Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

Anomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic. Current research in anomaly detection leverages offline learning working with static data or online learning fo...

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Bibliographic Details
Main Authors: Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Lifelong anomaly detection
lifelong learning
anomaly detection
continual learning
continual anomaly detection
Online Access:https://ieeexplore.ieee.org/document/10473036/

Internet

https://ieeexplore.ieee.org/document/10473036/

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