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All Journal Journal of Education and Teaching Learning Jurnal Ilmiah Telsinas Elektro, Sipil dan Teknik Informasi International Journal Software Engineering and Computer Science (IJSECS) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Journal of Artificial Intelligence and Digital Business ARMADA : Jurnal Penelitian Multidisiplin Room of Civil Society Development International Journal of Multidisciplinary Approach Research and Science Jurnal Riset Multidisiplin dan Inovasi Teknologi Journal of Community Service and Society Empowerment Journal of Social Science Utilizing Technology Journal of Social Science Utilizing Technology EDUTREND: Journal of Emerging Issues and Trends in Education Room of Civil Social Development
Fazil, Abdul Wajid
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Integrating Blockchain and Machine Learning for Predictive Cyber Defense Systems Kohistani, Ahmad Jamy; Azimi, Irfanullah; Fazil, Abdul Wajid
ARMADA : Jurnal Penelitian Multidisiplin Vol. 3 No. 12 (2025): ARMADA : Jurnal Penelitian Multidisplin, December 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

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Abstract

The rapid expansion of cyber threats targeting critical infrastructures highlights the limitations of traditional centralized security systems, which suffer from latency, scalability constraints, and single points of failure. This study addresses this problem by examining how the integration of Blockchain and Machine Learning (ML) can strengthen predictive cyber defense and enhance real-time anomaly detection. The purpose of the research is to synthesize current evidence on the security, efficiency, and operational benefits of Blockchain–ML frameworks through a Systematic Literature Review (SLR). Following PRISMA guidelines, a structured search was conducted across four major databases IEEE Xplore, ScienceDirect, Scopus, and Web of Science covering peer-reviewed literature published between 2020 and 2025. Using a four-category keyword strategy, the review initially identified 1100 records, ultimately narrowing the final dataset to 25 studies that met all inclusion criteria. The results indicate that Blockchain significantly enhances data integrity, auditability, and threat-intelligence reliability, while ML improves predictive accuracy and supports real-time detection. Together, these technologies outperform conventional centralized systems in terms of transparency, resilience, and operational efficiency. The study concludes that Blockchain–ML integration provides a robust foundation for next-generation, decentralized cybersecurity architectures, offering measurable improvements in security and system performance.
Co-Authors Ahmady, Ezatullah Amiri, Frishta Aslamzai, Sebghatullah Azimi, Irfanullah Enayat, Wahidullah Ezam, Zakirullah Ghafori, Agha Mohammad Ghafory, Hamayoon Habibi, Habibrahman Hakimi, Abdurrahman Hakimi, Faiz Mohammad Hakimi, Musawer Hakimi, Samer Hamidi, Shir Ahmad Hasas, Ansarullah I Wayan Aditya Suranata Katebzadah, Shairagha Khaliqyar, Khudai Qul Kohistani, Ahmad Jaamay Kohistani, Ahmad Jamy Kohistani, Jaamay Mohammadi, Mohammad Qias Musawi, Sayed Zabihullah Najieb, Khairullah Nasrat, Abdulfatah Qarizada, Abdulkhaliq Quchi, Mohammad Mustafa Quraishi, Tamana Quraishi, Tamanna Rahmani, Bilal Rahmani, Khoshal Rahman Sajid, Saidamin Samadzai, Abdul Wahid Shahidzay, Amir Kror Turan, Mohammad Nawab