size of the voice payload and improves network capacity utilization. The preliminary results demonstrated the importance of the introduced VSC method, while network capacity improved by up to 38.33%.
Improving firewall performance using hybrid of optimization algorithms and decision trees classifier Abualhaj, Mosleh M.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya Nabil; Alsaaidah, Adeeb M.; Anbar, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2839-2848

Abstract

One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.
Co-Authors Abu-Shareha, Ahmad Adel Abualhaj, Mosleh M. Al-Mahadeen, Layth Alsaaidah, Adeeb M. Anbar, Mohammed M. Abualhaj, Mosleh O. Hiari, Mohammad