Real-Time Detection and Prevention of IOT-Based DDOS Attacks in Network Environments

Briggs; Toala Dimabo; Sako; D. J. S; Nwiabu; N. D

doi:10.56201/ijasmt.vol.12.no9.2026pg24.33

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Real-Time Detection and Prevention of IOT-Based DDOS Attacks in Network Environments

Briggs, Toala Dimabo, Sako, D. J. S, Nwiabu, N. D

Abstract

The rapid growth of Inter-of-Things devices and their weak security configurations have significantly increased the vulnerability of modern network infrastructures to different forms of sophisticated cyber-attacks, particularly low-rate, stealthy, and botnet-driven DDoS attacks. Existing traditional intrusion detection and prevention systems often suffer from high latency, poor adaptability to evolving attack patterns, inadequate real-time response mechanisms, and limited privacy-preserving capabilities. Therefore, to address these limitations, this paper presents a real- time intelligent framework for the analysis, detection, and prevention of IoT-based Distributed Denial of Service attacks in network environments. The developed system utilized Long Short-Term Memory as the core real-time detection engine for identifying malicious IoT traffic patterns based on temporal and behavioral network features, and Reinforcement Learning (RL) technique to design an intelligent automated prevention and response mechanism capable of mitigating malicious traffic patterns dynamically. Furthermore, Homomorphic Encryption (HE) using the Cheon-Kim-Kim-Song scheme was integrated into the framework to preserve the privacy of sensitive network traffic during storage, analysis and transmission. The system was implemented using Python programming language, while MongoDB served as the NoSQL database for data storage and management. Experimental results showed that the proposed system achieved an excellent detection and prevention performance, recording a detection accuracy of 98.3%, precision of 97.8%, recall of 98.6%, and F1-score of 98.2%. Similarly, the reinforcement learning prevention module achieved an attack-blocking accuracy of 99.2% with a low false positive rate of 0.8%, confirming its effectiveness in dynamically mitigating malicious traffic while allowing legitimate network communications to proceed uninterrupted.

Keywords

Internet-of-ThingsLong Short-Term MemoryReinforcement LearningDistributed Denial of ServicePrivacy PreservationHomomorphic Encryption

References

[1] A. A. K. Majhi and S. Mohanty, 'A comprehensive review on internet of things applications in power systems,' IEEE Internet of Things Journal, 2024. [2] G. Mathur, R. K. Singh, M. Rakhra, and D. Prashar, 'Optimizing quality control in IIoT- based manufacturing: Leveraging big data analytics and IoT devices for enhanced decision- making strategies,' in Quality Assessment and Security in Industrial Internet of Things, ed: CRC Press, 2024, pp. 32-46. [3] Z. Shah, I. Ullah, H. Li, A. Levula, and K. Khurshid, 'Blockchain based solutions to mitigate distributed denial of service attacks in the Internet of Things : A survey,' Sensors, vol. 22, p. 1094, 2022. [4] M. H. Ali, M. M. Jaber, S. K. Abd, A. Rehman, M. J. Awan, R. Damaševičius, et al., 'Threat analysis and distributed denial of service attack recognition in the internet of things ,' Electronics, vol. 11, p. 494, 2022. [5] A. Fausto, G. Gaggero, F. Patrone, and M. Marchese, 'Reduction of the delays within an intrusion detection system based on software defined networking ,' IEEE Access, vol. 10, pp. 109850-109862, 2022. [6] I. N. Davies, O. E. Taylor, V. I. E. Anireh, and E. O. Bennett, 'Adaptive Hybrid Case-Based Neuro-Fuzzy Model for Intrusion Detection and Prevention for Smart Home Network,' International Journal of Computer Sciences and Engineering, vol. 10, pp. 01-10, 2024. [7] S. Ahmadi, 'Evolving botnet defenses: A survey of machine learning approaches for identifying polymorphic and evasive malware,' International Journal of Innovative Research and Scientific Studies, vol. 8, pp. 338-347, 2025. [8] N. Mohammad, 'Enhancing security and privacy in multi-cloud environments: A comprehensive study on encryption techniques and access control mechanisms,' International Journal of Computer Engineering and Technology , vol. 12, pp. 51- 63, 2021. [9] A. Pakmehr, A. Aßmuth, N. Taheri, and A. Ghaffari, 'DDoS attack detection techniques in IoT networks: a survey,' Cluster Computing, vol. 27, pp. 14637-14668, 2024. [10] K. Doshi, Y. Yilmaz, and S. Uludag, 'Timely detection and mitigation of stealthy DDoS attacks via IoT networks,' IEEE Transactions on Dependable and Secure Computing, vol. 18, pp. 2164-2176, 2021. [11] A. K. B. Arnob, M. Mridha, M. Safran, M. Amiruzzaman, and M. R. Islam, 'An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data,' International Journal of Computational Intelligence Systems, vol. 18, pp. 1-22, 2025. [12] S. Malekzadeh, S. Yousefi, and M. S. Tajbakhsh, 'DDos prevention in IoT networks by analyzing source-side inter-bot traffic using deep learning techniques,' The Journal of Supercomputing, vol. 81, p. 742, 2025. [13] I. N. Davies, O. E. Taylor, A. V. Ike, and B. E. Okoni, 'Node-Level Privacy Preservation Mechanism in Internet-of-Things Network using Elliptic Curve Cryptography,' Journal of Applied Computer Science & Mathematics, vol. 18, pp. 29-36, 2024. [14] M. Arazzi, S. Nicolazzo, and A. Nocera, 'A fully privacy-preserving solution for anomaly detection in iot using federated learning and homomorphic encryption,' Information Systems Frontiers, vol. 27, pp. 367-390, 2025.

Real-Time Detection and Prevention of IOT-Based DDOS Attacks in Network Environments

Abstract

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References

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