An Investigation of Flow-based Algorithms for Sybil Defense

abstract: Distributed systems are prone to attacks, called Sybil attacks, wherein an adversary may generate an unbounded number of bogus identities to gain control over the system. In this thesis, an algorithm, DownhillFlow, for mitigating such attacks is presented and tested experimentally. The tr...

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Other Authors: Bradley, Michael (Author)
Format: Dissertation
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.50111
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spelling ndltd-asu.edu-item-501112018-08-02T03:01:05Z An Investigation of Flow-based Algorithms for Sybil Defense abstract: Distributed systems are prone to attacks, called Sybil attacks, wherein an adversary may generate an unbounded number of bogus identities to gain control over the system. In this thesis, an algorithm, DownhillFlow, for mitigating such attacks is presented and tested experimentally. The trust rankings produced by the algorithm are significantly better than those of the distributed SybilGuard protocol and only slightly worse than those of the best-known Sybil defense algorithm, ACL. The results obtained for ACL are consistent with those obtained in previous studies. The running times of the algorithms are also tested and two results are obtained: first, DownhillFlow’s running time is found to be significantly faster than any existing algorithm including ACL, terminating in slightly over one second on the 300,000-node DBLP graph. This allows it to be used in settings such as dynamic networks as-is with no additional functionality needed. Second, when ACL is configured such that it matches DownhillFlow’s speed, it fails to recognize large portions of the input graphs and its accuracy among the portion of the graphs it does recognize becomes lower than that of DownhillFlow. Dissertation/Thesis Bradley, Michael (Author) Bazzi, Rida (Advisor) Richa, Andrea (Committee member) Sen, Arunabha (Committee member) Arizona State University (Publisher) Computer science eng 81 pages Masters Thesis Computer Science 2018 Masters Thesis http://hdl.handle.net/2286/R.I.50111 http://rightsstatements.org/vocab/InC/1.0/ 2018
collection NDLTD
language English
format Dissertation
sources NDLTD
topic Computer science
spellingShingle Computer science
An Investigation of Flow-based Algorithms for Sybil Defense
description abstract: Distributed systems are prone to attacks, called Sybil attacks, wherein an adversary may generate an unbounded number of bogus identities to gain control over the system. In this thesis, an algorithm, DownhillFlow, for mitigating such attacks is presented and tested experimentally. The trust rankings produced by the algorithm are significantly better than those of the distributed SybilGuard protocol and only slightly worse than those of the best-known Sybil defense algorithm, ACL. The results obtained for ACL are consistent with those obtained in previous studies. The running times of the algorithms are also tested and two results are obtained: first, DownhillFlow’s running time is found to be significantly faster than any existing algorithm including ACL, terminating in slightly over one second on the 300,000-node DBLP graph. This allows it to be used in settings such as dynamic networks as-is with no additional functionality needed. Second, when ACL is configured such that it matches DownhillFlow’s speed, it fails to recognize large portions of the input graphs and its accuracy among the portion of the graphs it does recognize becomes lower than that of DownhillFlow. === Dissertation/Thesis === Masters Thesis Computer Science 2018
author2 Bradley, Michael (Author)
author_facet Bradley, Michael (Author)
title An Investigation of Flow-based Algorithms for Sybil Defense
title_short An Investigation of Flow-based Algorithms for Sybil Defense
title_full An Investigation of Flow-based Algorithms for Sybil Defense
title_fullStr An Investigation of Flow-based Algorithms for Sybil Defense
title_full_unstemmed An Investigation of Flow-based Algorithms for Sybil Defense
title_sort investigation of flow-based algorithms for sybil defense
publishDate 2018
url http://hdl.handle.net/2286/R.I.50111
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