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.
V. K. Singh, Vins Patel, Neeraj Jain et al.· IEEE Transactions on Industr...· 0 citations
The exponential growth of IoT (Internet of Things) devices and their deployment has raised numerous threats of botnet-based cyberattacks, making IoT networks more vulnerable than traditional IT systems. The deployment of intelligent network-based intrusion detection systems (NIDS) on these edge devices forces researchers into an impossible trilemma: accuracy, speed, or size of the model. This study presents Tiny-IDS, an intelligent, lightweight system that predicts Mirai botnet attacks on IoT devices. Tiny-IDS is a lightweight and efficient Intrusion Detection System (IDS) that jointly optimizes for four key objectives: high accuracy, low inference latency, a nominal memory footprint, and high interpretability. The core of Tiny-IDS is a three-phase pipeline applied on N-BaIoT dataset. Model performance was evaluated using accuracy, inference time, model size, and interpretability. LightGBM achieved accuracy of 0.999999 but with a inference time of $0.904~\mu $ s/sample and bigger model size of 1.74 MB. Our proposed Distilled Student Tree outperformed other models in terms of efficiency, and achieved classification accuracy of 0.999562 while reducing inference time to $0.278~\mu $ s/sample and model size to 6.25 KB. 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.
Shyam Bahadur, Sudhanshu Kumar Jha, Rajkumar Singh Rathore et al.· IEEE Open Journal of the Com...· 0 citations
