Security breach has been recorded in high volume and has compromised several Information Systems and critical applications as well. An Intrusion Detection is the process of analyzing the events occurring in an information system in order to detect different security threats and vulnerabilities. Research and development communities are putting their extra effort for optimizing Intrusion Detection System performance as network data traffic including vulnerabilities are found to be complex and have shown dynamic properties. The idea to explore if certain classifier perform better for certain attack classes constitutes the motivation for this research work. In this research, performance of a comprehensive set of potential classifiers using Knowledge Discovery and Data (KDD99) dataset has been evaluated. Based on evaluated results, maximum accurate classifier for high attack detection rate and low false alarm rate has been chosen and suitable classifier has been proposed. The comparison of simulation result indicates that noticeable performance improvement can be achieved with the proposed classifier to detect different kinds of network attacks and security vulnerabilities.
목차
Abstract 1. Introduction 1.1 Problem Statement 1.2 Research Purpose 2. Methodology 2.1 Implementation and Comparison Model 2.2 Experimentation Modality 3. Performance Evaluation 4. Analysis of WEKA results and output 4.1. IDS implementation 5. Conclusion and Future Works References
키워드
Intrusion Detection System (IDS)KDD99 datasetClassifier selectionSecurity
한국AI디지털융합학회(구 한국디지털융합학회) [The Korean Academic Society of AI Digital Convergence]
설립연도
2015
분야
사회과학>경영학
소개
본 학회는 디지털 경영에 관련된 디지털 미디어, 디지털 통신, 디지털 방송, 디지털 콘텐츠, 디지털 문화, 디지털 사회, 디지털 유통, 디지털 금융, 디지털 물류, 디지털 정책, 디지털 기술, 디지털 교육 그리고 디지털과 아날로그의 비교 등에 대한 학제간 연구와 실사구시적인 적용을 통하여 디지털 경영의 발전과 한국이 세계적인 디지털 강국으로 성장하기 위한 학술적인 기반과 실무적인 지침을 조성하는 것을 목적으로 하고 있습니다.
간행물
간행물명
IJICTDC [International Journal of Information Communication Technology and Digital Convergence]