Artificial Intelligence and Big Data Framework for Cybersecurity Resilience in Distributed Networks
DOI:
https://doi.org/10.33050/kjn69m96Keywords:
Cybersecurity, Resilience, Data, Security, DistributedAbstract
The rapid expansion of distributed computer networks, driven by cloud computing, IoT ecosystems, edge computing, and software-defined infrastructures, has increased cybersecurity complexity. The growing volume, velocity, and variety of network data challenge traditional security mechanisms that focus primarily on threat detection, often neglecting system resilience, adaptive response, and recovery. This study develops a resilience-oriented intelligent big data analytics framework integrating Artificial Intelegence (AI), big data processing, and distributed cybersecurity monitoring to strengthen resilience in modern digital environments. A qualitative approach was employed through systematic literature review, conceptual modeling, thematic synthesis, and comparative analysis of existing architectures. The framework consists of four interconnected layers: data acquisition and aggregation, big data processing, intelligent analytics, and adaptive response and recovery. It supports continuous monitoring, anomaly detection, threat prediction, automated mitigation, and recovery orchestration. Comparative analysis indicates that prior studies focus mainly on improving intrusion detection or machine learning techniques, providing limited attention to resilience dimensions such as adaptability, fault tolerance, recovery efficiency, and operational stability. In contrast, the proposed framework integrates intelligent analytics with scalable big data infrastructures and distributed security mechanisms to create a unified resilience-oriented cybersecurity ecosystem. Findings suggest that combining AI-driven analytics, distributed processing, and adaptive security orchestration provides a strategic foundation for enhancing cybersecurity resilience, supporting sustainable digital infrastructure development, and ensuring operational stability in increasingly complex and interconnected network environments.
References
A. Sarjito, “Technological pride and national resilience: How innovation shapes stability and security,” Jurnal Lemhannas RI, vol. 12, no. 3, pp. 277–298, 2024.
L. Z. Nasution, H. Siregar, R. Ismal, and T. Mariyanti, “Tawhidi string relationship (tsr) approach to entrepreneurial growth in waqf linked sukuk,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 701–712, 2025.
P. Prabaswari, M. Alfikri, and I. Ahmad, “The implementation of policy for the establishment of a cyber incident response team to support information security in the government sector,” Matra Pembaruan, vol. 6, no. 1, pp. 1–14, 2022.
R. Anindita, V. H. Prastowo, and J. Parker, “Reinforcing the role of cyber village in improving indonesia msmes through an exploratory study,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 726–737, 2025.
A. B. P. Kasmo, S. Mor, L. Nugroho, M. Rohadi, and A. Purnama, “Integrating corporate strategy with digital transformation, cybersecurity, and sustainability,” Jurnal Lemhannas RI, vol. 13, no. 1, pp. 52–68, 2025.
R. P. Wadipalapa, R. Katharina, P. P. Nainggolan, S. Aminah, T. Apriani, D. Ma’rifah, and A. L. Anisah, “An ambitious artificial intelligence policy in a decentralised governance system: evidence from indonesia,” Journal of Current Southeast Asian Affairs, vol. 43, no. 1, pp. 65–93, 2024.
P. A. Sunarya, R. A. Sunarjo, M. Abbas, O. A. Al-Kamari, and S. Maulana, “Ai-driven educational data analytics and intelligent tutoring in learning factory environments,” International Transactions on Education Technology (ITEE), vol. 4, no. 1, pp. 14–30, 2025.
M. K. Gupta, A. K. Rai, M. Farooq et al., “Network security and protection strategies for big data: Challenges and innovations,” in 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), vol. 6. IEEE, 2023, pp. 705–709.
I. P. Gustiah and H. Newell, “Enhancing human resource management efficiency through scalable blockchain networks with an adaptive ai approach,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 2, pp. 114–123, 2025.
L. Diana, P. Dini, and D. Paolini, “Overview on intrusion detection systems for computers networking security,” Computers, vol. 14, no. 3, p. 87, 2025.
N. Nuryani, A. B. Mutiara, I. M. Wiryana, D. Purnamasari, and S. N. W. Putra, “Artificial intelligence model for detecting tax evasion involving complex network schemes,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 339–356, 2024.
M. Masunda and R. Ajayi, “Enhancing security in federated learning: designing distributed data science algorithms to reduce cyber threats,” Int J Adv Res Publ Rev, vol. 2, no. 4, pp. 399–421, 2025.
