Intrusion detection techniques can be divided into two groups according to the type of information they use: misuse detection and anomaly detection. Anomaly detection models normal behaviors and attempts to detect intrusions by noting significant deviations from normal behavior. By constructing models using multiple measures and combining them, we can expect an enhanced reliability in intrusion detection. In this paper, we propose a technique that combine multiple models using voting technique to improve the detection rate of intrusion detection system.
|Title of host publication||PRICAI 2000, Topics in Artificial Intelligence - 6th Pacific Rim International Conference on Artificial Intelligence, Proceedings|
|Editors||Riichiro Mizoguchi, John Slaney|
|ISBN (Print)||3540679251, 9783540679257, 9783540679257|
|Publication status||Published - 2000|
|Event||6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC, Australia|
Duration: 2000 Aug 28 → 2000 Sept 1
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000|
|Period||00/8/28 → 00/9/1|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)