@inproceedings{9e2205cd186f439d885ee88826dbe3fb,
title = "Binary- and Multi-class Network Intrusion Detection with Adaptive Synthetic Sampling and Deep Learning",
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. ",
author = "Jiang, {Jehn Ruey} and Li, {Chia Lin}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; null ; Conference date: 15-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1109/ICCE-TW52618.2021.9603206",
language = "???core.languages.en_GB???",
series = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
}