@inproceedings{d03f2a6d3e644bf49693b623f46129d8,
title = "Siamese network based feature learning for improved intrusion detection",
abstract = "Intrusion detection is a critical Cyber Security subject. Different Machine Learning (ML) approaches have been proposed for Intrusion Detection Systems (IDS). However, their application to real-life scenarios remains challenging due to high data dimensionality. Representation learning (RL) allows discriminative feature representation in a low dimensionality space. The application of this technique in IDS requires more investigation. This paper examines and discusses the contribution of Siamese network based representation learning in improving the IDS performance. Extensive experimental results under different evaluation scenarios show different improvement rates depending on the scenario.",
keywords = "Anomaly detection, Feature extraction, IDS, Representation learning, Siamese, UNSW15 data-set",
author = "Houda Jmila and \{Ibn Khedher\}, Mohamed and Gregory Blanc and \{El Yacoubi\}, \{Mounim A.\}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-36708-4\_31",
language = "English",
isbn = "9783030367077",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "377--389",
editor = "Tom Gedeon and Wong, \{Kok Wai\} and Minho Lee",
booktitle = "Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings",
}