# Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems

arXiv: https://arxiv.org/abs/2304.02947 · HTML: https://mlsanity.com/paper/2304.02947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection](https://mlsanity.com/paper/1903.06661/) (2019)
- [Anomaly Detection in High Dimensional Data](https://mlsanity.com/paper/1908.04000/) (2019)
- [In-Network Processing for Low-Latency Industrial Anomaly Detection in Softwarized Networks](https://mlsanity.com/paper/2112.03683/) (2021)
- [Real-Time Outlier Detection with Dynamic Process Limits](https://mlsanity.com/paper/2301.13527/) (2023)

## Abstract (arXiv)

This paper presents the Real-time Adaptive and Interpretable Detection (RAID) algorithm. The novel approach addresses the limitations of state-of-the-art anomaly detection methods for multivariate dynamic processes, which are restricted to detecting anomalies within the scope of the model training conditions. The RAID algorithm adapts to non-stationary effects such as data drift and change points that may not be accounted for during model development, resulting in prolonged service life. A dynamic model based on joint probability distribution handles anomalous behavior detection in a system and the root cause isolation based on adaptive process limits. RAID algorithm does not require changes to existing process automation infrastructures, making it highly deployable across different domains. Two case studies involving real dynamic system data demonstrate the benefits of the RAID algorithm, including change point adaptation, root cause isolation, and improved detection accuracy.