RT Conference Proceedings
T1 Evolving high-speed, easy-to-understand network intrusion detection rules with genetic programming
A1 Orfila Díaz-Pabon, Agustín
A1 Estévez Tapiador, Juan Manuel
A1 Ribagorda Garnacho, Arturo
AB An ever-present problem in intrusion detection technology is how to construct the patterns of (good, bad or anomalous) behaviour upon which an engine have to make decisions regarding the nature of the activity observed in a system. This has traditionally been one of the central areas of research in the field, and most of the solutions proposed so far have relied in one way or another upon some form of data mining–with the exception, of course, of human-constructed patterns. In this paper, we explore the use of Genetic Programming (GP) for such a purpose. Our approach is not new in some aspects, as GP has already been partially explored in the past. Here we show that GP can offer at least two advantages over other classical mechanisms: it can produce very lightweight detection rules (something of extreme importance for high-speed networks or resource-constrained applications) and the simplicity of the patterns generated allows to easily understand the semantics of the underlying attack.
PB Springer
SN 3-642-01128-4
SN 978-3-642-01128-3
SN 0302-9743
YR 2009
FD 2009-04
LK https://hdl.handle.net/10016/9552
UL https://hdl.handle.net/10016/9552
LA eng
NO Proceeding of: EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany, April 15-17, 2009
DS e-Archivo
RD 30 jun. 2024