Anomaly Detection for Water Treatment System based on Neural Network with Automatic Architecture Optimization
arXiv:1807.07282
Abstract
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.
arXiv comment9 pages
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Cited by in corpus (4)
- Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems
- Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
- Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks
- Software Enabled Security Architecture for Counteracting Attacks in Control Systems