声明
1. Introduction
1.2Background and Significance
1.2.1Intrusion Detection System
1.2.2Working of Intrusion Detection System
1.3Research Content and methodology
1.3.1Data Mining Process
1.3.2Data Mining Techniques and IDS
1.4 Objectives
1.5Scope of Thesis
1.6Key Problems solved
2.Intrusion Detection System Models
2.1 Background
2.2Hybrid IDS Classification Approach Model
2.3Hybrid Support Vector Machine (SVM) Model
2.4Na(i)ve Bayes Intrusion Detection System Model
2.5Hybrid Cluster Model for IDS
2.6 C4.5
3Proposed Approach
3.1 Introduction
3.2Feature Selection
3.3Fisher Discriminant Ratio (FDR)
3.4Classification Module
3.5Classifiers used
3.5.1 BayesNet
3.5.2J48 (C4.5 Decision Tree Revision 8)
3.5.3Decision Table
3.5.4JRip (RIPPER)
3.5.5Multilayer Perceptron (MLP)
3.5.6 SMO
3.5.7 IBk
3.6General Form of Proposed Model
4.Experiment and Results
4.1 Introduction
4.2Data set description
4.3Evaluation Setup
4.4Data Pre-Processing
4.5Classification and Performance Comparison
4.6Discussion on Results
5. Conclusion
参考文献
Research Projects and Publications in Master Study Publications during master study
致谢
Dalian University of Technology
Copyright Use Authorization of Master Degree Dissertation
大连理工大学学位论文版权使用授权书