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Please use this identifier to cite or link to this item: http://research.bfu.bg:8080/jspui/handle/123456789/492

Заглавие: Classification tree and Kullback - Leibler distance - based anomaly intrusion detection approach
Автори: Nikolova, Evgeniya
Jecheva, Veselina
Ключови Думи: Intrusion Detection
Anomaly Based IDS
Classification Trees
Relative entropy
Issue Date: 2013
Издател: Burgas Free University, 62, San Stefano Str., 8001 Burgas, Bulgaria
Цитиране: International Research Conference “Knowledge - traditions, innovations, perspectives”, Burgas 14-15 June 2013
Серия/Отчет но.: BFU_MK_2013_OTT;str-160
Резюме: 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.
Описание: International research conference 2013
URI: http://research.bfu.bg:8080/jspui/handle/123456789/492
ISBN: 978-954-9370-99-7
Appears in Collections:„Знанието – източник на иновации”

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