Research output: Contribution to conference - Without ISBN/ISSN › Other › peer-review
Research output: Contribution to conference - Without ISBN/ISSN › Other › peer-review
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TY - CONF
T1 - Autonomic diagnosis of anomalous network traffic.
AU - Marnerides, Angelos
AU - Pezaros, Dimitrios P.
AU - Hutchison, David
N1 - "©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
PY - 2010/6/14
Y1 - 2010/6/14
N2 - Network traffic abnormalities pose one of the greatest threats for networked environments. Autonomic communications offer a solution: it should be possible to design network mechanisms that behave adaptively and respond to any anomalous phenomenon that threatens normal network behaviour. In this paper we present the design of an adaptive anomaly detection component that has been built as part of an autonomic network system. We have implemented an entropy estimator to predict the onset of anomalous traffic behaviour within an autonomic resilience framework, and a Supervised Naïve Bayesian classifier which synergistically empower the core properties of self-adaptation, self-learning and self-protection for next generation networks. Being part of an always-on, automated measurement and control infrastructure, such mechanism enforces the adaptive system reaction to suboptimal network operation and its subsequent restoration, while requiring minimal static (re)configuration and operator intervention.
AB - Network traffic abnormalities pose one of the greatest threats for networked environments. Autonomic communications offer a solution: it should be possible to design network mechanisms that behave adaptively and respond to any anomalous phenomenon that threatens normal network behaviour. In this paper we present the design of an adaptive anomaly detection component that has been built as part of an autonomic network system. We have implemented an entropy estimator to predict the onset of anomalous traffic behaviour within an autonomic resilience framework, and a Supervised Naïve Bayesian classifier which synergistically empower the core properties of self-adaptation, self-learning and self-protection for next generation networks. Being part of an always-on, automated measurement and control infrastructure, such mechanism enforces the adaptive system reaction to suboptimal network operation and its subsequent restoration, while requiring minimal static (re)configuration and operator intervention.
KW - networkresilience
KW - anaproject
U2 - 10.1109/WOWMOM.2010.5534933
DO - 10.1109/WOWMOM.2010.5534933
M3 - Other
T2 - 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010
Y2 - 14 June 2010 through 17 June 2010
ER -