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Una-May O'Reilly and Erik Hemberg. Prado Sánchez, Daniel Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-12-18T19:47:16Z 2018-12-18T19:47:16Z 2018 2018 http://hdl.handle.net/1721.1/119722 1078639023 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 49-50). Using coevolutionary algorithms to find solutions to problems is a powerful search technique but once solutions are identified it can be difficult for a decision maker to select a solution to deploy. ESTABLO runs multiple competitive coevolutionary algorithm variants independently, in parallel, and then combines their test and solution results at the final generation into a compendium. From there, it re-evaluates each solution, according to three different measurements, on every test as well as on a set of unseen tests. For a decision maker, it finally identifies top solutions using various metrics and visualizes them in the context of other solutions. However, it can be difficult to decide on which coevolutionary algorithms to run individually or use in ESTABLO. A coevolutionary variant, POOLING, was then created using this same principle of combining multiple variants. POOLING runs competitive coevolutionary algorithm variants, combines their solutions after every generation, and seeds the next generation with the top solutions found. ESTABLO (with POOLING as one of its variants) is demonstrated on multiple cyber security related problems. We found that using ESTABLO was beneficial to most problems as different variants dominated in different scenarios. We also found that POOLING was able to consistently produce individuals that performed well against adversaries and in the context of all of their peers. by Daniel Prado Sánchez. M.Eng. 50 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. Visualizing adversaries : transparent pooling approaches for decision support in cybersecurity Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="c72ee1b7-444b-4731-8552-99d619115ea6"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Visualizing adversaries : transparent pooling approaches for decision support in cybersecurity</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Prado Sánchez, Daniel</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Using coevolutionary algorithms to find solutions to problems is a powerful search technique but once solutions are identified it can be difficult for a decision maker to select a solution to deploy. ESTABLO runs multiple competitive coevolutionary algorithm variants independently, in parallel, and then combines their test and solution results at the final generation into a compendium. From there, it re-evaluates each solution, according to three different measurements, on every test as well as on a set of unseen tests. For a decision maker, it finally identifies top solutions using various metrics and visualizes them in the context of other solutions. However, it can be difficult to decide on which coevolutionary algorithms to run individually or use in ESTABLO. A coevolutionary variant, POOLING, was then created using this same principle of combining multiple variants. POOLING runs competitive coevolutionary algorithm variants, combines their solutions after every generation, and seeds the next generation with the top solutions found. ESTABLO (with POOLING as one of its variants) is demonstrated on multiple cyber security related problems. We found that using ESTABLO was beneficial to most problems as different variants dominated in different scenarios. We also found that POOLING was able to consistently produce individuals that performed well against adversaries and in the context of all of their peers.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>