CLASSIFICATION TREE AND KULLBACK-LEIBLER DISTANCE-BASED ANOMALY INTRUSION DETECTION APPROACH
dc.contributor.author | Николова, Евгения | |
dc.contributor.author | Жечева, Веселина | |
dc.date.accessioned | 2025-02-22T15:20:30Z | |
dc.date.issued | 2014 | |
dc.description.abstract | In recent years anomaly detection has become an important area for both commercial interests as well as academic research. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. The present paper proposed an adaptive approach of anomaly based intrusion detection which is grounded on classification trees and relative entropy. The major results of the implemented simulation experiments are presented and discussed as well. | |
dc.identifier.issn | 1314-7846 | |
dc.identifier.uri | http://research.bfu.bg:4000/handle/123456789/98 | |
dc.language.iso | en | |
dc.publisher | Бургаски свободен университет | |
dc.relation.ispartofseries | Т. 3 Бр. 1 | |
dc.subject | Intrusion Detection | |
dc.subject | Anomaly Based IDS | |
dc.subject | Classification Trees | |
dc.subject | Relative entropy | |
dc.title | CLASSIFICATION TREE AND KULLBACK-LEIBLER DISTANCE-BASED ANOMALY INTRUSION DETECTION APPROACH | |
dc.type | Article |
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