Factor Analysis of Information Risk (FAIR) is a standard model for quantitatively estimating cybersecurity risks and has been implemented as a sequential Monte Carlo simulation in the RiskLens and FAIR-U applications. Monte Carlo simulations employ random sampling techniques to model certain systems through the course of many iterations. Due to their sequential nature, FAIR simulations in these applications are limited in the number of iterations they can perform in a reasonable amount of time. One method that has been extensively used to speed up Monte Carlo simulations is to implement them to take advantage of the massive parallelization available when using modern Graphics Processing Units (GPUs). Such parallelized simulations have been shown to produce significant speedups, in some cases up to 3,000 times faster then the sequential versions. Due to the FAIR simulation's need for many samples from various beta distributions, three methods of generating these samples via inverse transform sampling on the GPU are investigated. One method calculates the inverse incomplete beta function directly, and the other two methods approximate this function - trading accuracy for improved parallelism. This method is then utilized in a GPU accelerated implementation of the FAIR simulation from RiskLens and FAIR-U using NVIDIA's CUDA technology.
","abstract_html":"<p>Factor Analysis of Information Risk (FAIR) is a standard model for quantitatively estimating cybersecurity risks and has been implemented as a sequential Monte Carlo simulation in the RiskLens and FAIR-U applications. Monte Carlo simulations employ random sampling techniques to model certain systems through the course of many iterations. Due to their sequential nature, FAIR simulations in these applications are limited in the number of iterations they can perform in a reasonable amount of time. One method that has been extensively used to speed up Monte Carlo simulations is to implement them to take advantage of the massive parallelization available when using modern Graphics Processing Units (GPUs). Such parallelized simulations have been shown to produce significant speedups, in some cases up to 3,000 times faster then the sequential versions. Due to the FAIR simulation's need for many samples from various beta distributions, three methods of generating these samples via inverse transform sampling on the GPU are investigated. One method calculates the inverse incomplete beta function directly, and the other two methods approximate this function - trading accuracy for improved parallelism. This method is then utilized in a GPU accelerated implementation of the FAIR simulation from RiskLens and FAIR-U using NVIDIA's CUDA technology.</p>","abstract_has_math":false,"creators":["Ireland, Forrest L."],"institution":null,"degree_name":"Master of Science (MS) in Computer Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01T08:00:00Z","date_published":"2018-01-01T08:00:00Z","updated_at":"2026-07-24T02:13:16Z","subjects":["Information Security","Theory and Algorithms"],"languages":[],"rights":["Access is available to all users"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.ewu.edu/theses/497","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ireland, Forrest L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS) in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information Security","Theory and Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Access is available to all users"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.ewu.edu/theses/497"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Factor Analysis of Information Risk (FAIR) is a standard model for quantitatively estimating cybersecurity risks and has been implemented as a sequential Monte Carlo simulation in the RiskLens and FAIR-U applications. Monte Carlo simulations employ random sampling techniques to model certain systems through the course of many iterations. Due to their sequential nature, FAIR simulations in these applications are limited in the number of iterations they can perform in a reasonable amount of time. One method that has been extensively used to speed up Monte Carlo simulations is to implement them to take advantage of the massive parallelization available when using modern Graphics Processing Units (GPUs). Such parallelized simulations have been shown to produce significant speedups, in some cases up to 3,000 times faster then the sequential versions. Due to the FAIR simulation's need for many samples from various beta distributions, three methods of generating these samples via inverse transform sampling on the GPU are investigated. One method calculates the inverse incomplete beta function directly, and the other two methods approximate this function - trading accuracy for improved parallelism. This method is then utilized in a GPU accelerated implementation of the FAIR simulation from RiskLens and FAIR-U using NVIDIA's CUDA technology.
"]},{"key":"dc:title","label":"Title","values":["GPU accelerated risk quantification"]}]}],"canonical_facts":{"dc:creator":["Ireland, Forrest L."],"dc:description.abstract":["Factor Analysis of Information Risk (FAIR) is a standard model for quantitatively estimating cybersecurity risks and has been implemented as a sequential Monte Carlo simulation in the RiskLens and FAIR-U applications. Monte Carlo simulations employ random sampling techniques to model certain systems through the course of many iterations. Due to their sequential nature, FAIR simulations in these applications are limited in the number of iterations they can perform in a reasonable amount of time. One method that has been extensively used to speed up Monte Carlo simulations is to implement them to take advantage of the massive parallelization available when using modern Graphics Processing Units (GPUs). Such parallelized simulations have been shown to produce significant speedups, in some cases up to 3,000 times faster then the sequential versions. Due to the FAIR simulation's need for many samples from various beta distributions, three methods of generating these samples via inverse transform sampling on the GPU are investigated. One method calculates the inverse incomplete beta function directly, and the other two methods approximate this function - trading accuracy for improved parallelism. This method is then utilized in a GPU accelerated implementation of the FAIR simulation from RiskLens and FAIR-U using NVIDIA's CUDA technology.
"],"dc:identifier":["https://dc.ewu.edu/theses/497"],"dc:rights":["Access is available to all users"],"dc:subject":["Information Security","Theory and Algorithms"],"dc:title":["GPU accelerated risk quantification"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS) in Computer Science"]},"updated_at":"2026-07-24T02:13:16Z"}