H. Herman, W. Achmad, N. Aulia, S. Rusdian, and T. Green, “Utilizing ipfs for decentralized data storage a security and censorship resistance solution,” Blockchain Frontier Technology, vol. 5, no. 2, pp. 124–135, 2026.
E. Edozie, A. N. Shuaibu, B. O. Sadiq, and U. K. John, “Artificial intelligence advances in anomaly detection for telecom networks.” Artif. Intell. Rev., vol. 58, no. 4, p. 100, 2025.
A. G. Prawiyogi, S. Purnama, and L. Meria, “Smart cities using machine learning and intelligent applications,” International Transactions on Artificial Intelligence, vol. 1, no. 1, pp. 102–116, 2022.
X. Zhao, “Network security situational awareness and early warning architecture based on big data,” International Journal of System Assurance Engineering and Management, pp. 1–17, 2024.
A. Hlacs and H. Wells, “Using digital technology to strengthen oversight of public procurement in portugal: The use of data analytics and machine learning by the tribunal de contas,” OECD Publishing, Paris, Tech. Rep. 83, Jun. 2025. [Online]. Available: https://doi.org/10.1787/43add03b-en
T. S. Goh, S. Martinez, S. L. Sitorus, and T. L. Anita, “Adaptive strategic approaches and their impact on economic growth in dynamic markets,” Startupreneur Business Digital (SABDA Journal), vol. 4, no. 2, pp. 174–183, 2025.
M. H. Kabir, M. Razib, Y. Arafat, R. A. M. Rashed, and Z. Jesan, “Strengthening us critical infrastructure resilience through nist-aligned cybersecurity governance and ai-driven threat detection,” Journal of Computer Science and Technology Studies, vol. 7, no. 6, pp. 1120–1134, 2025.
I. Sembiring, D. Manongga, U. Rahardja, and Q. Aini, “Understanding data-driven analytic decision making on air quality monitoring an empirical study,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 418–431, 2024.
P. Nair and R. Yadavalli, “Hybrid ai-driven resilient architecture (hadra) for adaptive and autonomous cybersecurity: Analytical insights on emerging trends and challenges,” in 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON). IEEE, 2025, pp. 1–7.
T. K. Chowdhury, “Ai-powered deep learning models for real-time cybersecurity risk assessment in en- terprise it systems,” ASRC Procedia: Global Perspectives in Science and Scholarship, vol. 1, no. 01, pp. 675–704, 2025.
P. S. Sarker, S. K. Sadanandan, and A. K. Srivastava, “Resiliency metrics for monitoring and analysis of cyber-power distribution system with iots,” IEEE Internet of Things Journal, vol. 10, no. 9, pp. 7469– 7479, 2022.
V. Tzavara and S. Vassiliadis, “Tracing the evolution of cyber resilience: a historical and conceptual review,” International Journal of Information Security, vol. 23, no. 3, pp. 1695–1719, 2024.
A. Vaseashta, “Applying resilience to hybrid threats in infrastructure, digital, and social domains using multisectoral, multidisciplinary, and whole-of-government approach,” in Building cyber resilience against hybrid threats. SAGE Publications 1 Oliver’s Yard, 55 City Road, London, EC1Y 1SP, 2022, pp. 42–59.
G. T. Sulistyantoro, A. Khaq, M. Z. K. Khan, A. Amin, and A.-A.-M. Hussain, “Shaping artificial intelligence governance and risk management in the public sector: Regulatory insights,” Lex Publica, vol. 11, no. 1, pp. 161–181, 2024.
M. S. Uddin, M. S. Sikder, M. M. Anwar, and F. Hossain, “Ai-driven cybersecurity and big data-enabled mis frameworks: Strengthening supply chain integrity, energy resilience, and critical infrastructure pro- tection,” Journal of Computer Science and Technology Studies, vol. 7, no. 9, pp. 223–232, 2025.
A. Pratiwi, M. E. Rahmawyanet, P. A. W. Putra, D. I. Sensuse et al., “Systematic literature review on artificial intelligence in indonesia’s public sector: Reimagining digital government,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 11, no. 2, pp. 304–316, 2025.
