论文标题
LSTM用于网络物理系统中基于模型的异常检测
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems
论文作者
论文摘要
异常检测是检测数据与在给定情况下的正常行为不同的数据的任务。为了解决此问题，可以学习数据驱动的模型以预测当前或将来的观察结果。通常，异常行为取决于系统的内部动力学，并且在静态环境中看起来正常。为了解决此问题，该模型也应取决于状态。长期的短期记忆（LSTM）神经网络已被证明对于学习时间依赖性不同的时间序列特别有用，因此是学习任意复杂的网络物理系统行为的有趣的通用方法。为了执行异常检测，我们稍微修改了标准标准2误差以包含模型不确定性的估计值。我们分析了人工和真实数据的方法。
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. In order to perform anomaly detection, we slightly modify the standard norm 2 error to incorporate an estimate of model uncertainty. We analyse the approach on artificial and real data.
