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All Journal CESS (Journal of Computer Engineering, System and Science) ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) ProBisnis : Jurnal Manajemen Indonesian Journal Accounting (IJAcc) International Journal of Cyber and IT Service Management (IJCITSM) Journal of Computer Science and Technology Application
Tuti Nurhaeni
Universitas Raharja
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks Aswadi Jaya; Suca Rusdian; Fitra Putri Oganda; Tuti Nurhaeni; John Edwards
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kz2w4z84

Abstract

The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.
Co-Authors Abdul Musonip Adam Faturahman Al- Bahra Ankur Singh Ari Wibowo Aswadi Jaya Dian Maharani Damanik Eka Dian Astuti Era Era Hia Felix Sutisna Fitra Putri Oganda Galih Putra Cesna John Edwards Marviola Hardini Meri Mayang Sari Millia Idfitri Muhamad Rifqi Gian Sabillah Mukti Budiarto Ninda Lutfiani Noah Rangi Nuke Puji Lestari Santoso Putu Aditya Eka Pratama Refa Azka Suca Rusdian Sugeng Santoso Untung Rahardja Widya Febriani