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.

翻译：异常检测工具和方法是现代网络物理系统与故障预测系统中的关键能力。尽管用于异常检测的深度学习架构发展迅速，但针对特定数据集进行模型优化仍是一个繁琐且耗时的过程。神经进化作为一种完全自动化的最优神经网络搜索方法，支持基于梯度与非梯度的微调，可成为该问题的高效解决方案。然而，现有方法大多聚焦于优化模型架构，而未考虑特征子空间与模型权重。本文提出异常检测神经进化（AD-NEv）——一种面向多变量时间序列异常检测的可扩展多层优化神经进化框架。该方法采用创新协同策略：i）基于装袋技术优化集成模型的特征子空间；ii）优化单个异常检测模型的架构；iii）执行网络权重的非梯度微调。在广泛采用的多变量异常检测基准数据集上的大量实验评估表明，AD-NEv提取的模型性能优于已知的深度学习异常检测架构。此外，结果显示AD-NEv能高效完成整个流程，并在多GPU环境下展现出高可扩展性。