2026-09-19T08:26:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1229922026-06-06T00:54:34Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Una-May O'Reilly and Erik Hemberg.
Zhang, Linda(Linda E.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-22T00:00:36Z
2019-11-22T00:00:36Z
2019
2019
https://hdl.handle.net/1721.1/122992
1127291730
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 55-57).
Distributed Denial of Service (DDoS) cyberattacks continue to increase and cause disruptions in both industry and politics. As more critical information and services are provided through networks, it becomes more important to keep these networks available. However, since cyber-adversaries continuously change and adapt, stationary defense strategies do not effectively secure networks against attacks. We modeled attacker-defender interactions using competitive coevolutionary algorithms and investigated Nash equilibria within these cybersecurity problems. In particular, we examined and presented variations on two existing algorithms that look for Nash equilibria: NashSolve and HybridCoev. To compare these algorithms' performances against other existing heuristics, we considered multiple evaluation methods: the first calculates average fitness scores, the second creates a compendium of MEU, MinMax, and inverse Pareto front ratio scores, and the third utilizes Nash averaging. Although NashSolve and HybridCoev do not perform significantly better on average for either attacker or defender populations relative to other heuristics in these evaluations, they are able to produce strong individual strategies.
by Linda Zhang.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
57 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Investigating coevolutionary algorithms for finding Nash equilibria in cybersecurity problems
Thesis
application/pdf
2019-11-22T00:00:35Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Distributed Denial of Service (DDoS) cyberattacks continue to increase and cause disruptions in both industry and politics. As more critical information and services are provided through networks, it becomes more important to keep these networks available. However, since cyber-adversaries continuously change and adapt, stationary defense strategies do not effectively secure networks against attacks. We modeled attacker-defender interactions using competitive coevolutionary algorithms and investigated Nash equilibria within these cybersecurity problems. In particular, we examined and presented variations on two existing algorithms that look for Nash equilibria: NashSolve and HybridCoev. To compare these algorithms' performances against other existing heuristics, we considered multiple evaluation methods: the first calculates average fitness scores, the second creates a compendium of MEU, MinMax, and inverse Pareto front ratio scores, and the third utilizes Nash averaging. Although NashSolve and HybridCoev do not perform significantly better on average for either attacker or defender populations relative to other heuristics in these evaluations, they are able to produce strong individual strategies.</Abstract>
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