Securing Internet of Things Devices with Federated Learning: A Privacy-Preserving Approach for Distributed Intrusion Detection
Source abstract
Abstract
The rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy. Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service (DDoS) and malware injections. Our evaluation on three benchmark IoT datasets demonstrates significant improvements: achieving 92.78% accuracy on NSL-KDD, 91.47% on BoT-IoT, and 92.05% on UNSW-NB15. The precision, recall, and F1-scores for all datasets consistently exceed 91%. Furthermore, the communication overhead was reduced to 85 MB for NSL-KDD, 105 MB for BoT-IoT, and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches. This study contributes to the domain by presenting a scalable, secure, and privacy-preserving solution tailored to the unique characteristics of IoT environments. The proposed framework is adaptable to dynamic and heterogeneous settings, with potential applications extending to other privacy-sensitive domains. Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Network Security and Intrusion Detection · Cryptography and Data Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
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