@inproceedings{e2045e91bcc44ebeaa46e4075208db37,
title = "High resolution SOM approach to improving anomaly detection in intrusion detection systems",
abstract = "Machine learning in general and artificial neural networks in particular are commonly used to address the problem of detecting anomalies in intrusion detection systems. Self-Organizing Maps (SOMs) have been shown to be a promising tool for this purpose, but the limitation of the cardinality of their display space has resulted in SOMs being a black box method and impeded the design of a simpler network architecture. High resolution SOMs are a very recent development that can overcome these problems. This paper explores how high resolution SOMs can help with anomaly detection in intrusion detection systems. Experiments on a large and well established benchmark problem show that high resolution SOMs improve results while allowing a simple network architecture. It is also shown that high resolution SOMs allow the development of better understanding of the results and the problem domain.",
keywords = "Anomaly detection, High resolution neural network, Intrusion detection, Self organising map",
author = "Ayu Saraswati and Markus Hagenbuchner and Zhou, \{Zhi Quan\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 29th Australasian Joint Conference on Artificial Intelligence, AI 2016 ; Conference date: 05-12-2016 Through 08-12-2016",
year = "2016",
doi = "10.1007/978-3-319-50127-7\_16",
language = "English",
isbn = "9783319501260",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "191--199",
editor = "Kang, \{Byeong Ho\} and Quan Bai",
booktitle = "AI 2016",
address = "Germany",
}