How to cite this paper
APA: Mamodiya, U., Kishor, I., Almaiah, M., Alqutaish, A., Shehab, R & Obeidat, M. (2026). Behavior-aware cybersecurity using artificial intelligence and cryptographic intelligence. International Journal of Data and Network Science, 10(2), 699-722.
Chicago/Turabian: Mamodiya, U., Kishor, I., Almaiah, M., Alqutaish, A., Shehab, R & Obeidat, M. 2026. "Behavior-aware cybersecurity using artificial intelligence and cryptographic intelligence." International Journal of Data and Network Science 10, no. 2 (2026): 699-722.
AMA: Mamodiya, U., Kishor, I., Almaiah, M., Alqutaish, A., Shehab, R & Obeidat, M. Behavior-aware cybersecurity using artificial intelligence and cryptographic intelligence. International Journal of Data and Network Science. 2026;10(2):699-722.
References
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Aljumaiah, O., Jiang, W., Reddy Addula, S., & Amin Almaiah, M. (2025). Analyzing cybersecurity risks and threats in IT infrastructure based on NIST framework. Journal of Cyber Security and Risk Auditing, 2025(2), 12–26. https://doi.org/10.63180/jcsra.thestap.2025.2.2
Almaayah, M., & Sulaiman, R. B. (2024). Cyber risk management in the Internet of Things: Frameworks, models, and best practices. STAP Journal of Security Risk Management, 2024(1), 3–23. https://doi.org/10.63180/jsrm.thestap.2024.1.1
Almaiah, M. A., & Kadel, R. (2025). Leveraging ACO, GA, and GWO for enhancing port scan attack detection using machine learning. Journal of Cyber Security and Risk Auditing, 2025(4), 306–326. https://doi.org/10.63180/jcsra.thestap.2025.4.9
Almarshood, R., & Rahman, M. M. H. (2025). Enhancing intrusion detection systems by using machine learning in smart cities: Issues, challenges and future research direction. STAP Journal of Security Risk Management, 2025(1), 3–21. https://doi.org/10.63180/jsrm.thestap.2025.1.1
Almasabi, M., Khemakhem, M., Eassa, F., Abi Sen, A. A., Alkhodre, A., & Harbaoui, A. (2024). A smart framework to detect threats and protect data of IoT based on machine learning. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3498603
Al-Mukhtar, W. N. M. (2024). AI in cybersecurity: Transformative approaches to safeguarding information technology systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 391–412. https://doi.org/10.61841/turcomat.v15i3.14945
Al-Na’amneh, Q., Aljawarneh, M., Alhazaimeh, A. S., Hazaymih, R., & Shah, S. M. (2025). Securing trust: Rule-based defense against on/off and collusion attacks in cloud environments. STAP Journal of Security Risk Management, 2025(1), 85–114. https://doi.org/10.63180/jsrm.thestap.2025.1.5
Alotaibi, A., Aldawghan, H., & Rahman, M. M. H. (2026). IoT security concerns with non-fungible tokens: A review. STAP Journal of Security Risk Management, 2026(1), 1–30. https://doi.org/10.63180/jsrm.thestap.2026.1.1
Al-Quayed, F., Ahmad, Z., & Humayun, M. (2024). A situation-based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of Industry 4.0. IEEE Access, 12, 34800–34819. https://doi.org/10.1109/ACCESS.2024.3372187
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Alshuaibi, A., Almaayah, M., & Ali, A. (2025). Machine learning for cybersecurity issues: A systematic review. Journal of Cyber Security and Risk Auditing, 2025(1), 36–46. https://doi.org/10.63180/jcsra.thestap.2025.1.4
Ang, S., Ho, M., Huy, S., & Janarthanan, M. (2026). A multi-layered adaptive cybersecurity framework for the banking sector integrating next-generation firewalls with AI-driven IDPS. STAP Journal of Security Risk Management, 2026(1), 67–76. https://doi.org/10.63180/jsrm.thestap.2026.1.4
Asiri, M. M., Alfraihi, H., Said, Y., Othman, K. M., Salama, A. S., & Marzouk, R. (2024). Securing consumer electronics devices: A blockchain-based access management approach enhanced by deep learning threat modeling for IoT ecosystems. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3441094
Bondhala, S. (2025). Cybersecurity in AI-driven data centers: Reinventing threat detection. International Journal of Advanced Research in Science, Communication and Technology, 510–519. https://doi.org/10.48175/ijarsct-24464
Cano Garcia, J. L., et al. (2024). Securing AI systems: A comprehensive overview of cryptographic techniques for enhanced confidentiality and integrity. Proceedings of IEEE MECO. https://doi.org/10.1109/meco62516.2024.10577883
