Title:
Graph-based algorithms and models for security, healthcare, and finance
Graph-based algorithms and models for security, healthcare, and finance
| dc.contributor.advisor | Chau, Duen Horng | |
| dc.contributor.author | Tamersoy, Acar | |
| dc.contributor.committeeMember | Navathe, Shamkant B. | |
| dc.contributor.committeeMember | De Choudhury, Munmun | |
| dc.contributor.committeeMember | Basole, Rahul C. | |
| dc.contributor.committeeMember | Roundy, Kevin A. | |
| dc.contributor.department | Computational Science and Engineering | |
| dc.date.accessioned | 2016-05-27T13:23:05Z | |
| dc.date.available | 2016-05-27T13:23:05Z | |
| dc.date.created | 2016-05 | |
| dc.date.issued | 2016-04-15 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2016-05-27T13:23:05Z | |
| dc.description.abstract | Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking in people's computers with 99.61% true positive rate at 0.01% false positive rate; our application of ADAGE on malware detection (patent-pending) enables to detect malware in a streaming setting; and EDOCS (patent-pending) flags comment spammers among 197 thousand users on a social media platform accurately and preemptively. (2) Graph-induced Behavior Characterization: We derive new insights and knowledge that characterize certain behavior from graphs using statistical and algorithmic techniques. We present two examples: a study on identifying attributes of smoking and drinking abstinence and relapse from an addiction cessation social media community; and an exploratory analysis of how company insiders trade. Our work has already made impact to society: deployed by Symantec, AESOP is protecting over 120 million people worldwide from malware; EDOCS has been deployed by Yahoo and it guards multiple online communities from comment spammers. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/54986 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Graph mining | |
| dc.subject | Information propagation | |
| dc.subject | Behavior characterization | |
| dc.subject | Malware detection | |
| dc.subject | Comment spammer detection | |
| dc.subject | Smoking abstinence and relapse | |
| dc.subject | Alcohol abstinence and relapse | |
| dc.subject | Insider trading | |
| dc.title | Graph-based algorithms and models for security, healthcare, and finance | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Chau, Duen Horng | |
| local.contributor.corporatename | School of Computational Science and Engineering | |
| local.contributor.corporatename | College of Computing | |
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| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| thesis.degree.level | Doctoral |