Imbalanced Data Detection Kernel Method in Closed Systems

摘要

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & ISVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.