- Title
- Data classification for artificial intelligence construct training to aid in network incident identification using network telescope data
- Creator
- Cowie, Bradley
- Creator
- Irwin, Barry V W
- Subject
- To be catalogued
- Date Issued
- 2010
- Date
- 2010
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/430125
- Identifier
- vital:72667
- Identifier
- https://doi.org/10.1145/1899503.1899544
- Description
- This paper considers the complexities involved in obtaining training da-ta for use by artificial intelligence constructs to identify potential network incidents using passive network telescope data. While a large amount of data obtained from network telescopes exists, this data is not current-ly marked for known incidents. Problems related to this marking process include the accuracy of the markings, the validity of the original data and the time involved. In an attempt to solve these issues two methods of training data generation are considered namely; manual identification and automated generation. The manual technique considers heuristics for finding network incidents while the automated technique considers building simulated data sets using existing models of virus propagation and malicious activity. An example artificial intelligence system is then constructed using these marked datasets.
- Format
- 5 pages
- Format
- Language
- English
- Relation
- Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
- Relation
- Cowie, B. and Irwin, B., 2010, October. Data classification for artificial intelligence construct training to aid in network incident identification using network telescope data. In Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists (pp. 356-360)
- Relation
- Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists volume 2010 number 1 356 360 2010 978-1-60558-950-3
- 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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Collections
Irwin, Barry V W (Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
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