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Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia

Instituto de Matemática

 
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Evento: 'Palestra de Fernando Silveira no PESC: palestra: ASTUTE: Detecting a Different Class of Traffic Anomalies '

Eventos PESC (Palestras, Seminários, etc.)
Palestras, Seminários, etc. do PESC/COPPE/UFRJ.
Data: Tuesday, May 08, 2012 At 11:00
Duração: 2 Horas

Um ex-aluno de mestrado do PESC, Fernando Silveira, veio para o SBRC e estará ministrando uma palestra no PESC.

Fernando obteve o doutorado na França, e agora trabalha no Laboratório da Technicolor em Palo Alto, California.

Dia: 8 de Maio
hora: 11:00
local: H-324B
URL do Fernando: http://paloalto.thlab.net/people/fernando-silveira

Título da palestra: ASTUTE: Detecting a Different Class of Traffic Anomalies
Abstract:
Network operators often need to deal with events that compromise their networks.
One approach to find these events is to monitor the aggregate traffic in one or several network links and then look for significant deviations from some statistical model of normal behavior.
This is known as the anomaly detection problem and the success of any anomaly detection technique depends on the specific model which is assumed to represent normal traffic.
In this work we present a simple, yet surprisingly effective model of normal traffic behavior based on an "equilibrium" assumption.
Specifically, when many flows are multiplexed on a non-saturated link, their volume changes over short timescales tend to cancel each other out, making the average change across flows close to zero.
This equilibrium property holds if the flows are nearly independent, and it is violated by traffic changes caused by several, potentially small, correlated flows.
Many important traffic anomalies (both malicious and benign) fit this description.
Based on this observation, we exploit equilibrium to design a computationally simple method to detect correlated anomalous flows. We compare our method to two well known techniques on three network links. We manually classify the anomalies detected by the three methods, and discover that our method uncovers a different class of anomalies than previous techniques do.

This is joint work with Christophe Diot, Nina Taft, and Ramesh Govindan.


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