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Spatio-temporal anomaly detection in computer networks using graph convolutional recurrent neural networks (GCRNNs)
Spatio-temporal anomaly detection in computer networks using graph convolutional recurrent neural networks (GCRNNs)
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机译:使用图卷积递归神经网络(GCRNN)在计算机网络中进行时空异常检测
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摘要
In one embodiment, a device receives sensor data from a plurality of nodes in a computer network. The device uses the sensor data and a graph that represents a topology of the nodes in the network as input to a graph convolutional neural network. The device provides an output of the graph convolutional neural network as input to a convolutional long short-term memory recurrent neural network. The device detects an anomaly in the computer network by comparing a reconstruction error associated with an output of the convolutional long short-term memory recurrent neural network to a defined threshold. The device initiates a mitigation action in the computer network for the detected anomaly.
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