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A Simple Recurrent Unit Model based Intrusion Detection System with DCGAN
2019
IEEE Access
Due to the complex and time-varying network environments, traditional methods are difficult to extract accurate features of intrusion behavior from the high-dimensional data samples and process the high-volume of these data efficiently. Even worse, the network intrusion samples are submerged into a large number of normal data packets, which leads to insufficient samples for model training; therefore it is accompanied by high false detection rates. To address the challenge of unbalanced positive
doi:10.1109/access.2019.2922692
fatcat:qzea74ipcfalrkwht4feypkexi