Estimation of distribution algorithm for optimization of neural networks for intrusion detection system
- Authors
- Chen, Yuehui; Zhang, Yong; Abraham, Ajith
- Issue Date
- 2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS, v.4029, pp 9 - 18
- Pages
- 10
- Journal Title
- ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS
- Volume
- 4029
- Start Page
- 9
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/65461
- DOI
- 10.1007/11785231_2
- ISSN
- 0302-9743
1611-3349
- Abstract
- An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. An IDS does not eliminate the use of preventive mechanism but it works as the last defensive mechanism in securing the system. This paper evaluates the performances of Estimation of Distribution Algorithm (EDA) to train a feed-forward neural network classifier for detecting intrusions in a network. Results are then compared with Particle Swarm Optimization (PSO) based neural classifier and Decision Trees (DT). Empirical results clearly show that evolutionary computing techniques could play an important role in designing real time intrusion detection systems.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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