Article Per Year (5 Year)
p-Index From 2021 - 2026
0.983
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Research and Applied Technology (INJURATECH) Jurnal Ilmiah Computer Science
Sallow, Zina Bibo
Unknown Affiliation
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Cybernetic Deception: Unraveling the Layers of Email Phishing Threats Zangana, Hewa Majeed; Mohammed, Ayaz Khalid; Sallow, Amira Bibo; Sallow, Zina Bibo
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

E-mail phishing, a tireless and versatile cybersecurity risk, requires a intensive examination to fortify organizational resistances. This broad survey dives into the multifaceted measurements of e-mail phishing, including mental control strategies, mechanical complexities, and real-world experiences determined from assorted case considers. The investigation of location and anticipation procedures covers a extend of commitments, tending to half breed machine learning approaches, the significance of client instruction, and the part of administrative compliance. These procedures give a significant system for organizations pointing to improve their flexibility against the energetic scene of phishing strategies. The theoretical underscores the administrative landscape's significant part in forming cybersecurity hones, advertising a organized establishment for organizations to adjust with legitimate prerequisites. Expecting future patterns and challenges, such as the integration of characteristic dialect preparing procedures and the complexities of cloud-based phishing assaults, gets to be basic for maintained cyber versatility. In conclusion, this paper serves as a comprehensive direct, enabling people and organizations with the information and methodologies required to explore the complex scene of e-mail phishing dangers. It recognizes the energetic nature of the danger scene, highlighting the progressing travel in combating computerized duplicity and invigorating preparation against the ever-evolving strategies of phishing foes.
The Human Factor in Cybersecurity: Addressing the Risks of Insider Threats Zangana, Hewa Majeed; Sallow, Zina Bibo; Omar, Marwan
Jurnal Ilmiah Computer Science Vol. 3 No. 2 (2025): Volume 3 Number 2 January 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i2.37

Abstract

In the rapidly evolving landscape of cybersecurity, the human element remains one of the most critical and complex factors to manage. Insider threats, whether originating from malicious intent or inadvertent actions, pose significant risks to organizational security. This paper explores the multifaceted nature of insider threats, examining the motivations and behaviors that drive individuals to compromise systems. By analyzing case studies and current research, we identify key vulnerabilities and the role of organizational culture in mitigating these risks. Furthermore, we propose comprehensive strategies for detecting, preventing, and responding to insider threats, emphasizing the importance of continuous education, robust access controls, and advanced monitoring technologies. This paper aims to provide a holistic understanding of the human factor in cybersecurity and offers practical solutions to address the pervasive challenge of insider threats.
Cloud Architectures for Distributed Serverless Computing: A Review of Event-Driven and Function-as-a-Service Paradigms Zangana, Hewa Majeed; Sallow, Zina Bibo; Omar, Marwan
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 6 No. 2 (2024): November 2024
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v6i2.8597

Abstract

The advent of serverless computing has revolutionized the cloud computing landscape, providing scalable, cost-effective, and flexible solutions for modern application development. This paper comprehensively reviews cloud architectures for distributed serverless computing, focusing on event-driven and Function-as-a-Service (FaaS) paradigms. This research explores the fundamental principles and benefits of serverless computing, highlighting its impact on development practices and infrastructure management. The review covers key components, including orchestration, scalability, and security, and examines leading serverless platforms and frameworks. Through critically analyzing current research and industry practices, identify challenges and propose future directions for optimizing serverless architectures. This paper aims to explain how event-driven and FaaS paradigms reshape cloud computing, enabling developers to build resilient and efficient applications without server management. Our research found that event-driven architectures in serverless computing offer significant advantages in scalability, real-time processing, and resource utilization. FaaS paradigms provide modularity, granularity, and cost-effectiveness, making them suitable for various applications. Cloud-edge collaborative architectures are crucial for achieving low-latency and high-performance serverless applications but require robust security, privacy, and resource management frameworks.
The Synergy of Blockchain and Cybersecurity: Building Trust in Digital Environments Zangana, Hewa Majeed; Sallow, Zina Bibo; Mustafa, Firas Mahmood; Husain, Mamo Muhamad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1701

Abstract

The rapid expansion of digital ecosystems has intensified concerns about data security, privacy, and trust. Blockchain technology, characterized by its decentralized, immutable, and transparent nature, offers a transformative approach to strengthening cybersecurity. This paper examines the synergy between blockchain and cybersecurity, emphasizing how blockchain’s cryptographic foundations, consensus mechanisms, and smart contracts can mitigate cyber threats, enhance authentication, and ensure data integrity. By analyzing emerging trends, challenges, and real-world applications, this study underscores the potential of blockchain to reinforce digital trust and resilience across diverse sectors. The findings contribute to the ongoing discourse on secure digital environments by proposing an integrated framework for blockchain-based cybersecurity solutions
AI-Driven Fraud Detection in Digital Banking: A Hybrid Approach using Deep Learning and Anomaly Detection Mohammed, Harman Salih; Sallow, Zina Bibo; Zangana, Hewa Majeed
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5757

Abstract

The rapid digital transformation in the banking sector has introduced new opportunities for efficiency and customer convenience but has also amplified the risks of financial fraud. Traditional fraud detection mechanisms, often reliant on static rule-based systems, struggle to keep pace with the dynamic, evolving nature of fraudulent activities. This paper proposes a novel hybrid framework that integrates deep learning models with anomaly detection techniques to enhance the accuracy, robustness, and adaptability of fraud detection in digital banking. The proposed approach leverages a deep neural network (DNN) architecture trained under supervised learning to capture complex transactional patterns and combines it with autoencoder-based unsupervised anomaly detection to uncover previously unseen fraud strategies. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications and its potential for multi-institutional deployment, enabling secure inter-bank fraud intelligence sharing without compromising data privacy. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications. This work contributes to the growing field of AI-driven financial security by addressing both detection performance and adaptability to emerging fraud behaviors.
Co-Authors Husain, Mamo Muhamad Mohammed, Ayaz Khalid Mohammed, Harman Salih Mustafa, Firas Mahmood Omar, Marwan Sallow, Amira Bibo Zangana, Hewa Majeed