Czaja, P., Gdowski, B., Niemiec, M., et al. (2025). Cybersecurity challenges and opportunities of machine learning-based artificial intelligence. Neural Computing and Applications, 37, 27931–27956. https://doi.org/10.1007/s00521-025-11604-9
Edim, B. E., Akpan, I. U., & Omotosho, M. O. (2025). AI-augmented cybersecurity threat intelligence: Enhancing situational awareness. International Journal of Science and Research Archive, 14(1), 890–897. https://doi.org/10.30574/ijsra.2025.14.1.2650
Ejeofobiri, C. K., et al. (2024). The role of artificial intelligence in enhancing cybersecurity. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2024.13.2.2161
Falowo, O. I., Botsyoe, L. E., Koshoedo, K., & Özer, M. (2024). Enhancing cybersecurity with artificial immune systems and general intelligence. IEEE Access, 12, 123811–123822. https://doi.org/10.1109/ACCESS.2024.3454543
Frade, J. M. C. B. C., dos Santos, L. D., & Costa, R. L. de C. (2024). DBD-Guardian and privacy-aware near real-time cybersecurity analytics. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3477979
Gampel, A., & Eveleigh, T. (2025). Model-based systems engineering cybersecurity risk assessment for industrial control systems leveraging NIST risk management framework methodology. Journal of Cyber Security and Risk Auditing, 2025(4), 204–221. https://doi.org/10.63180/jcsra.thestap.2025.4.2
Gharibi, W. (2025). Machine learning and cybersecurity—Trends and future challenges. Electronics, 14(20), Article 4007. https://doi.org/10.3390/electronics14204007
Gracious, L. A. A. (2025). The convergence of artificial intelligence and cybersecurity. In Advances in information security, privacy, and ethics (pp. 463–476). https://doi.org/10.4018/979-8-3373-0563-9.ch028
He, Z., Davila, D., Bi, S., Wang, T., & Hou, T. (2025). Machine learning for cybersecurity: A survey of applications, adversarial challenges, and future research directions. Electronics, 14(23), Article 4563. https://doi.org/10.3390/electronics14234563
Hoseini, S. V., Suutala, J., Partala, J., & Halunen, K. (2024). Threat modeling AI/ML with the attack tree. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3497011
Ishtaiwi, M. A., Al Khaldy, A., Al-Qerem, A., Aldweesh, A., & Almomani, A. (2024). Artificial intelligence in cryptographic evolution. In Advances in computational intelligence and robotics. https://doi.org/10.4018/979-8-3693-5330-1.ch002
Jaafar, H. S., Abed, A. A., & Al-Shareeda, M. A. (2026). A secure industrial Internet of Things (IIoT) framework for real-time PI control and cloud-integrated industrial monitoring. STAP Journal of Security Risk Management, 2026(1), 77–86. https://doi.org/10.63180/jsrm.thestap.2026.1.5
Jana, S., Biswas, R., Banerjee, C., Patra, T., Pal, M., & Pal, K. (2024). Leveraging artificial intelligence for enhancing cybersecurity: A comprehensive review and analysis. International Journal of Advanced Research in Science, Communication and Technology, 173–183. https://doi.org/10.48175/ijarsct-19030
Jones, R. K. (2025). Advanced defense techniques: Beyond traditional security measures. In Advanced cybersecurity strategies (pp. 153–178). https://doi.org/10.1002/9781394340750.ch7
Karunamurthy, & KiruthivasanR. (2023). Human-in-the-loop intelligence: Advancing AI-centric cybersecurity for the future. Quing: International Journal of Multidisciplinary Scientific Research and Development. https://doi.org/10.54368/qijmsrd.2.3.0011
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McDonald, G. L., Li, L., & Al Mallah, R. (2024). Finding the optimum security policies to autonomous cyber operations with competitive reinforcement learning. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3446310
Mishra, M., Pradhan, R. C., Agrawalla, K., & Bokka, R. (2025). AI for cybersecurity threat detection: A machine-enabled computing perspective. Proceedings of IEEE ASSIC, 1–5. https://doi.org/10.1109/assic64892.2025.11158640
Mohamed, N. (2024). Artificial intelligence in cybersecurity: A review of solutions for APT-exploited vulnerabilities. Proceedings of IEEE ICCCNT, 1–7. https://doi.org/10.1109/icccnt61001.2024.10724084
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