Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Gawanmeh, Amjad
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
The role of disruptive technologies in the metaverse worlds: state of the art survey Al-Karaki, Jamal N.; Gawanmeh, Amjad; Awad, Ahmed; Zerai Teklesenbet, Natnael
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2211-2223

Abstract

The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Co-Authors Al-karaki, Jamal Al-Karaki, Jamal N. Alauthman, Mohammad Almomani, Ammar Atalla, Shadi Awad, Ahmed Ibrahim, Noor Farizah Mashaleh, Ashraf S. Zerai Teklesenbet, Natnael