TY - GEN
T1 - Binary- and Multi-class Network Intrusion Detection with Adaptive Synthetic Sampling and Deep Learning
AU - Jiang, Jehn Ruey
AU - Li, Chia Lin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85123052968&partnerID=8YFLogxK
U2 - 10.1109/ICCE-TW52618.2021.9603206
DO - 10.1109/ICCE-TW52618.2021.9603206
M3 - 會議論文篇章
AN - SCOPUS:85123052968
T3 - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
BT - 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Y2 - 15 September 2021 through 17 September 2021
ER -