Intrusion Detection Method Based on Deep Learning
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Mingyuan Xin, Yong Wang and lin Fan
In order to solve the problem of high false alarm rate and low false alarm rate in massive network data intrusion detection, this paper proposes an intrusion detection model based on convolution neural network, which can improve the classification accuracy by selecting convolution core and data to extract local feature correlation features.
In-Depth learning, Intrusion detection, Artificial intelligence