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 ...
Abstract: Video anomaly detection (VAD) is of great importance for a variety of real-time applications in video surveillance. Most deep learning-based anomaly detection algorithms adopt a one-class ...
This project focuses on fault detection in turbofan engines using the NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset, specifically the DS02 subset. The system combines ...
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 ...
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 ...
Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online ...
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