@inproceedings{5f2891197eb14befa52804ec0b52289e,
title = "A Protocol-based Intrusion Detection System using Dual Autoencoders",
abstract = "This paper proposes a dual Autoencoder-based Intrusion Detection System (duAE-IDS) for the ever-changing network attacks. duAE-IDS is a protocol-based IDS, which divides traffic by its application-layer protocol. duAE-IDS determines the traffic's abnormality by considering both the criteria and the application-layer protocol. The criteria are obtained by training our neural network model (duAE model) with traffic containing only one type of application-layer protocol. duAE-IDS represents each traffic flow with 67 features with eight new features for TCP traffic to improve detection accuracy. duAE-Idsuses two sparse autoencoders and one 1D CNN to extract features from traffic for every application-layer protocol. We conduct several experiments to prove the abilities and flexibilities of duAE-IDS. We prove that duAE-Idstrained with the known datasets can reach an F1-score of 0.87 for detecting attack traffic in an unknown network. We can run duAE-Idsin any network without pre-collecting the traffic of the network.",
keywords = "Autoen-coder, Feature Extraction, Intrusion Detection, Sparse Autoencoder",
author = "Huang, {Yu Lun} and Hung, {Ching Yu} and Hu, {Hsiao Te}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st International Conference on Software Quality, Reliability and Security, QRS 2021 ; Conference date: 06-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/QRS54544.2021.00084",
language = "English",
series = "IEEE International Conference on Software Quality, Reliability and Security, QRS",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "749--758",
booktitle = "Proceedings - 2021 21st International Conference on Software Quality, Reliability and Security, QRS 2021",
}