Citation :

M.N. Karuppusamy, N. Sasirekha, "MA-RL and WOA-Based Secure Routing: A Federated Learning Approach for Trust Evaluation and DoS Attack Mitigation in IoT Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 109-135, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P108

Abstract

The growing size and sophistication of the Internet of Things (IoT) has brought up the issue of network security and routing efficiency, especially in unfriendly settings where Denial-of-Service (DoS) attacks are frtaent. Conventional trust-based routing algorithms are not very flexible and are not capable of adequately addressing these dynamic threats. This paper puts forward a well-constructed smart and secure routing architecture that incorporates Multi-Agent Reinforcement Learning (MA-RL), Whale Optimization Algorithm (WOA), and Federated Learning (FL) to carry out more sophisticated trust scoring and DoS attack early warning. The framework is designed in three central steps, namely: (1) dynamic trust by MA-RL on the basis of metrics such as Packet Forwarding Ratio (PFR), rewards/penalties, and Received Signal Strength Indicator (RSSI); (2) privacy-preserving DoS detection via FL-based decentralized anomaly detection; and (3) optimized secure path selection with the use of WOA, where there is trust, energy efficiency, and a low latency. Moreover, a predictive time-series model predicts degradation of trust, which enables malicious nodes to be isolated in advance. The experimental assessment of the simulated IoT settings during the DoS attacks proves considerable positive changes in the detection rate, routing speed, and network stability. The proposed model has a high potential of securing the next-generation IoT networks as it has an adaptive learning capacity which is scalable.

Keywords

IoT, Security, DoS attack detection, Federated Learning, Trust management, Whale Optimization Algorithm.

References

[1] Constantinos Kolias et al., “Security, Privacy, and Trust on Internet of Things” Wireless Communications and Mobile Computing, vol. 2019, no. 1, pp. 1-3, 2019.
[CrossRef] [Google Scholar] [Publisher Link]  

[2] Vishal Sharma et al., “Security, Privacy and Trust for Smart Mobile-Internet of Things (M-IoT): A Survey,” IEEE access, vol. 8, pp. 167123-167163, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Mirsaeid Hosseini Shirvani, and Mohammad Masdari, “A Survey Study on Trust-based Security in Internet of Things: Challenges and Issues,” Internet of Things, vol. 21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Mumin Adam et al., “A Survey on Security, Privacy, Trust, and Architectural Challenges in IoT Systems,” IEEE Access, vol. 12, pp. 57128-57149, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Shan Li, Muddesar Iqbal, and Neetesh Saxena, “Future Industry Internet of Things with Zero-Trust Security,” Information Systems Frontiers, vol. 26, no. 5, pp. 1653-1666, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Chandra Prabha Kaliappan et al., “Advancing IoT Security: A Comprehensive AI-based Trust Framework for Intrusion Detection,” Peer-to-Peer Networking and Applications, vol. 17, no. 5, pp. 2737-2757, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Izhar Ahmed Khan et al., “A Context-Aware Zero Trust-based Hybrid Approach to IoT-based Self-Driving Vehicles Security,” Ad Hoc Networks, vol. 167, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Anup W. Burange et al., “Safeguarding the Internet of Things: Elevating IoT Routing Security through Trust Management Excellence,” Computer Standards and Interfaces, vol. 91, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[9] S. Aravindan, and A. Rajaram, “Energy-Aware Multi-Attribute Trust Modal for Secure MANET-IoT Environment,” Multimedia Tools and Applications, vol. 83, no. 38, pp. 85637-85662, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Guillen-Perez Antonio, and Cano Maria-Dolores, “Multi-Agent Deep Reinforcement Learning to Manage Connected Autonomous Vehicles at Tomorrow's Intersections,” IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7033-7043, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Thang Quoc Nguyen et al., “Multi-Agent Reinforcement Learning in Designing the Low-Dropout Regulator Circuits,” ACM Transactions on Design Automation of Electronic Systems, vol. 31, no. 6, pp. 1-20, 2026.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Mohammad H. Nadimi-Shahraki et al., “A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations,” Archives of Computational Methods in Engineering, vol. 30, no. 7, pp. 4113-4159, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[13] Hardi M. Mohammed, Shahla U. Umar, and Tarik A. Rashid, “A Systematic and Meta‐Analysis Survey of Whale Optimization Algorithm,” Computational intelligence and neuroscience, vol. 2019, no. 1, pp. 1-25, 2019.
[CrossRef] [Google Scholar] [Publisher Link] 

