Edge AI-based self-learning technique for mitigating DDoS attacks in WSN

Abstract

Wireless Sensor Networks (WSNs) are increasingly deployed in critical applications but remain highly susceptible to Distributed Denial of Service (DDoS) attacks. The constrained nature of WSNs-limited energy, memory, and processing power-renders traditional centralized intrusion detection systems inefficient due to high latency, bandwidth consumption, and privacy risks. This research presents a lightweight, edge AI-based self-learning framework that leverages federated deep learning to detect and mitigate DDoS attacks in WSNs without sharing raw sensor data. The proposed model enables local model training at individual nodes and aggregates updates securely at a central server, preserving data privacy while reducing network overhead. A multi-layer deep learning architecture is utilized to enhance anomaly detection capability across heterogeneous devices. Experimental results demonstrate the effectiveness of the approach, achieving 99.2 % accuracy, 96 % precision, 94 % recall, and improved corresponding F1-scores on benchmark datasets. Compared to traditional centralized models, the framework reduces latency by 31.7 % and communication overhead by 28 %, while maintaining high detection performance even under Non-IID data distributions. Additionally, it shows resilience to class imbalance and scalability across distributed nodes. These results confirm the framework’s suitability for real-time, privacy-preserving intrusion detection in modern WSNs. Future work will extend this system with adaptive client selection, adversarial robustness techniques, and deployment on live WSN hardware to validate real-time performance and security under evolving threat conditions.