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Journal of Technologies Information and Communication

Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence
Kamrul Hasan 1, Forhad Hossain 2, Al Amin 2, Yadab Sutradhar 3, Israt Jahan Jeny 4, Shakik Mahmud 5 *
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1 Trine University, Indiana, United States
2 St. Francis College, Brooklyn, New York, United States
3 Maharishi International University, Fairfield, Iowa, United States
4 University of Bremen, Bremen, Germany
5 Japan-Bangladesh Robotics and Advanced Technology Research Center, Nilphamari, Bangladesh
* Corresponding Author
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Research Article

Journal of Technologies Information and Communication, 2025 - Volume 5 Issue 1, Article No: 33122
https://doi.org/10.55267/rtic/16176

Published Online: 17 Mar 2025

Views: 370 | Downloads: 152

Open Access
How to cite this article
APA 6th edition
In-text citation: (Hasan et al., 2025)
Reference: Hasan, K., Hossain, F., Amin, A., Sutradhar, Y., Jeny, I. J., & Mahmud, S. (2025). Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence. Journal of Technologies Information and Communication, 5(1), 33122. https://doi.org/10.55267/rtic/16176
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Hasan K, Hossain F, Amin A, Sutradhar Y, Jeny IJ, Mahmud S. Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence. Journal of Technologies Information and Communication. 2025;5(1):33122. https://doi.org/10.55267/rtic/16176
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Hasan K, Hossain F, Amin A, Sutradhar Y, Jeny IJ, Mahmud S. Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence. Journal of Technologies Information and Communication. 2025;5(1), 33122. https://doi.org/10.55267/rtic/16176
Chicago
In-text citation: (Hasan et al., 2025)
Reference: Hasan, Kamrul, Forhad Hossain, Al Amin, Yadab Sutradhar, Israt Jahan Jeny, and Shakik Mahmud. "Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence". Journal of Technologies Information and Communication 2025 5 no. 1 (2025): 33122. https://doi.org/10.55267/rtic/16176
Harvard
In-text citation: (Hasan et al., 2025)
Reference: Hasan, K., Hossain, F., Amin, A., Sutradhar, Y., Jeny, I. J., and Mahmud, S. (2025). Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence. Journal of Technologies Information and Communication, 5(1), 33122. https://doi.org/10.55267/rtic/16176
MLA
In-text citation: (Hasan et al., 2025)
Reference: Hasan, Kamrul et al. "Enhancing Proactive Cyber Defense: A Theoretical Framework for AI-Driven Predictive Cyber Threat Intelligence". Journal of Technologies Information and Communication, vol. 5, no. 1, 2025, 33122. https://doi.org/10.55267/rtic/16176
ABSTRACT
The rapid evolution of cyber threats and the dynamic nature of the threat landscape have necessitated the development of proactive and predictive defense mechanisms. This research proposes an AI-driven framework for predictive cyber threat intelligence aimed at enhancing organizational cybersecurity by identifying and mitigating threats before they materialize. The framework integrates diverse data sources, including network logs, endpoint data, and threat intelligence feeds, to generate actionable insights using advanced machine learning algorithms such as anomaly detection, pattern recognition, and predictive analytics. A continuous feedback loop ensures the adaptability of the framework through model retraining, anomaly adjustment, and performance monitoring. By leveraging supervised and unsupervised learning models, the framework addresses both known and unknown threats, providing scalable, real-time threat detection and risk assessment capabilities. This approach shifts the cybersecurity paradigm from reactive to proactive, enabling organizations to anticipate and counteract sophisticated cyber-attacks effectively. The proposed system’s application is demonstrated through practical scenarios, highlighting its potential to transform decision-making in high-stakes cybersecurity environments.
KEYWORDS
Cyber Threat Intelligence Artificial Intelligence Machine Learning Anomaly Detection Cybersecurity Risk Assessment
REFERENCES
  • Ahmed, M., Panda, S., Xenakis, C., & Panaousis, E. (2022, August). MITRE ATT&CK-driven cyber risk assessment. In Proceedings of the 17th International Conference on Availability, Reliability and Security (pp. 1-10).
  • Alsowail, R. A., & Al-Shehari, T. (2022). Techniques and countermeasures for preventing insider threats. PeerJ Computer Science, 8, e938.
  • Bang, H. N. (2021). A gap analysis of the legislative, policy, institutional and crises management frameworks for disaster risk management in Cameroon. Progress in Disaster Science, 11, 100190.
  • Bokhari, S., Hamrioui, S., & Aider, M. (2022). Cybersecurity strategy under uncertainties for an IoE environment. Journal of Network and Computer Applications, 205, 103426.
  • Bostani, H., & Sheikhan, M. (2017). Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach. Computer Communications, 98, 52-71.
  • Dekker, M. (2023). Managing the uncertainties of cybersecurity. Journal of Financial Transformation, 57, 8-13.
  • Dekker, M., & Alevizos, L. (2024). A threat‐intelligence driven methodology to incorporate uncertainty in cyber risk analysis and enhance decision‐making. Security and Privacy, 7(1), e333.
  • Dhirani, L. L., Armstrong, E., & Newe, T. (2021). Industrial IoT, cyber threats, and standards landscape: Evaluation and roadmap. Sensors, 21(11), 3901.
  • Gaber, M. G., Ahmed, M., & Janicke, H. (2024). Malware detection with artificial intelligence: A systematic literature review. ACM Computing Surveys, 56(6), 1-33.
  • George, G., & Thampi, S. M. (2019). Vulnerability-based risk assessment and mitigation strategies for edge devices in the Internet of Things. Pervasive and Mobile Computing, 59, 101068.
  • Giuca, O., Popescu, T. M., Popescu, A. M., Prostean, G., & Popescu, D. E. (2021). A survey of cybersecurity risk management frameworks. In Soft Computing Applications: Proceedings of the 8th International Workshop Soft Computing Applications (SOFA 2018), Vol. I 8 (pp. 240-272). Springer International Publishing.
  • Javaheri, D., Fahmideh, M., Chizari, H., Lalbakhsh, P., & Hur, J. (2024). Cybersecurity threats in FinTech: A systematic review. Expert Systems with Applications, 241, 122697
  • Kaur, H., SL, D. S., Paul, T., Thakur, R. K., Reddy, K. V. K., Mahato, J., & Naveen, K. (2024). Evolution of endpoint detection and response (edr) in cyber security: A comprehensive review. In E3S Web of Conferences (Vol. 556, p. 01006). EDP Sciences.
  • Manoharan, G., Sharma, A., Vani, V. D., Raj, V. H., Jain, R., & Nijhawan, G. (2024). Predictive Analytics for Inventory Management in E-commerce Using Machine Learning Algorithms. In 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-5). IEEE.
  • Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), 856.
  • Rizky, A., Firli, M. Z., Lindzani, N. A., Audiah, S., & Pasha, L. (2024). Advanced cyber threat detection: Big data-driven ai solutions in complex networks. Journal of Computer Science and Technology Application, 1(2), 136-143.
  • Vanamala, M., Yuan, X., Smith, W., & Bennett, J. (2022). Interactive Visualization Dashboard for Common Attack Pattern Enumeration Classification. In Proceedings of the International Conference on Software Engineering and Applications (ICSEA 2022) (Vol. 2022, p. 79).
  • Welburn, J. W., & Strong, A. M. (2022). Systemic cyber risk and aggregate impacts. Risk Analysis, 42(8), 1606-1622.
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