# Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation

arXiv: https://arxiv.org/abs/2305.08977 · HTML: https://mlsanity.com/paper/2305.08977/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Online Learning With Adaptive Rebalancing in Nonstationary Environments](https://mlsanity.com/paper/2009.11942/) (2020)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [Queue-based Resampling for Online Class Imbalance Learning](https://mlsanity.com/paper/1809.10388/) (2018)
- [Data-efficient Online Classification with Siamese Networks and Active Learning](https://mlsanity.com/paper/2010.01659/) (2020)
- [A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification](https://mlsanity.com/paper/2210.04949/) (2022)
- [Data augmentation on-the-fly and active learning in data stream classification](https://mlsanity.com/paper/2210.06873/) (2022)

## Built on by (most cited first, 9 of 9)

- [Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification](https://mlsanity.com/paper/2404.02572/) (2024)
- [Online Detection of Water Contamination Under Concept Drift](https://mlsanity.com/paper/2501.02107/) (2025)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework](https://mlsanity.com/paper/2508.07085/) (2025)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)
- [Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks](https://mlsanity.com/paper/2508.16336/) (2025)
- [Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams](https://mlsanity.com/paper/2602.09681/) (2026)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)
- [Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams](https://mlsanity.com/paper/2605.29834/) (2026)

## Abstract (arXiv)

In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environments, which can cause deterioration of the predictive performance of a model. To address the above challenges, the paper proposes an autoencoder-based incremental learning method with drift detection (strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD. We further conduct a comparative study, showing that the proposed method significantly outperforms existing baseline and advanced methods.