# Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization

arXiv: https://arxiv.org/abs/1807.07282 · HTML: https://mlsanity.com/paper/1807.07282/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals](https://mlsanity.com/paper/2107.12626/) (2021)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2202.10001/) (2022)
- [Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System](https://mlsanity.com/paper/2305.00982/) (2023)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)

## Abstract (arXiv)

We continue to develop our neural network (NN) based forecasting approach to anomaly detection (AD) using the Secure Water Treatment (SWaT) industrial control system (ICS) testbed dataset. We propose genetic algorithms (GA) to find the best NN architecture for a given dataset, using the NAB metric to assess the quality of different architectures. The drawbacks of the F1-metric are analyzed. Several techniques are proposed to improve the quality of AD: exponentially weighted smoothing, mean p-powered error measure, individual error weight for each variable, disjoint prediction windows. Based on the techniques used, an approach to anomaly interpretation is introduced.