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Autonomic diagnosis of anomalous network traffic.

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Autonomic diagnosis of anomalous network traffic. / Marnerides, Angelos; Pezaros, Dimitrios P.; Hutchison, David.
2010. 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010, Montreal, Canada.

Research output: Contribution to conference - Without ISBN/ISSN Otherpeer-review

Harvard

Marnerides, A, Pezaros, DP & Hutchison, D 2010, 'Autonomic diagnosis of anomalous network traffic.', 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010, Montreal, Canada, 14/06/10 - 17/06/10. https://doi.org/10.1109/WOWMOM.2010.5534933

APA

Marnerides, A., Pezaros, D. P., & Hutchison, D. (2010). Autonomic diagnosis of anomalous network traffic.. 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010, Montreal, Canada. https://doi.org/10.1109/WOWMOM.2010.5534933

Vancouver

Marnerides A, Pezaros DP, Hutchison D. Autonomic diagnosis of anomalous network traffic.. 2010. 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010, Montreal, Canada. doi: 10.1109/WOWMOM.2010.5534933

Author

Marnerides, Angelos ; Pezaros, Dimitrios P. ; Hutchison, David. / Autonomic diagnosis of anomalous network traffic. 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010, Montreal, Canada.

Bibtex

@conference{1dbe67f9dcd947e39079afd236629cf5,
title = "Autonomic diagnosis of anomalous network traffic.",
abstract = "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 Na{\"i}ve 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.",
keywords = "networkresilience, anaproject",
author = "Angelos Marnerides and Pezaros, {Dimitrios P.} and David Hutchison",
note = "{"}{\textcopyright}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.{"}; 11th IEEE WoWMoM Conference Autonomic and Opportunistic Communications Workshop 2010 ; Conference date: 14-06-2010 Through 17-06-2010",
year = "2010",
month = jun,
day = "14",
doi = "10.1109/WOWMOM.2010.5534933",
language = "English",

}

RIS

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 -