Title :
Network Intrusion Detection Using Rough Sets Based Parallel Genetic Algorithm Hybrid Model
Author :
Zhou, Fen ; Yang, Gaizhen
Author_Institution :
Dept. of Comput. Sci., Huanggang Normal Univ., Huangang, China
Abstract :
The thesis proposes a hybrid intrusion detection model based on the parallel genetic algorithm and the rough set theory. Due to the difficult for the status of intrusion detection rules. This model, taking the advantage of rough set´s streamline the edge to data and genetic algorithm´s high parallelism, succeeds in introducing the genetic-rough set theory to the instrusion detection. The application of hubrid genetic algorithm in solving the rough set reduction saves computing time. The concludes that model can result in high detection rate and low false detection rate to different types of network via experiments.
Keywords :
computer network security; genetic algorithms; parallel algorithms; rough set theory; network intrusion detection; parallel genetic algorithm hybrid model; rough set theory; Classification algorithms; Computational modeling; Data mining; Data models; Feature extraction; Heuristic algorithms; Intrusion detection; Coarse-grained model; IDs; Rough Set theory; Rule extraction; parallel genetic algorithm;
Conference_Titel :
Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on
Conference_Location :
Huanggang
Print_ISBN :
978-1-4244-8148-4
Electronic_ISBN :
978-0-7695-4196-9
DOI :
10.1109/IPTC.2010.165
