RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process

Abstract

An RNN-based forecasting approach is used to early detect anomalies in industrial multivariate time series data from a simulated Tennessee Eastman Process (TEP) with many cyber-attacks. This work continues a previously proposed LSTM-based approach to the fault detection in simpler data. It is considered necessary to adapt the RNN network to deal with data containing stochastic, stationary, transitive and a rich variety of anomalous behaviours. There is particular focus on early detection with special NAB-metric. A comparison with the DPCA approach is provided. The generated data set is made publicly available.

ICML 2017 Time Series Workshop, Sydney, Australia, 2017

References in corpus (1)

• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Cited by in corpus (3)

• Deep Learning for Anomaly Detection: A Survey
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
• Unsupervised Learning Based Robust Multivariate Intrusion Detection System for Cyber-Physical Systems using Low Rank Matrix