# Diagnosis driven Anomaly Detection for CPS

arXiv: https://arxiv.org/abs/2311.15924 · HTML: https://mlsanity.com/paper/2311.15924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 27 Nov 2023.

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

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data](https://mlsanity.com/paper/2009.04756/) (2020)
- [How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms](https://mlsanity.com/paper/2207.12583/) (2022)
- [Finding faults: A scoping study of fault diagnostics for Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2101.05451/) (2021)

## Abstract (arXiv)

In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks. However, diagnosis algorithms require symptoms, i.e. temporally and spatially isolated anomalies, as input. Thus, anomaly detection and diagnosis must be developed together to provide a holistic solution for diagnosis in CPS. We therefore propose a method for utilizing deep learning-based anomaly detection to generate inputs for Consistency-Based Diagnosis (CBD). We evaluate our approach on a simulated and a real-world CPS dataset, where our model demonstrates strong performance relative to other state-of-the-art models.