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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Atalla, Shadi
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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.
Optimized indoor radio signal prediction with 3D ray tracing model at 2.4 and 5 GHz Tarapiah, Saed; Sulaiman, Batoul; Natsheh, Emad; Atalla, Shadi; Abu Kharmeh, Suleiman; Rashed, Abdallah
Bulletin of Electrical Engineering and Informatics 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/eei.v14i4.9441

Abstract

Channel propagation models are essential in developing efficient wireless communication networks. Indoor propagation relies on the nature of the surrounding environment. Therefore, many researchers have provided different ways for effective propagation modeling and received power prediction. In this paper, ray-tracing-based site-specific propagation models are presented. The actual measurements are obtained using many wireless access points (AP) based on IEEE 802.11 with different technologies a/b/g and n as transmitters and mobile phone with a proposed mobile application used as a receiver to collect the power at different locations called reference points (RPs), these measurements are done without the existence of people movement. The simulation results are obtained using wireless InSite simulator depends on 3D shoot and bounce ray (SBR) method. The simulation measurements are assessed by comparing it with the actual measurements and they analyze statistically such that the correlation coefficient R between them reaches up to 80% which is an indicator to an acceptable agreement. Path loss characteristic affected by the building materials and distance along the receiver’s route is evaluated.
Co-Authors Abu Kharmeh, Suleiman Al-karaki, Jamal Alauthman, Mohammad Almomani, Ammar Gawanmeh, Amjad Ibrahim, Noor Farizah Mashaleh, Ashraf S. Natsheh, Emad Rashed, Abdallah Sulaiman, Batoul Tarapiah, Saed