Adnev A Scalable Multilevel Neuroevolution Framework For Multivariate Anomaly Detection

# AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection

**arXiv ID:** 2305.16497
**Authors:** Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo
**Published:** 2023-05-25T21:52:38Z
**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 non-gradient 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 multi-level optimized neuroevolution framework for multivariate time series anomaly detection. The method represents a novel approach to synergically: i) optimize feature subspaces for an ensemble model based on the bagging technique; ii) optimize the model architecture of single anomaly detection models; iii) perform non-gradient 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 GPUs are available.

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## References

- [arXiv:2305.16497](http://arxiv.org/abs/2305.16497v1)