Federated learning-enabled lightweight intrusion detection system for wireless sensor networks: A cybersecurity approach against DDoS attacks in smart city environments
Background: Wireless Sensor Networks (WSNs) are vital in applications such as healthcare, smart cities, and environmental monitoring, but are vulnerable to cyberattacks due to their resource-constrained nature. Traditional Intrusion Detection Systems (IDS) depend on centralized architectures, which...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000791 |
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| Summary: | Background: Wireless Sensor Networks (WSNs) are vital in applications such as healthcare, smart cities, and environmental monitoring, but are vulnerable to cyberattacks due to their resource-constrained nature. Traditional Intrusion Detection Systems (IDS) depend on centralized architectures, which increase communication overhead and privacy risks and create a single point of failure. Objective: This paper proposes a novel Federated Learning-based Lightweight IDS (FL-LIDS) that utilizes optimized lightweight models to enable real-time, privacy-preserving DDoS attack detection in resource-constrained WSNs for smart city environments and presents a comprehensive comparative analysis of models to evaluate their effectiveness within the Federated Learning (FL) framework. Methods: FL-LIDS utilizes the optimized lightweight deep learning models for intrusion detection, which provides effective anomaly recognition with minimal resource usage, making it suitable for resource-limited WSN environments. The lightweight methods are evaluated in terms of their efficiency on the TON-IoT dataset. Results: The study demonstrates the effectiveness of various FL-LIDS in detecting and preventing DDoS attacks with high detection rates and minimal latency. Metrics used to examine performance include accuracy, F1-score, precision, and recall in emulated WSN scenarios. The lightweight deep learning architecture optimizes accuracy and computational cost, with the lightweight hybrid CNN + LSTM model achieving superior intrusion detection performance, making it ideal for WSN-based smart city environments. Conclusion: These cybersecurity systems provide a highly scalable and high-strength means of protecting smart city ecosystems in order to offer uninterrupted service provisioning. This research indicates that the FL provides an effective cybersecurity solution for WSNs. |
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| ISSN: | 2667-3053 |