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Type: Artigo de evento
Title: Dependency Graph To Improve Notifications' Semantic On Anomaly Detection
Author: Zarpelao B.B.
De Mendes L.S.
Proenca Jr. M.L.
Abstract: Besides identifying anomalies, detection systems must offer additional information about the occurrence, aiming to help the network administrator in order to build an accurate diagnostic. This paper presents a lightweight approach to detect anomalies, improving the semantic power of notifications sent to network administrator. The key point of the proposed anomaly detection system is a correlation system based on a directed graph which represents the possible paths of anomaly propagation through the SNMP objects in a network element. The results obtained from initial tests were encouraging and showed that our system is able to detect anomalies on the monitored network element, avoiding the high false alarms rate. ©2008 IEEE.
Rights: fechado
Identifier DOI: 10.1109/NOMS.2008.4575199
Date Issue: 2008
Appears in Collections:Unicamp - Artigos e Outros Documentos

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