Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) The Indonesian Journal of Computer Science
Jones, Rebet
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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

Found 2 Documents
Search

 1 
Revolutionizing Cybersecurity: The GPT-2 Enhanced Attack Detection and Defense (GEADD) Method for Zero-Day Threats Jones, Rebet; Omar, Marwan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol 5 No 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.12741

Abstract

The escalating sophistication of cyber threats, particularly zero-day attacks, necessitates advanced detection methodologies in cybersecurity. This study introduces the GPT-2 Enhanced Attack Detection and Defense (GEADD) method, an innovative approach that integrates the GPT-2 model with metaheuristic optimization techniques for enhanced detection of zero-day threats. The GEADD method encompasses data preprocessing, Equilibrium Optimization (EO)-based feature selection, and Salp Swarm Algorithm-Based Optimization (SABO) for hyperparameter tuning, culminating in a robust framework capable of identifying and classifying zero-day attacks with high accuracy. Through a comprehensive evaluation using standard datasets, the GEADD method demonstrates superior performance in detecting zero-day threats compared to existing models, highlighting its potential as a significant contribution to the field of cybersecurity. This study not only presents a novel application of deep learning for cyber threat detection but also sets a foundation for future research in AI-driven cybersecurity solutions
The Impact of AI on Secure Cloud Computing: Opportunities and Challenges Jones, Rebet
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4383

Abstract

This paper explores the intersection of Artificial Intelligence (AI) and cloud computing, focusing on the security implications. As cloud computing becomes increasingly ubiquitous, the integration of AI presents both opportunities and challenges. This paper provides an in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security. Key topics include AI's role in threat detection, data protection, access management, and the challenges related to AI bias, interpretability, and adversarial attacks.
Co-Authors Omar, Marwan