  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.
