Real-Time Threat Detection in Mobile Networks Using an Adaptive AI-Based Firewall Framework
DOI:
https://doi.org/10.3991/ijim.v20i15.62594Keywords:
Dynamic Firewalls, Malware Detection, Fuzzy Systems, Identity and Access Management (IAM)Abstract
The rapid growth of cyber landscapes and the development of a new cybersecurity model incorporating PET, deep learning, fuzzy systems, keystroke dynamic authentication, and encryption. It is used to prevent attacks by malware or unauthorized access to cloud systems. The proposed framework, which integrates an artificial intelligence (AI)-driven approach with identity and access management (IAM), enables the adaptive implementation of risk-based login authentication and real time anomaly detection. Unlike conventional security systems that depend on fixed rules and signatures, we provide more sophisticated solutions. A Floydel firewall is dynamically tailored through deep neural networks (DNNs) and automatically adjusts to fluctuating traffic patterns. It employs malware classification based on behavior, utilizes fuzzy logic to manage uncertainty during intrusions, and uses keystroke dynamics for user verification through typing patterns. The experiment demonstrates a 97.6% detection accuracy on benchmark data, while significantly reducing false positives and ensuring data confidentiality through encryption. The nature of cloud security can evolve based on the specific circumstances and threats we encounter. Looking ahead, we plan to delve into cryptography and distributed training to bolster decentralized infrastructures. This proposed framework aims to fortify data protection and ensure user privacy in essential areas like healthcare, financial services, and e-governance, thereby fostering increased trust.
References
[1] Sivaguru, R., Srinath, R., Rubha, M. S., Banu, R. Y., & Kumar, K. S. (2024). Network traffic-based ransomware detection. International Education and Research Journal, 15 March 2024.
[2] Abcam, A., Cranshaw, L., Worthington, C., & Vandenberghe, J. (2025). Behavioral pattern analysis for real-time detection of ransomware attacks. OSF, 24 January 2025.
[3] Chao, J., & Xie, T. (2024). Deep learning-based network security threat detection and defense. International Journal of Advanced Computer Science and Applications, 1 January 2024.
[4] Hussain, A., Saadia, A., Alhussein, M., Gul, A., & Aurangzeb, K. (2024). Enhancing ransomware defense: Deep learning-based detection and family-wise classification of evolving threats. PeerJ, 29 November 2024.
[5] Roy, K. C., & Chen, Q. (2021). DeepRan: Attention-based BiLSTM and CRF for ransomware early detection and classification. Information Systems Frontiers, 1 April 2021.
[6] Kim, J.-S., & Park, K. W. (2022). Ransomware classification framework using the behavioral performance visualization of execution objects. CMC-Computers, Materials & Continua, 1 January 2022.
[7] Tarab, H. I. (2024). Cyber-attack detection and identification using deep learning. International Journal of Computing and Artificial Intelligence, 1 January 2024.
[8] Arivudainambi, D., Kumar, K. A. V., & Kumar, R. V. (2020). Ransomware traffic classification using deep learning models. International Journal of Web Portals, 1 January 2020.
[9] Okafor, M. O. (2024). Deep learning in cybersecurity: Enhancing threat detection and response. World Journal of Advanced Research and Reviews, 17 December 2024.
[10] Gürfidan, R., Atmaca, Ş., & Yi̇ği̇t, T. (2023). Real-time intelligent anomaly detection and prevention system. Sakarya University Journal of Computer and Information Sciences, 24 September 2023.
[11] Kumar, D., & Mathur, R. (2024). Ransomware detection using deep learning. International Journal of Research Publication and Reviews, 6(3), March 2024.
[12] Shah, S. A., & Zeadally, S. (2023). Network threat detection and classification using machine and deep learning. ACM Computing Surveys, 56(8), October 2023.
[13] Sethi, S. P., et al. (2023). Deep learning-based ransomware detection using high-resolution encoded images. IEEE Transactions on Dependable and Secure Computing, 20(4), July-August 2023.
[14] Arain, Q. A., Ahmed, F., & Rho, S. (2023). Towards network traffic classification using efficient machine learning algorithms. IEEE Access, 11, 2023.
[15] Liu, Y., Ye, J., Wu, X., & He, Y. (2024). Federated learning for ransomware attack detection in multi-edge environments. IEEE Transactions on Dependable and Secure Computing, Early Access, 2024.
[16] Debbarma, A., & Thakur, S. (2024). An improved CNN–LSTM-based hybrid deep learning model for ransomware detection. International Journal of Information Security and Privacy (IJISP), 18(1), 2024.
[17] Huda, S., Ahmed, E., Alrubaian, M., Al-Dhelaan, A., & Alzahrani, A. (2024). An ensemble deep learning approach for ransomware threat detection in large-scale systems. IEEE Access, 12, 2024.
[18] Nasser, M., Deebak, B. D., & Shankar, A. (2023). AI-driven malware and ransomware detection framework for smart healthcare systems. Computer Networks, 232, 109596, December 2023.
[19] Alazab, M., Khan, S., & Awad, A. I. (2023). Explainable artificial intelligence for cybersecurity: Threat detection and mitigation. IEEE Transactions on Network and Service Management, 20(3), September 2023.
[20] Kumar, R., Sharma, M., & Singh, H. (2022). Comparative study of CNN, LSTM, and hybrid CNN-LSTM models for anomaly detection in IoT networks. Journal of Ambient Intelligence and Humanized Computing, 13(9), 2022.
[21] Kaur, R., & Arora, A. (2023). Malware detection using quantum machine learning: A new frontier in cybersecurity. Quantum Information Processing, 22(4), April 2023.
[22] Singh, P., & Kaur, M. (2023). Adaptive deep learning framework for ransomware threat detection. Computers & Security, 132, 103208, October 2023.
[23] Alshamrani, A., et al. (2022). Intrusion detection in the era of quantum computing: A survey of current techniques and future directions. IEEE Communications Surveys & Tutorials, 24(2), April-June 2022.
[24] L. Fan, “Strategies for Enhancing Linguistic Teaching Effectiveness Based on Interactive Technologies in Mobile Learning Environments”, Int. J. Interact. Mob. Technol., vol. 20, no. 03, pp. pp. 30–44, Feb. 2026.
[25] Christhu Raj M. R., Vignesh S. R., and Rajeev Sukumaran, “Integrating Mobile Technologies with Energy Harvesting for Disaster Detection in Underwater Wireless Sensor Networks Using Stochastic Network Calculus”, Int. J. Interact. Mob. Technol., vol. 20, no. 03, pp. pp. 106–120, Feb. 2026.
[26] Gupta, R., & Bali, S. (2022). Quantum cryptography integrated with AI: A robust solution for ransomware protection. International Journal of Information Security Science, 11(2), 2022.
[27] Noor, R. M., & Nordin, N. H. (2023). AI-powered anomaly detection system for cloud computing environments. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), 2023.
[28] Dey, N., Ashour, A. S., & Balas, V. E. (2023). Ransomware detection and mitigation using intelligent systems: A survey. Computers & Electrical Engineering, 107, 108768, March 2023.
[29] Patel, A., & Mehta, A. (2022). Deep reinforcement learning for proactive cyber threat intelligence. Neural Computing and Applications, 34(11), 2022.
[30] Rasheed, M. B., & Khan, R. Z. (2023). Hybrid ensemble deep learning model for threat detection in industrial networks. IEEE Transactions on Industrial Informatics, 19(5), May 2023.
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Copyright (c) 2026 Sai Kiranmai Dornala, Senthil Kumar P.
This work is licensed under a Creative Commons Attribution 4.0 International License.
