Binary- and Multi-class Network Intrusion Detection with Adaptive Synthetic Sampling and Deep Learning

Jehn Ruey Jiang, Chia Lin Li

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665433280
DOIs
出版狀態已出版 - 2021
事件8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan
持續時間: 15 9月 202117 9月 2021

出版系列

名字2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

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???event.eventtypes.event.conference???8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
國家/地區Taiwan
城市Penghu
期間15/09/2117/09/21

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