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Anomaly detection in smart environments: a comprehensive survey

Abstract: Anomaly detection is a critical task in ensuring the security and safety of infrastructure and individuals in smart environments. This paper provides a comprehensive analysis of recent anomaly detection solutions in data streams supporting smart environments, with a specific focus on multivariate time series anomaly detection in various environments, such as smart home, smart transport, and smart industry. The aim is to offer a thorough overview of the current state-of-the-art in anomaly detection techniques applicable to these environments. This includes an examination of publicly available datasets suitable for developing these techniques. The survey is designed to inform future research and practical applications in the field, serving as a valuable resource for researchers and practitioners. It not only reviews a range of state-of-the-art anomaly detection methods, from statistical and proximity-based to those adopting deep learning-methods but also covers fundamental aspects of anomaly detection. These aspects include the categorization of anomalies, detection scenarios, challenges associated, and evaluation metrics for assessing the techniques' performance.

 Autoría: Fahrmann D., Martin L., Sanchez L., Damer N.,

 Fuente: IEEE Access, 2024, 12, 64006 -64049

 Editorial: Institute of Electrical and Electronics Engineers, Inc.

 Fecha de publicación: 29/04/2024

 Nº de páginas: 44

 Tipo de publicación: Artículo de Revista

 DOI: 10.1109/ACCESS.2024.3395051

 ISSN: 2169-3536

 Proyecto español: PID2021-125725OB-I00

 Url de la publicación: https://doi.org/10.1109/ACCESS.2024.3395051

Autoría

FÄHRMANN , DANIEL

LAURA MARTIN GONZALEZ

DAMER , NASER