- Title
- Capturefoundry: a gpu accelerated packet capture analysis tool
- Creator
- Nottingham, Alastair, Richter, John, Irwin, Barry V W
- Subject
- To be catalogued
- Date
- 2012
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/430112
- Identifier
- vital:72666
- Identifier
- https://doi.org/10.1145/2389836.2389877
- Description
- Packet captures are used to support a variety of tasks, including network administration, fault diagnosis and security and network related research. Despite their usefulness, processing packet capture files is a slow and tedious process that impedes the analysis of large, long-term captures. This paper discusses the primary components and observed performance of CaptureFoundry, a stand-alone capture analysis support tool designed to quickly map, filter and extract packets from large capture files using a combination of indexing techniques and GPU accelerated packet classification. All results are persistent, and may be used to rapidly extract small pre-filtered captures on demand that may be analysed quickly in existing capture analysis applications. Performance results show that CaptureFoundry is capable of generating multiple indexes and classification results for large captures at hundreds of megabytes per second, with minimal CPU and memory overhead and only minor additional storage space requirements.
- Format
- 9 pages, pdf
- Language
- English
- Relation
- Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, Nottingham, A., Richter, J. and Irwin, B., 2012, October. Capturefoundry: a gpu accelerated packet capture analysis tool. In Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference (pp. 343-352), Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference volume 2012 number 1 343 352 2012 978-1-4503-1308-7
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the ACM Digital Library Statement (https://libraries.acm.org/digital-library/policies#anchor3)
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- Visitors: 5
- Downloads: 2
Collections
Irwin, Barry V W (Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Capturefoundry.pdf | 688 KB | Adobe Acrobat PDF | View Details |