Abstract: The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The ...
ABSTRACT: Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex ...
ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...
We showcase a novel unsupervised learning method with a Convolutional Variational Autoencoder (CVAE) model that can automatically classify and cluster different types ...
To say that neutrinos aren’t the easiest particles to study would be a bit of an understatement. Outside of dark matter, there’s not much in particle physics that is as slippery as the elusive “ghost ...
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A complete workflow for building, training, and deploying a lightweight LSTM Autoencoder anomaly detector for temperature data on the ESP32 microcontroller—without TensorFlow or TFLite. This project ...
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