| Summary: | 碩士 === 逢甲大學 === 資訊工程學系 === 107 === With the advancement of modern science and technology, numerous applications of the Internet of Things are developing faster and faster. Smart grid is one of the examples which provides full communication, monitor, and control abilities to the components in the power systems in order to meet the increasing demands of reliable energy. In such systems, many components can be monitored and controlled remotely. As a result, they could be vulnerable to malicious cyber-attacks if there exist exploitable loopholes. In the power system, the disturbances caused by cyber-attacks are mixed with those caused by natural events. It is crucial for the intrusion detection systems in the smart grid to classify the types of disturbances and pinpoint the attacks with high accuracy.
The amount of information in a smart grid system is much larger than before, and the amount of computation of the big data increases accordingly. Many analyzing techniques have been proposed to extract useful information in these data and deep learning is one of them. It can be applied to “learn” a model from a large set of training data and classify unknown events from subsequent data. In this paper, we apply the methods of recurrent neural network (RNN) algorithm as well as two other variants to train models for intrusion detection in smart grid. Our experiment results showed that RNN can achieves high accuracy and precision on a set of real data collected from an experimental power system network.
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