A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT
This addresses privacy and scalability issues in IoT security, though it is incremental as it applies existing federated learning methods to a specific domain.
The paper tackled botnet detection in IoT by proposing a lightweight federated learning framework that preserves privacy and reduces communication costs, achieving high detection accuracy in experiments on benchmark datasets.
The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these challenges, we present a lightweight and privacy-preserving botnet detection framework based on federated learning. This approach enables distributed devices to collaboratively train models without exchanging raw data, thus maintaining user privacy while preserving detection accuracy. A communication-efficient aggregation strategy is introduced to reduce overhead, ensuring suitability for constrained IoT networks. Experiments on benchmark IoT botnet datasets demonstrate that the framework achieves high detection accuracy while substantially reducing communication costs. These findings highlight federated learning as a practical path toward scalable, secure, and privacy-aware intrusion detection for IoT ecosystems.