تقرير
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process
| العنوان: | RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process |
|---|---|
| المؤلفون: | Filonov, Pavel, Kitashov, Fedor, Lavrentyev, Andrey |
| سنة النشر: | 2017 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Computer Science - Learning |
| الوصف: | 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. Comment: ICML 2017 Time Series Workshop, Sydney, Australia, 2017 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1709.02232 |
| رقم الأكسشن: | edsarx.1709.02232 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |