文章基本信息

标题：FEATURE SELECTION, LEARNING METRICS AND DIMENSION REDUCTION IN TRAINING AND CLASSIFICATION PROCESSES IN INTRUSION DETECTION SYSTEMS
本地全文：下载
作者：FABIO MENDOZA PALECHOR ; ALEXIS DE LA HOZ MANOTAS ; EMIRO DE LA HOZ FRANCO 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：82
期号：2
出版社：Journal of Theoretical and Applied
摘要：This research presents an IDS prototype in Matlab that assess network traffic connections contained in the NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior results applying dimension reduction technique ISOMAP. The classification process used a supervised learning technique called Support Vector Machines (SVM). The comparative analysis related to detection rates by attack category are conclusive that MRMR+PCA+SVM (selection, reduction and classification techniques) combined obtained more promising results, just using 5 of 41 available features in the dataset. The results obtained were: 85.42% normal traffic, 80.77% DoS, 90.41% Probe, 91.78% U2R and 83.25% R2L.
关键词：System Intrusion Detection (IDS); Feature Selection Toolbox (FEAST); Isometric Feature Mapping ISOMAP; Support Vector Machine (SVM); Principal Component Analysis (PCA).