MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Investigating coevolutionary algorithms For expensive fitness evaluations in cybersecurity

Author(s)
Pertierra Arrojo, Marcos (Marcos A.)
Thumbnail
DownloadFull printable version (1.801Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Una-May O'Reilly and Erik Hemberg.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
Coevolutionary algorithms require evaluating fitness of solutions against adversaries, and vice versa, in order to select high quality individuals to generate offspring and evolve the population. However, some problems require computationally expensive fitness evaluations, which makes it hard to generate solutions in a feasible amount of time. In this thesis, we devise coevolutionary algorithms and methods that achieve good results with fewer fitness evaluations, and we present methods for selecting a solution to deploy after running experiments with multiple coevolutionary algorithms. Comparing our new algorithms presented with baselines, we found that MEULockstepCoev performs relatively well, especially for attackers.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
 
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 75-76).
 
Date issued
2018
URI
http://hdl.handle.net/1721.1/120388
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.