Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks
DOI:
https://doi.org/10.33050/kz2w4z84Keywords:
Artificial Intelligence, Cybersecurity, Large Scale Computer, Threat Detection, Adaptive LearningAbstract
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.
References
M. A. Ameedeen, R. A. Hamid, T. H. Aldhyani, L. A. K. M. Al-Nassr, S. O. Olatunji, and P. Subramanian, “A framework for automated big data analytics in cybersecurity threat detection,” Mesopotamian Journal of Big Data, vol. 2024, pp. 175–184, 2024.
V. M. De Oliveira, H. M. De Oliveira, G. M. Santos, J. Geremias, and E. K. Viegas, “A big data framework for scalable and cross-dataset capable machine learning in network intrusion detection systems,” IEEE Access, 2025.
K. M. Jha, V. Bodepudi, S. B. Boppana, N. Katnapally, S. R. Maka, and M. Sakuru, “Deep learning-enabled big data analytics for cybersecurity threat detection in erp ecosystems,” Review of Contemporary Philosophy, vol. 22, no. 1, pp. 6193–6209, 2023.
K. A. Shakil, M. A. Wani, O. Elezaj, M. Asim, and A. Ateya, “Securing big data assets in a data-driven world with deep learning and natural language processing,” in Cybersecurity, Cybercrimes, and Smart Emerging Technologies. CRC Press, 2025, pp. 58–64.
U. Rahardja, “Social media analysis as a marketing strategy in online marketing business,” Startupreneur Business Digital (SABDA Journal), vol. 1, no. 2, pp. 176–182, 2022.
K. D. O. Ofoegbu, O. S. Osundare, C. S. Ike, O. G. Fakeyede, and A. B. Ige, “Real-time cybersecurity threat detection using machine learning and big data analytics: A comprehensive approach,” Computer Science & IT Research Journal, vol. 4, no. 3, pp. 478–501, 2024.
F. Ekundayo, I. Atoyebi, A. Soyele, and E. Ogunwobi, “Predictive analytics for cyber threat intelligence in fintech using big data and machine learning,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–15, 2024.
N. Z. Khalaf, A. Barazanchi, I. Ibraheem, A. Radhi, P. Shah, and R. Sekhar, “Development of real-time threat detection systems with ai-driven cybersecurity in critical infrastructure,” Mesopotamian Journal of CyberSecurity, vol. 5, no. 2, pp. 501–513, 2025.
S. M. T. Nizamudeen, “Intelligent intrusion detection framework for multi-clouds–iot environment using swarm-based deep learning classifier,” Journal of Cloud Computing, vol. 12, no. 1, p. 134, 2023.
J. Jones, E. Harris, Y. Febriansah, A. Adiwijaya, and I. N. Hikam, “Ai for sustainable development: Applications in natural resource management, agriculture, and waste management,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 143–149, 2024.
N. Ahmed, A. b. Ngadi, J. M. Sharif, S. Hussain, M. Uddin, M. S. Rathore, J. Iqbal, M. Abdelhaq, R. Alsaqour, S. S. Ullah et al., “Network threat detection using machine/deep learning in sdn-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction,” Sensors, vol. 22, no. 20, p. 7896, 2022.
S. C. Amarasinghe, “Developing robust deep learning models for intelligent infrastructure: Addressing scalability, security, and privacy challenges,” Applied Research in Artificial Intelligence and Cloud Computing, vol. 7, no. 4, pp. 1–10, 2024.
C. Madhavram, E. P. Galla, S. K. Rajaram, G. K. Patra et al., “Ai-driven threat detection: Leveraging big data for advanced cybersecurity compliance,” Available at SSRN 5029406, 2022.
S. A. Oladosu, A. B. Ige, C. C. Ike, P. A. Adepoju, O. O. Amoo, and A. I. Afolabi, “Ai-driven security for next-generation data centers: Conceptualizing autonomous threat detection and response in cloud-connected environments,” GSC Adv Res Rev, vol. 15, no. 2, pp. 162–172, 2023.
A. Rengarajan, “Cloud-based ai-driven threat detection framework for smart grid cybersecurity,” International Journal of Future Innovative Science and Technology (IJFIST), vol. 8, no. 6, p. 16065, 2025.
H. Safitri, M. H. R. Chakim, and A. Adiwijaya, “Strategy based technology-based startups to drive digital business growth,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 2, pp. 207–220, 2023.
