Large-Scale Realistic Network Data Generation on a Budget

dc.contributor.VIAF51867299 (Thuraisingham, BM)
dc.contributor.authorRicks, Brian
dc.contributor.authorTague, P.
dc.contributor.authorThuraisingham, Bhavani M.
dc.contributor.utdAuthorRicks, Brian
dc.contributor.utdAuthorThuraisingham, Bhavani M.
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractMany novel problems in computer networking require relevant network trace data during the research process. Unfortunately, such data can often be hard to find, which becomes a problem within itself. While generating appropriate data using in-lab network testbeds and simulators are feasible solutions, the former has limitations in terms of network scale, while the latter has limitations in the generated data. To help address these issues, we present an approach for the generation of realistic network trace data in a contained, large-scale network environment. We use network emulation to enable large-scale, in-lab networking, and a software framework we developed to support autonomous client-side protocols and services, including user-behavioral models which scale in a shared CPU environment. Our framework also enables quick experiment setup and monitoring. We show through experimentation on a low-end laptop that our approach enables network scale into the hundreds of nodes, allowing anyone with even basic hardware to generate potentially relevant, realistic network data.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationRicks, B., P. Tague, and B. Thuraisingham. 2018. "Large-scale realistic network data generation on a budget." Proceedings - International Conference on Information Reuse and Integration for Data Science, 19th: 23-30, doi:10.1109/IRI.2018.00012
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights©2018 IEEE
dc.source.journalProceedings - International Conference on Information Reuse and Integration for Data Science, 19th
dc.subjectBehavioral assessment--Research
dc.subjectBudget--Cost control
dc.subjectComputer networks
dc.subjectComputer programming
dc.subjectHuman behavior models
dc.subjectComputer networks
dc.subjectFeasibility studies--Computer programs
dc.titleLarge-Scale Realistic Network Data Generation on a Budget


Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
164.17 KB
Adobe Portable Document Format
Link to Article