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This work shows that production‐grade, privacy‐preserving intrusion detection is feasible in IoT networks at the edge, and addresses the concerns of data privacy and non‐IID data distribution inherent to distributed IoT networks.
The integration of smart meters into residential environments has allowed smooth collection of electricity consumption data, which is critical for demand response and coordinated operation in cyber-physical distributed energy resource systems. However, existing centralized methods of consumer characteristic identification pose significant risks to data privacy and confidentiality. Furthermore, the inherent limitations imposed due to noisy data cause a steep degradation in the accuracy and system-level decision-making. Addressing the issues of accuracy and data confidentiality, this paper proposes a confidence-aware federated learning framework for privacy-preserving inference of electricity consumer characteristics from raw smart meter data. The proposed method employs decentralized retention of smart meter data, using federated learning to refine network performance while ensuring that raw data remain localized at the client level. By evaluating a combined distribution of noisy and clean labels, erroneous data points are identified and excluded, thereby enhancing the model's efficacy and robustness. The effectiveness of the approach is validated using the Irish Commission for Energy Regulation dataset. The proposed framework demonstrates notable improvements, achieving an average gain of 3.97% in accuracy and 3.68% in MCC score compared to state-of-the-art methods, while maintaining strong data privacy guarantees.
The results demonstrate the effectiveness of the proposed Tiny-IDS in accurately identifying Mirai botnet attacks on IoT devices along with a minimal memory footprint and low inference time, while also emphasizing the need for IoT-specific evaluation frameworks to support the development of robust and lightweight IDS.
A Hybrid Multi-Modal Deep Learning framework that combines visual and structural features reduces modality collapse and creates a much stronger system compared to using either input type on its own, improves detection performance on deeply hidden contemporary threats, including the AveMariaRAT and CobaltStrike families.
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