Szczegóły publikacji
Opis bibliograficzny
AD-NEv: a scalable multilevel neuroevolution framework for multivariate anomaly detection / Marcin PIETROŃ, Dominik ŻUREK, Kamil FABER, Roberto Corizzo // IEEE Transactions on Neural Networks and Learning Systems ; ISSN 2162-237X . — 2025 — vol. 36 no. 5, s. 8939–8953. — Bibliogr. s. 8952–8953, Abstr. — Publikacja dostępna online od: 2024-08-14
Autorzy (4)
- AGHPietroń Marcin
- AGHŻurek Dominik
- AGHFaber Kamil
- Corizzo Roberto
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159702 |
|---|---|
| Data dodania do BaDAP | 2025-05-26 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TNNLS.2024.3439404 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | IEEE Transactions on Neural Networks and Learning Systems |
Abstract
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and nongradient fine-tuning. However, existing methods mostly focus on optimizing model architectures without taking into account feature subspaces and model weights. In this work, we propose anomaly detection neuroevolution (AD-NEv)—a scalable multilevel optimized neuroevolution framework for multivariate time-series anomaly detection. The method represents a novel approach to synergically: 1) optimize feature subspaces for an ensemble model based on the bagging technique; 2) optimize the model architecture of single anomaly detection models; and 3) perform nongradient fine-tuning of network weights. An extensive experimental evaluation on widely adopted multivariate anomaly detection benchmark datasets shows that the models extracted by AD-NEv outperform well-known deep learning architectures for anomaly detection. Moreover, results show that AD-NEv can perform the whole process efficiently, presenting high scalability when multiple graphics processing units (GPUs) are available.