A. A. Almazroi and N. Ayub, “Deep learning hybridization for improved malware detection in smart internet of things,” Scientific reports, vol. 14, no. 1, p. 7838, 2024.
A. Budˇzys, O. Kurasova, and V. Medvedev, “Deep learning-based authentication for insider threat detection in critical infrastructure,” Artificial intelligence review., vol. 57, no. 10, pp. 1–35, 2024.
A. Ali, H. Ali, A. Saeed, A. Ahmed Khan, T. T. Tin, M. Assam, Y. Y. Ghadi, and H. G. Mohamed, “Blockchain-powered healthcare systems: enhancing scalability and security with hybrid deep learning,” Sensors, vol. 23, no. 18, p. 7740, 2023.
S. Potluri, “A deep learning-driven framework for detecting anomalous data breaches in distributed cloud storage infrastructures,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 3, pp. 80–87, 2024.
Y. Shino, H. Kenta, and I. K. Mertayasa, “Media promotional for art in tangerang city with audio visual adobe creative,” Aptisi Transactions on Technopreneurship (ATT), vol. 4, no. 2, pp. 192–204, 2022.
A. K. S. Ali, A. Raza, H. Arif, and A. A. Hussain, “Intelligent intrusion detection and data protection in information security using artificial intelligence and machine learning techniques,” Spectrum of Engineering Sciences, pp. 818–828, 2025.
T. Arjunan, “Real-time detection of network traffic anomalies in big data environments using deep learning models,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 9, pp. 10–22 214, 2024.
M. A. Rahman, B. T. Haque, M. I. Hossan, and M. S. K. C. Rubel, “Adaptive threat detection framework for iot-enabled healthcare, financial, and connected systems in the united states,” Frontiers in Computer Science and Artificial Intelligence, vol. 4, no. 3, pp. 68–86, 2025.
N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Machine learning and deep learning for big data analytics: A review of methods and applications,” Partners Universal International Innovation Journal, vol. 2, no. 3, pp. 172–197, 2024.
P. Sivalakshmi, U. Kavitha, R. Usha, O. Pattanaik, S. Maniraj, and C. Srinivasan, “Smart retail store surveillance and security with cloud-powered video analytics and transfer learning algorithms,” in 2024 second International conference on intelligent cyber physical systems and internet of things (ICoICI). IEEE, 2024, pp. 242–247.
M. Aminu, A. Akinsanya, D. A. Dako, and O. Oyedokun, “Enhancing cyber threat detection through real-time threat intelligence and adaptive defense mechanisms,” International Journal of Computer Applications Technology and Research, vol. 13, no. 8, pp. 11–27, 2024.
K. P. Seng, L. M. Ang, and E. Ngharamike, “Artificial intelligence internet of things: A new paradigm of distributed sensor networks,” International Journal of Distributed Sensor Networks, vol. 18, no. 3, p. 15501477211062835, 2022.
H. Gadde, “Leveraging ai for scalable query processing in big data environments,” International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 02, pp. 435–465, 2023.
M. Andronie, G. L˘az˘aroiu, M. Iatagan, I. Hurloiu, R. S, tef˘anescu, A. Dijm˘arescu, and I. Dijm˘arescu, “Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools in the internet of robotic things,” ISPRS International Journal of Geo-Information, vol. 12, no. 2, p. 35, 2023.
H. N. AlEisa, F. Alrowais, R. Allafi, N. S. Almalki, R. Faqih, R. Marzouk, M. M. Alnfiai, A. Motwakel, and S. S. Ibrahim, “Transforming transportation: Safe and secure vehicular communication and anomaly detection with intelligent cyber–physical system and deep learning,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 1736–1746, 2023.
N. Anwar, A. M. Widodo, B. A. Sekti, M. B. Ulum, M. Rahaman, and H. D. Ariessanti, “Comparative analysis of nij and nist methods for microsd investigations: A technopreneur approach,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 169–181, 2024.
A. A. Khan, A. A. Laghari, A. M. Baqasah, R. Bacarra, R. Alroobaea, M. Alsafyani, and J. A. J. Alsayay- deh, “Bdlt-iomt—a novel architecture: Svm machine learning for robust and secure data processing in internet of medical things with blockchain cybersecurity,” The Journal of Supercomputing, vol. 81, no. 1, p. 271, 2025.
