Abstract
Intrusion detection system (IDS) is becoming more and more important for detecting network intrusions, anomalies or attacks. This paper proposes a method that first uses adaptive synthetic (ADASYN) sampling to oversample data in small-size class, then uses deep learning models, such as the variational autoencoder (VAE), long short-term memory (LSTM) network, and deep neural network (DNN), for network intrusion detection. The well-known NSL-KDD dataset is applied to evaluate the effectiveness and superiority of the proposed method.
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