Evolving intrusion detection systems
- Authors
- Abraham, A.; Grosan, C.
- Issue Date
- 2006
- Publisher
- Springer Verlag
- Citation
- Studies in Computational Intelligence, v.13, pp 57 - 79
- Pages
- 23
- Journal Title
- Studies in Computational Intelligence
- Volume
- 13
- Start Page
- 57
- End Page
- 79
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47047
- DOI
- 10.1007/11521433_3
- ISSN
- 1860-949X
1860-9503
- 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 Chapter evaluates the performances of two Genetic Programming techniques for IDS namely Linear Genetic Programming (LGP) and Multi-Expression Programming (MEP). Results are then compared with some machine learning techniques like Support Vector Machines (SVM) and Decision Trees (DT). Empirical results clearly show that GP techniques could play an important role in designing real time intrusion detection systems. © 2006 Springer-Verlag Berlin Heidelberg.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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