# Anomaly Detection using Autoencoders in High Performance Computing Systems

arXiv: https://arxiv.org/abs/1811.05269 · HTML: https://mlsanity.com/paper/1811.05269/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 25 of 25)

- [ADTR: Anomaly Detection Transformer with Feature Reconstruction](https://mlsanity.com/paper/2209.01816/) (2022)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Autoencoders for Semivisible Jet Detection](https://mlsanity.com/paper/2112.02864/) (2021)
- [Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning](https://mlsanity.com/paper/2407.17545/) (2024)
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- [Unveiling Multiple Descents in Unsupervised Autoencoders](https://mlsanity.com/paper/2406.11703/) (2024)
- [Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark](https://mlsanity.com/paper/2501.06591/) (2025)
- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN](https://mlsanity.com/paper/2308.06663/) (2023)
- [XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection](https://mlsanity.com/paper/2502.09194/) (2025)
- [Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators](https://mlsanity.com/paper/2101.04645/) (2021)
- [GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs](https://mlsanity.com/paper/2106.15504/) (2021)
- [R2-AD2: Detecting Anomalies by Analysing the Raw Gradient](https://mlsanity.com/paper/2206.10259/) (2022)
- [Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data](https://mlsanity.com/paper/2405.18929/) (2024)
- [TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization](https://mlsanity.com/paper/2512.07082/) (2025)
- [Online Self-Evolving Anomaly Detection in Cloud Computing Environments](https://mlsanity.com/paper/2111.08232/) (2021)
- [Unveil the unseen: Exploit information hidden in noise](https://mlsanity.com/paper/2209.08376/) (2022)
- [Improved Anomaly Detection through Conditional Latent Space VAE Ensembles](https://mlsanity.com/paper/2410.12328/) (2024)
- [Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare](https://mlsanity.com/paper/2103.10478/) (2021)
- [Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection](https://mlsanity.com/paper/2110.01447/) (2021)
- [Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data](https://mlsanity.com/paper/2202.05093/) (2022)
- [Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2205.14676/) (2022)
- [Self-supervised Learning for Anomaly Detection in Computational Workflows](https://mlsanity.com/paper/2310.01247/) (2023)
- [MTAD: Tools and Benchmarks for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2401.06175/) (2024)
- [Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals](https://mlsanity.com/paper/2506.18288/) (2025)

## Abstract (arXiv)

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states). We propose a novel approach for anomaly detection in High Performance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with). We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).