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

by   Marcin Pietroń, et al.

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.


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

Neuroevolution is one of the methodologies that can be used for learning...

TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model

Trajectory Prediction (TP) is an important research topic in computer vi...

Ensemble neuroevolution based approach for multivariate time series anomaly detection

Multivariate time series anomaly detection is a very common problem in t...

Visualization for Multivariate Gaussian Anomaly Detection in Images

This paper introduces a simplified variation of the PaDiM (Pixel-Wise An...

Feature anomaly detection system (FADS) for intelligent manufacturing

Anomaly detection is important for industrial automation and part qualit...

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Unsupervised anomaly detection with localization has many practical appl...

Anomaly Detection with Test Time Augmentation and Consistency Evaluation

Deep neural networks are known to be vulnerable to unseen data: they may...

Please sign up or login with your details

Forgot password? Click here to reset