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All Journal Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Makura, Sheunesu
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection Makura, Sheunesu; Dobson, Caden; Rananga, Seani
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1076

Abstract

Online banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection with network packet analysis to mitigate fraudulent activities, focusing particularly on Distributed Denial of Service (DDoS) attacks. The key contribution is an ensemble model combining Isolation Forest and K-means clustering, which achieves 98% accuracy and 98% F1-score in anomaly detection while reducing false positives to 2% which is a critical improvement for operational deployment in banking systems. The framework’s semi-supervised architecture enables zero-day fraud detection without reliance on labeled attack data, addressing a fundamental limitation of signature-based systems. By leveraging feature optimization (PCA/t-SNE) and real-time processing capabilities, this solution offers financial institutions a practical, adaptive defense mechanism against evolving cyber threats. The results demonstrate significant potential for integration into existing banking security infrastructures to enhance fraud prevention with minimal disruption.
Digital Forensic-Ready Voting Model Muyambo, Edmore; Baror, Stacey; Makura, Sheunesu
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5019

Abstract

The increasing digitalization of elections through internet-based voting (e-voting) systems introduces both opportunities for enhanced accessibility and threats to electoral integrity. Existing electronic voting systems often lack built-in forensic capabilities necessary to detect, preserve, and prove incidents of vote rigging or cyber manipulation. This paper proposes a Digital Forensic-Ready Voting Model (DFRVM) that integrates forensic-by-design principles, blockchain technology, and legal admissibility frameworks to ensure accountability, transparency, and verifiability in the electoral process. The model emphasizes proactive evidence collection, real-time monitoring, and tamper-evident audit trails to strengthen post-election dispute resolution.
Co-Authors Baror, Stacey Dobson, Caden Muyambo, Edmore Rananga, Seani