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Kea, K., Han, Y. and Kim, T.-K. (2023) Enhancing Anomaly Detection in Distributed Power Systems Using Autoencoder-Based Federated Learning. PLOS ONE, 18, e0290337.
https://doi.org/10.1371/journal.pone.0290337

has been cited by the following article:

• TITLE: Intrusion Detection for Edge-IoT Using LSTM-Autoencoder

  AUTHORS: Bodjré Aka Hugues Félix, Kié Eba Victoire, N’guessan N’takpé Christian Placide, Brou Pacôme, Asseu Olivier Pascal

  KEYWORDS: Intrusion Detection System (IDS), Edge Computing, Internet of Things (IoT), LSTM, Autoencoder, Zero-Day Attack

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.9, September 15, 2025

  ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on Raspberry Pi 4, our solution achieves an F1-score of 0.96 with 42 ms latency and detects anomalies, including zero-day attacks, with 97.2% accuracy on the TON_IoT and NSL-KDD datasets. Compared to CNN or Random Forest-based approaches, it consumes 40% fewer resources. A comparative analysis with Snort and Bro also reveals superior energy efficiency (1.8 W vs. 3.2 W) and better adaptability to dynamic environments.