S. L. Sitorus, R. T. H. Safariningsih, A. H. A. N. Karsa, M. A. Komara, and R. Evans, “Optimizing smart contracts and blockchain for sustainable digital fashionpreneurship,” Blockchain Frontier Technology, vol. 5, no. 1, pp. 37–48, 2025.
I. E. Kezron, “Enhancing cybersecurity capacity in small and medium enterprises: A framework for workforce development,” Iconic Research And Engineering Journals, vol. 7, no. 10, pp. 421–428, 2024.
I. O. Evans-Uzosike, C. G. Okatta, B. Otokiti, O. Ejike, and O. T. Kufile, “Closing the cybersecurity talent gap: A strategic workforce readiness framework,” World Journal of Innovation and Modern Technology, vol. 9, no. 6, pp. 230–241, 2025.
I. R. Maulana, U. Rahardja, N. Azizah, M. Rakhmansyah, and M. A. Komara, “Leveraging ipfs to build secure and decentralized websites in the web 3.0 era,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 1–12, 2025.
A. N. Lone, S. Mustajab, and M. Alam, “A comprehensive study on cybersecurity challenges and opportunities in the iot world,” Security and Privacy, vol. 6, no. 6, p. e318, 2023.
R. Indrawan, A. Ratih, H. Agustian, and R. Evans, “Governance models for blockchain integrated iot ecosystems,” Blockchain Frontier Technology, vol. 5, no. 2, pp. 219–229, 2026.
J. Yu, A. V. Shvetsov, and S. H. Alsamhi, “Leveraging machine learning for cybersecurity resilience in industry 4.0: Challenges and future directions,” IEEE access, vol. 12, pp. 159 579–159 596, 2024.
P. Rathod, N. Polemi, M. Lehto, K. Kioskli, J. Wessels, and R. Lugo, “Leveraging the european cybersecurity skills framework (ecsf) in eu innovation projects: Workforce development through skilling, upskilling, and reskilling,” in 2024 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2024, pp. 1–9.
M. S. Islam, M. A. Rahman, M. A. Bin Ameedeen, H. Ajra, Z. B. Ismail, and J. M. Zain, “Blockchain enabled cybersecurity provision for scalable heterogeneous network: A comprehensive survey.” Computer Modeling in Engineering & Sciences (CMES), vol. 138, no. 1, p. 43, 2024.
W. S. Admass, Y. Y. Munaye, and A. A. Diro, “Cyber security: State of the art, challenges and future directions,” Cyber Security and Applications, vol. 2, p. 100031, 2024.
M. Siahaan, S. Kosasi, N. Sukendri, and A. Husain, “Enhancing smes business performance through strategic digital transformation,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 85–96, 2025.
M. M. I. Jabed, A. S. Khawer, S. Ferdous, D. H. Niton, A. B. Gupta, and M. S. Hossain, “Integrating business intelligence with ai-driven machine learning for next-generation intrusion detection systems,” International Journal of Research and Applied Innovations, vol. 6, no. 6, pp. 9834–9849, 2023.
B. Rawat, A. S. Bist, D. Apriani, N. I. Permadi, and E. A. Nabila, “Ai based drones for security concerns in smart cities,” APTISI Transactions On Management (ATM), vol. 7, no. 2, pp. 125–130, 2023.
D. S. Mary, L. J. S. Dhas, A. Deepa, M. A. Chaurasia, and C. J. J. Sheela, “Network intrusion detection: An optimized deep learning approach using big data analytics,” Expert Systems with Applications, vol. 251, p. 123919, 2024.
A. Simanjuntak, A. Sutarman, S. A. Anjani, and A. Nuche, “Integrating artificial intelligence in e-learning for organizational well-being through orange technology mapping,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 13–26, 2025.
O. A. D. Wulandari, D. Apriani, and Y. Febriansyah, “Sustainable institutional entrepreneurial culture and innovation for economic growth,” APTISI Transactions on Management, vol. 7, no. 3, pp. 221–230, 2023.
W. Sun, A. Katsifodimos, and R. Hai, “Accelerating machine learning queries with linear algebra query processing,” in Proceedings of the 35th International Conference on Scientific and Statistical Database Management, 2023, pp. 1–12.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Syaifuddin Syaifuddin, Ahmad Gunawan, Maulana Arif Komara, Agung Lorenzo
This work is licensed under a Creative Commons Attribution 4.0 International License.
