RT Conference Proceedings
T1 Effectiveness evaluation of data mining based IDS
A1 Orfila, Agustín
A1 Carbó Rubiera, Javier Ignacio
A1 Ribagorda Garnacho, Arturo
AB Data mining has been widely applied to the problem of Intrusion Detection in computer networks. However, the misconception of the underlying problem has led to out of context results. This paper shows that factors such as the probability of intrusion and the costs of responding to detected intrusions must be taken into account in order to compare the effectiveness of machine learning algorithms over the intrusion detection domain. Furthermore, we show the advantages of combining different detection techniques. Results regarding the well known 1999 KDD dataset are shown.
PB Springer
SN 3-540-36036-0
SN 978-3-540-36036-0
SN 0302-9743
YR 2006
FD 2006-07
LK https://hdl.handle.net/10016/9575
UL https://hdl.handle.net/10016/9575
LA eng
NO Proceeding of: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006.
DS e-Archivo
RD 30 jun. 2024