Monitoring Internet traffic in order to both dynamically tune network resources and ensure services continuity is a big challenge. Two main research issues characterize the analysis of the huge amount of data generated by Internet traffic: 1) learning a normal adaptive model which must be able to detect anomalies, and 2) computational efficiency of the learning algorithm in order to work properly on-line. In this chapter, we propose a methodology which returns a set of symbolic objects representing an adaptive model of ‘normal’ daily network traffic. The model can then be used to discover traffic anomalies of interest for the network administrator.
Symbolic Analysis to Learn Evolving CyberTraffic
CARUSO, COSTANTINA;MALERBA, Donato
2007-01-01
Abstract
Monitoring Internet traffic in order to both dynamically tune network resources and ensure services continuity is a big challenge. Two main research issues characterize the analysis of the huge amount of data generated by Internet traffic: 1) learning a normal adaptive model which must be able to detect anomalies, and 2) computational efficiency of the learning algorithm in order to work properly on-line. In this chapter, we propose a methodology which returns a set of symbolic objects representing an adaptive model of ‘normal’ daily network traffic. The model can then be used to discover traffic anomalies of interest for the network administrator.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
