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Li et al. [12] proposed an autoencoder intrusion detection system based on the random forest algorithm, which used shallow autoencoder neural network to reduce the computational complexity, shorten the detection time, and effectively improve the detection accuracy. Yisroel Mirsky et al. [13] proposed a online, unsupervised and efficient network intrusion detection system, which is Kitsune based on ANN. In this paper, we use autoencoder as the anomaly detection module, which is inspired by Kitsune. |
IEEE/ICACT20220329 Slide.05
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