| Publisher | Chung-Ang University | ||
|---|---|---|---|
| Format | 579.8KB PDF | Date added | 31 Jul 2005 |
| Topics | Network Security, Security Tools, Intrusion Detection Systems | ||
| Downloads | 4 | ||
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 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.
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