As our reliance on the Internet continues to grow, the need for secure, reliable networks also increases. Using a modified genetic algorithm and a switch-based neural network model, this thesis outlines the creation of a powerful intrusion detection system (IDS) capable of detecting network attacks.
The new genetic algorithm is tested against traditional and other modified genetic algorithms using common benchmark functions, and is found to produce better results in less time, and with less human interaction. The IDS is tested using the standard benchmark data collection for intrusion detection: the DARPA 98 KDD99 set. Results are found to be comparable to those achieved using ant colony optimization, and superior to those obtained with support vector machines and other genetic algorithms. / Thesis (Master, Computing) -- Queen's University, 2009-03-03 13:28:23.787
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/1720 |
| Date | 17 March 2009 |
| Creators | Stewart, IAN |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | 1260857 bytes, application/pdf |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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