(A) tree-based intrusion detection system (IDS) considering data features = 데이터의 특징을 고려한 트리 기반 침입탐지 시스템

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Intrusion Detection is a serious global concern. The potential of network intrusion has posed a threat to national security; meanwhile the increasing prevalence of malware and incidents of network intrusions hinder the utilization of the Internet to its greatest benefit and incur significant economic losses to individuals, enterprises and public organizations. In this thesis, an efficient algorithm for Intrusion Detection System as Tree-based Intrusion Detection System considering Data Features is proposed to enhance the misclassification, detection and false positive rate by considering data features. Our results show a significant improvement in the misclassification, detection and false positive rate for the most difficult to detect attacks (e.g., Probing). In ours simulation, we used a Neural Network as classifier. This classifier basically shows lower performances than others. Nevertheless, our approach shows the better results in most cases. For that reason, if our approach, Tree-based Intrusion Detection System considering Data Features, is applied to other classifiers ( e.g., Support Vector Machine and Self-Organizing Map ) when design an intrusion detection system, we will get improved results.
Advisors
Kim, Se-Hunresearcher김세헌researcher
Description
한국과학기술원 : 산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2010
Identifier
418980/325007  / 020083041
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2010.2, [ iv, 42 p. ]

Keywords

데이타 특징; IDS; Data-Mining; Data Features; 침입탐지시스템; 데이타마이닝

URI
http://hdl.handle.net/10203/40877
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=418980&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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