Anomaly Detection with Autoencoder for Unlabeled Data

Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

Abstract: This research proposes a lightweight hybrid approach for anomaly detection in correlated IoT sensor data, combining PCA for fast monitoring and Autoencoders for deeper analysis. Validated on ...

Scientific Research Publishing

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.

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 ...

GitHub

Process and analyze solar proton flux data from NOAA GOES11/13 satellites, including spectrum calculations, event detection, and NOAA S-scale categorization

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Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach

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.

Process and analyze solar proton flux data from NOAA GOES11/13 satellites, including spectrum calculations, event detection, and NOAA S-scale categorization

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