[14] Dinh C. Nguyen et al., “Federated Learning for Internet of Things: A Comprehensive Survey,” IEEE Communications Surveys and Tutorials, vol. 23, no. 3, pp. 1622-1658, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Ahmed Imteaj et al., “A Survey on Federated Learning for Resource-Constrained IoT Devices,” IEEE Internet of Things Journal, vol. 9, no. 1, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Amer Aljaedi et al., “A Lightweight Encryption Algorithm for Resource-Constrained IoT Devices using Quantum and Chaotic Techniques with Metaheuristic Optimization,” Scientific Reports, vol. 15, no. 1, pp. 1-26, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Liang Wang, Yilin Li, and Lina Zuo, “Trust Management for IoT Devices based on Federated Learning and Blockchain,” The Journal of Supercomputing, vol. 81, no. 1, pp. 1-28, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Yang Zhang et al., “Post-Quantum Secure Identity-based Signature Scheme with Lattice Assumption for Internet of Things Networks,” Sensors, vol. 24, no. 13, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Kobra Mabodi et al., “Multi-level Trust-based Intelligence Schema for Securing of Internet of Things (IoT) against Security Threats using Cryptographic Authentication,” The Journal of Supercomputing, vol. 76, no. 9, pp. 7081-7106, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Guntur Dharma Putra et al., “Trust-based Blockchain Authorization for IoT,” IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 1646-1658, 2021.
[CrossRef] [Google Scholar] [Publisher Link] 

[21] Mohammad Faisal et al., “Establishment of Trust in Internet of Things by Integrating Trusted Platform Module: To Counter Cybersecurity Challenges,” Complexity, vol. 2020, no. 1, pp. 1-9, 2020.
[CrossRef] [Google Scholar] [Publisher Link]  

[22] Zhang Xiaojian et al., “Power IoT Security Protection Architecture based on Zero Trust Framework,” 2021 IEEE 5^th International Conference on Cryptography, Security and Privacy (CSP), Zhuhai, China, pp. 166-170, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Rajesh Kumar, and Rewa Sharma, “AI-Driven Dynamic Trust Management and Blockchain-based Security in Industrial IoT,” Computers and Electrical Engineering, vol. 123, 2025.
[CrossRef] [Google Scholar] [Publisher Link]  

[24] Ali M. Al-Sharafi et al., “Ensuring Zero Trust Security in Consumer Internet of Things using Federated Learning-based Attack Detection Model,” IEEE Access, vol. 13, pp. 54423-54438, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Torana Kamble et al., “Secure Data Transmission in Cloud Computing using a Cyber-Security Trust Model with Multi-Risk Protection Scheme in Smart IOT Application,” Cluster Computing, vol. 28, no. 2, 2024.
[CrossRef] [Google Scholar] [Publisher Link]  

[26] Maryam Naghibi, Hamid Barati, and Ali Barati, “A Dynamic Trust based Clustering Method for Secure Data Gathering in Internet of Things,” Computing, vol. 107, no. 4, 2025. 
[CrossRef] [Google Scholar] [Publisher Link] 

[27] Wei Zheng, Tomley Anwlnkom, and Ye Liu, “Trust-Med6G: A Secure and Trustworthy Medical Data Privacy Protection Mechanism in 6g-IoT,” IEEE Internet of Things Journal, vol. 13, no. 5, pp. 8248-8260, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[28] Ali Farhat et al., “Interaction-Aware Trust Management Scheme for IoT Systems with Machine Learning-based Attack Detection,” IEEE Internet of Things Journal, vol. 12, no. 11, pp. 17169-17182, 2025.
[CrossRef] [Google Scholar] [Publisher Link] 

[29] Ting Li et al.., “TCDT: A Trust-Enabled Crowdsourced Data Trading System in Intelligent Blockchain over Internet of Things,” Expert Systems with Applications, vol. 265, 2025.
[CrossRef] [Google Scholar] [Publisher Link]  

[30] Xiangyu Liu, and Yanhui Du, “Towards Effective Feature Selection for IoT Botnet Attack Detection using a Genetic Algorithm,” Electronics, vol. 12, no. 5, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[31] A.B. Feroz Khan et al., “DDoS Attack Modeling and Resistance using Trust based Protocol for the Security of Internet of Things,” Journal of Engineering Research, vol. 11, no. 2, pp. 1-7, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[32] Shuqin Zhang et al., “Multi-Source Knowledge Reasoning for Data-Driven IoT Security,” Sensors, vol. 21, no. 22, pp. 1-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]