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Ethan Harris
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Advanced Big Data Analytics for Proactive Cyber Threat Mitigation in Large Scale Computer Networks Ratna Tri Hari Safariningsih; Untung Rahardja; Mitra Terima Des Sincer Putri; Nur Azizah; Ethan Harris
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kr1b0a59

Abstract

The rapid expansion of digital infrastructure and interconnected systems, large scale computer networks increasingly face sophisticated cyber threats that challenge traditional security mechanisms. The growing volume, velocity, and variety of network data require more advanced analytical approaches capable of detecting and mitigating threats proactively. Over recent years, artificial intelligence and big data technologies have demonstrated strong potential in improving the efficiency and accuracy of cybersecurity systems, particularly in environments characterized by high data complexity and dynamic attack patterns. Motivated by these challenges, this study proposes an advanced big data analytics approach integrated with artificial intelligence techniques to support proactive cyber threat mitigation in large scale computer networks. The proposed method processes large scale network traffic data using intelligent analytical models capable of identifying abnormal behavioral patterns and predicting potential cyber attacks before they fully develop. Experimental simulations using benchmark network datasets indicate that the proposed approach improves detection accuracy, reduces false alarm rates, and enhances the responsiveness of cybersecurity systems when compared with several conventional analytical techniques. The integration of scalable big data processing with adaptive artificial intelligence models also demonstrates strong capability in handling complex and high volume network environments. The findings highlight that advanced big data analytics combined with artificial intelligence can significantly strengthen proactive cyber defense mechanisms in modern computer networks, contributing to more resilient and adaptive cybersecurity infrastructures capable of responding to evolving digital threats.
Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Agung Rizky; Ahmad Gunawan; Maulana Arif Komara; Muchlisina Madani; Ethan Harris
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.68

Abstract

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.
Co-Authors Agung Rizky Ahmad Gunawan Maulana Arif Komara Mitra Terima Des Sincer Putri Muchlisina Madani Nur Azizah Ratna Tri Hari Safariningsih Untung Rahardja