# AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework

arXiv: https://arxiv.org/abs/2404.07968 · HTML: https://mlsanity.com/paper/2404.07968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://mlsanity.com/paper/2210.09071/) (2022)
- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [One-Class Adversarial Nets for Fraud Detection](https://mlsanity.com/paper/1803.01798/) (2018)
- [Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges](https://mlsanity.com/paper/2006.05415/) (2020)
- [Autoencoders for unsupervised anomaly detection in high energy physics](https://mlsanity.com/paper/2104.09051/) (2021)
- [Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders](https://mlsanity.com/paper/2006.13265/) (2020)
- [Deep Learning for Anomaly Detection in Log Data: A Survey](https://mlsanity.com/paper/2207.03820/) (2022)
- [MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation](https://mlsanity.com/paper/1812.00352/) (2018)
- [Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams](https://mlsanity.com/paper/1710.00811/) (2017)
- [Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection](https://mlsanity.com/paper/2010.15949/) (2020)
- [Ensemble neuroevolution based approach for multivariate time series anomaly detection](https://mlsanity.com/paper/2108.03585/) (2021)
- [Spectrum-Diverse Neuroevolution with Unified Neural Models](https://mlsanity.com/paper/1902.06703/) (2019)
- [MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders](https://mlsanity.com/paper/2106.11914/) (2021)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)

## Abstract (arXiv)

Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based 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 frameworks incorporating neuroevolution lack of support for new layers and architectures and are typically limited to convolutional and LSTM layers. In this paper we propose AD-NEv++, a three-stage neuroevolution-based method that synergically combines subspace evolution, model evolution, and fine-tuning. Our method overcomes the limitations of existing approaches by optimizing the mutation operator in the neuroevolution process, while supporting a wide spectrum of neural layers, including attention, dense, and graph convolutional layers. Our extensive experimental evaluation was conducted with widely adopted multivariate anomaly detection benchmark datasets, and showed that the models generated by AD-NEv++ outperform well-known deep learning architectures and neuroevolution-based approaches for anomaly detection. Moreover, results show that AD-NEv++ can improve and outperform the state-of-the-art GNN (Graph Neural Networks) model architecture in all anomaly detection benchmarks.