@inproceedings{d8fd680e94934b23b3b082594c2b4698,
title = "Network Intrusion Detection Using CNN-based Classification Method",
abstract = "With the rapid advancement of Artificial Intelligence (AI) in recent years, there has been a growing body of AI-related research in the field of Network Intrusion Detection Systems (NIDSs). In this paper, a network intrusion detection method based on Convolutional Neural Network (CNN) was tested using the KDD Cup 99 dataset. Along with Sigmoid-weighted Linear Unit (SiLU) as the activation function and Stochastic Gradient Descent (SGD) as the optimizer, Batch Normalization (BN) was applied to mitigate internal covariate shift. The evaluation was carried out using performance metrics for the attack classes, which yielded good results for intrusion detection and demonstrated an impressive average accuracy rate of 99.26% after ten epochs, showcasing its promising results in intrusion detection.",
author = "Peng, {Tzu En} and Liu, {I. Hsien} and Li, {Jung Shian} and Liu, {Chuan Kang}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023 ; Conference date: 14-12-2023 Through 16-12-2023",
year = "2023",
doi = "10.1109/BCD57833.2023.10466285",
language = "English",
series = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "361--364",
editor = "Jongwoo Park and Lan, {Ngo Thi Phuong} and Sungtaek Lee and Tien, {Tran Anh} and Jongbae Kim",
booktitle = "2023 IEEE/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023",
address = "United States",
}