2 Search Results for "Sun, Timothy"

Document
DOI: 10.4230/LIPIcs.CP.2022.17
DUELMIPs: Optimizing SDN Functionality and Security

Authors: Timothy Curry, Gabriel De Pace, Benjamin Fuller, Laurent Michel, and Yan (Lindsay) Sun

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Software defined networks (SDNs) define a programmable network fabric that can be reconfigured to respect global networks properties. Securing against adversaries who try to exploit the network is an objective that conflicts with providing functionality. This paper proposes a two-stage mixed-integer programming framework. The first stage automates routing decisions for the flows to be carried by the network while maximizing readability and ease of use for network engineers. The second stage is meant to quickly respond to security breaches to automatically decide on network counter-measures to block the detected adversary. Both stages are computationally challenging and the security stage leverages large neighborhood search to quickly deliver effective response strategies. The approach is evaluated on synthetic networks of various sizes and shown to be effective for both its functional and security objectives.
Cite as

Timothy Curry, Gabriel De Pace, Benjamin Fuller, Laurent Michel, and Yan (Lindsay) Sun. DUELMIPs: Optimizing SDN Functionality and Security. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{curry_et_al:LIPIcs.CP.2022.17,
  author =	{Curry, Timothy and De Pace, Gabriel and Fuller, Benjamin and Michel, Laurent and Sun, Yan (Lindsay)},
  title =	{{DUELMIPs: Optimizing SDN Functionality and Security}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{17:1--17:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.17},
  URN =		{urn:nbn:de:0030-drops-166468},
  doi =		{10.4230/LIPIcs.CP.2022.17},
  annote =	{Keywords: Network security, mixed integer programming, large neighborhood search}
}
Document
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2017.37
Sample-Based High-Dimensional Convexity Testing

Authors: Xi Chen, Adam Freilich, Rocco A. Servedio, and Timothy Sun

Published in: LIPIcs, Volume 81, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017)

Abstract
In the problem of high-dimensional convexity testing, there is an unknown set S in the n-dimensional Euclidean space which is promised to be either convex or c-far from every convex body with respect to the standard multivariate normal distribution. The job of a testing algorithm is then to distinguish between these two cases while making as few inspections of the set S as possible. In this work we consider sample-based testing algorithms, in which the testing algorithm only has access to labeled samples (x,S(x)) where each x is independently drawn from the normal distribution. We give nearly matching sample complexity upper and lower bounds for both one-sided and two-sided convexity testing algorithms in this framework. For constant c, our results show that the sample complexity of one-sided convexity testing is exponential in n, while for two-sided convexity testing it is exponential in the square root of n.
Cite as

Xi Chen, Adam Freilich, Rocco A. Servedio, and Timothy Sun. Sample-Based High-Dimensional Convexity Testing. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 81, pp. 37:1-37:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{chen_et_al:LIPIcs.APPROX-RANDOM.2017.37,
  author =	{Chen, Xi and Freilich, Adam and Servedio, Rocco A. and Sun, Timothy},
  title =	{{Sample-Based High-Dimensional Convexity Testing}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2017)},
  pages =	{37:1--37:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-044-6},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{81},
  editor =	{Jansen, Klaus and Rolim, Jos\'{e} D. P. and Williamson, David P. and Vempala, Santosh S.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2017.37},
  URN =		{urn:nbn:de:0030-drops-75867},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2017.37},
  annote =	{Keywords: Property testing, convexity, sample-based testing}
}
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