# Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks

arXiv: https://arxiv.org/abs/1710.09207 · HTML: https://mlsanity.com/paper/1710.09207/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2017.

## Built on by (most cited first, 30 of 40)

- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Training OOD Detectors in their Natural Habitats](https://mlsanity.com/paper/2202.03299/) (2022)
- [Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/2105.10500/) (2021)
- [MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2308.15300/) (2023)
- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems](https://mlsanity.com/paper/2307.03761/) (2023)
- [Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol](https://mlsanity.com/paper/2106.16020/) (2021)
- [TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack](https://mlsanity.com/paper/2209.06388/) (2022)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer](https://mlsanity.com/paper/2408.15185/) (2024)
- [Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination](https://mlsanity.com/paper/2110.14825/) (2021)
- [Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data](https://mlsanity.com/paper/2305.09475/) (2023)
- [An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2309.09436/) (2023)
- [Extending Isolation Forest for Anomaly Detection in Big Data via K-Means](https://mlsanity.com/paper/2104.13190/) (2021)
- [DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2302.10753/) (2023)
- [GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks](https://mlsanity.com/paper/2504.00058/) (2025)
- [Unsupervised detection and open-set classification of fast-ramped flexibility activation events](https://mlsanity.com/paper/2111.02174/) (2021)
- [Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data](https://mlsanity.com/paper/2202.05538/) (2022)
- [A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset](https://mlsanity.com/paper/2303.09703/) (2023)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning](https://mlsanity.com/paper/2502.03245/) (2025)
- [Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings](https://mlsanity.com/paper/2507.12295/) (2025)
- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [Interactive System-wise Anomaly Detection](https://mlsanity.com/paper/2304.10704/) (2023)
- [AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn](https://mlsanity.com/paper/2306.01977/) (2023)
- [Unsupervised Anomaly Detection via Nonlinear Manifold Learning](https://mlsanity.com/paper/2306.09441/) (2023)

## Abstract (arXiv)

We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (OC-SVM) and Support Vector Data Description (SVDD) algorithms. As the first time in the literature, we jointly train and optimize the parameters of the LSTM architecture and the OC-SVM (or SVDD) algorithm using highly effective gradient and quadratic programming based training methods. To apply the gradient based training method, we modify the original objective criteria of the OC-SVM and SVDD algorithms, where we prove the convergence of the modified objective criteria to the original criteria. We also provide extensions of our unsupervised formulation to the semi-supervised and fully supervised frameworks. Thus, we obtain anomaly detection algorithms that can process variable length data sequences while providing high performance, especially for time series data. Our approach is generic so that we also apply this approach to the Gated Recurrent Unit (GRU) architecture by directly replacing our LSTM based structure with the GRU based structure. In our experiments, we illustrate significant performance gains achieved by our algorithms with respect to the conventional methods.