Lstm For Model-Based Anomaly Detection In Cyber-Physical Systems
Benedikt Eiteneuer, Oliver Niggemann · Zenodo (CERN European Organization for Nuclear Research) · 2018
Presented on the 29th International Workshop on Principles of Diagnostics, Warsaw 2018 Anomaly detection is the task of detecting data which differs from the normal behaviour of a system in a given context. In order to approach this problem, data-driven models can be learned to predict current or future observations. Oftentimes, anomalous behaviour depends on the internal dynamics of the system and looks normal in a static context. To address this problem, the model should also operate depending on state. Long Short-Term Memory (LSTM) neural networks have been shown to be particularly useful to learn time sequences with varying length of temporal dependencies and are therefore an interesting general purpose approach to learn the behaviour of arbitrarily complex Cyber-Physical Systems.