A. Hammad and R. Abu-Zaid, “Applications of ai in decentralized computing systems: harnessing artificial intelligence for enhanced scalability, efficiency, and autonomous decision-making in distributed architectures,” Applied Research in Artificial Intelligence and Cloud Computing, vol. 7, no. 6, pp. 161–187, 2024.
N. Mohamed, “Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the- art techniques and future paradigms,” Knowledge and Information Systems, vol. 67, no. 8, pp. 6969–7055, 2025.
U. Rahardja, I. D. Hapsari, P. H. Putra, and A. N. Hidayanto, “Technological readiness and its impact on mobile payment usage: A case study of go-pay,” Cogent Engineering, vol. 10, no. 1, p. 2171566, 2023.
M. A. Alam, A. R. Nabil, A. A. Mintoo, and A. Islam, “Real-time analytics in streaming big data: techniques and applications,” Journal of Science and Engineering Research, vol. 1, no. 01, pp. 104–122, 2024.
P. A. D. S. N. Wijesekara and S. Gunawardena, “A review of blockchain technology in knowledge-defined networking, its application, benefits, and challenges,” Network, vol. 3, no. 3, pp. 343–421, 2023.
B. T. Haque, M. A. Rahman, M. S. K. C. Rubel, and M. I. Hossan, “Large language model (llm)–driven threat correlation and governance automation for security operations in us enterprise systems,” Journal of Computer Science and Technology Studies, vol. 7, no. 8, pp. 1296–1315, 2025.
R. Vadisetty and A. Polamarasetti, “Generative ai-driven distributed cybersecurity frameworks for ai-integrated global big data systems,” in 2024 International Conference on Emerging Technologies and Innovation for Sustainability (EmergIN). IEEE, 2024, pp. 595–600.
U. Rahardja, Q. Aini, A. S. Bist, S. Maulana, and S. Millah, “Examining the interplay of technology readiness and behavioural intentions in health detection safe entry station,” JDM (Jurnal Dinamika Manajemen), vol. 15, no. 1, pp. 125–143, 2024.
N. Lutfiani, A. Ivanov, N. P. L. Santoso, S. V. Sihotang, and S. Purnama, “E-commerce growth plan for msmes’ sustainable development enhancement,” CORISINTA, vol. 1, no. 1, pp. 80–86, 2024.
M. A. M. Farzaan, M. C. Ghanem, A. El-Hajjar, and D. N. Ratnayake, “Ai-powered system for an efficient and effective cyber incidents detection and response in cloud environments,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 3, pp. 623–643, 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.
OECD, Trends in Adult Learning: New Data from the 2023 Survey of Adult Skills, ser. Getting Skills Right. Paris: OECD Publishing, 2025. [Online]. Available: https://doi.org/10.1787/ec0624a6-en
A. Imashev, “Ai-driven zero-trust security framework for detecting advanced persistent threats in cloud environments,” ICONIC Res. Eng. JOURNALS, vol. 9, no. 5, pp. 477–491, 2025.
N. Fahmi, D. E. Hastasakti, D. Zaspiagi, R. K. Saputra, and S. Wijayanti, “A comparison of blockchain application and security issues from bitcoin to cybersecurity,” Blockchain Frontier Technology, vol. 2, no. 2, pp. 58–65, 2023.
N. Azeri, O. Hioual, and O. Hioual, “A distributed intelligence framework for enhancing resilience and data privacy in dynamic cyber-physical systems,” Cluster Computing, vol. 27, no. 5, pp. 6289–6304, 2024.
E. T. Rusmiati, L. Febrina, Y. Sari, and E. M. S. Sakti, “Adoption of ai driven ecological preaching systems using sem pls analysis,” Aptisi Transactions on Technopreneurship (ATT), vol. 8, no. 1, pp. 284–295, 2026.
A. Pandey, K. Bepari, R. Tiwari, V. Prakash, J. Singh, R. Singh, and H. Singh, “Self-learning ai agents for adaptive cyber defense in internet of things ecosystems,” networks, vol. 53, no. 54, p. 56, 2025.
T. H. Fadhil, M. I. Al-Karkhi, and L. A. Al-Haddad, “Legal and communication challenges in smart grid cybersecurity: classification of network resilience under cyber attacks using machine learning,” Journal of Communications, vol. 20, no. 2, 2025.
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