Show simple item record

dc.contributor.authorFields, Jeremy
dc.contributor.authorSengupta, Saumendra; Adviser
dc.contributor.authorWhite, Joshura; Reviewer
dc.contributor.authorSpetka, Scott; Reviewer
dc.date.accessioned2016-10-07T18:28:48Z
dc.date.accessioned2020-06-22T14:35:17Z
dc.date.available2016-10-07T18:28:48Z
dc.date.available2020-06-22T14:35:17Z
dc.date.issued2016-08
dc.identifier.urihttp://hdl.handle.net/20.500.12648/1078
dc.descriptionMaster of Science Thesis in Computer and Information Sciences Department of Computer Sciences, SUNY Polytechnic Institute. Approved and recommended for acceptance as a thesis in partial fulfillment of the requirements for the degree of Master of Science in Computer and Information Sciences.en_US
dc.description.abstractThe goal of this thesis is to investigate and analyze botnet activity on social media networks. We first start by creating an algorithm and scoring method for “likely bots,” and analyze them in conjunction with their neighboring messages to determine whether there is a likely group of bots, or botnet. Chapters 1 & 2 cover the overview of the work, and the previous research done by others. Multiple datasets were collected from Twitter, over different time frames, including random samples, and targeted topics. Chapters 3 & 4 cover the methodology and results of the approach using these datasets. The method is shown to have high accuracy.en_US
dc.language.isoen_USen_US
dc.subjectbotnet activityen_US
dc.subjectTwitteren_US
dc.subjectSocial Media Networksen_US
dc.titleBotnet Campaign Detection on Twitteren_US
dc.typeThesisen_US
refterms.dateFOA2020-06-22T14:35:17Z
dc.description.institutionSUNY Polytechnic Institute


Files in this item

Thumbnail
Name:
JFields-Thesis.pdf
Size:
5.394Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record