Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection

TL;DR

This paper introduces a scalable neuroevolution-based framework with multi-level optimization for designing deep learning architectures tailored for multivariate anomaly detection, demonstrating improved performance on multiple datasets.

Contribution

It presents a novel multi-level neuroevolution approach incorporating ensemble methods, non-gradient fine tuning, and optimized hyperparameters for unsupervised anomaly detection.

Findings

Achieved state-of-the-art scores on SWAT, WADI, MSL, and SMAP datasets.

Demonstrated faster architecture search with reduced training data.

Improved anomaly detection performance over existing deep learning models.

Abstract

Neuroevolution is one of the methodologies that can be used for learning optimal architecture during training. It uses evolutionary algorithms to generate the topology of artificial neural networks and its parameters. The main benefits are that it is scalable and can be fully or partially non gradient method. In this work, a modified neuroevolution technique is presented which incorporates multi-level optimisation. The presented approach adapts evolution strategies for evolving an ensemble model based on the bagging technique, using genetic operators for optimising single anomaly detection models, reducing the training dataset to speedup the search process and perform non-gradient fine tuning. Multivariate anomaly detection as an unsupervised learning task is the case study upon which the presented approach is tested. Single model optimisation is based on mutation and crossover operators and is focused on finding optimal window sizes, the number of layers, layer depths, hyperparameters etc. to boost the anomaly detection scores of new and already known models. The proposed framework and its protocol shows that it is possible to find architecture within a reasonable time frame which can boost all well known multivariate anomaly detection deep learning architectures. The work concentrates on improvements to the multi-level neuroevolution approach for anomaly detection. The main modifications are in the methods of mixing groups and single model evolution, non-gradient fine tuning and a voting mechanism. The presented framework can be used as an efficient learning network architecture method for any different unsupervised task where autoencoder architectures can be used. The tests were run on SWAT, WADI, MSL and SMAP datasets and the presented approach evolved the architectures that achieved the best scores among other deep learning models.