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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1974/1720
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| Title: | A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection |
| Authors: | Stewart, IAN |
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| Keywords: | Network security
Intrusion Detection Systems (IDS)
Data mining
Machine learning
Real time detection
Genetic algorithm
Neural networks |
| Issue Date: | 2009 |
| Series/Report no.: | Canadian theses |
| Abstract: | 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. |
| Description: | Thesis (Master, Computing) -- Queen's University, 2009-03-03 13:28:23.787 |
| URI: | http://hdl.handle.net/1974/1720 |
| Appears in Collections: | Queen's Theses & Dissertations
Computing Graduate Theses
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