Machine Learning

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

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  • arXiv
    5 pts

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

    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.

    RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process
    by Pavel Filonov, Fedor Kitashov, Andrey Lavrentyev
    https://arxiv.org/pdf/1709.02232v1.pdf

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