In intrusion detection systems, classifiers still suffer from several
drawbacks such as data dimensionality and dominance, different network feature
types, and data impact on the classification. In this paper two significant
enhancements are presented to solve these drawbacks. The first enhancement is
an improved feature selection using sequential backward search and information
gain. This, in turn, extracts valuable features that enhance positively the
detection rate and reduce the false positive rate. The second enhancement is
transferring nominal network features to numeric ones by exploiting the
discrete random variable and the probability mass function to solve the problem
of different feature types, the problem of data dominance, and data impact on
the classification. The latter is combined to known normalization methods to
achieve a significant hybrid normalization approach. Finally, an intensive and
comparative study approves the efficiency of these enhancements and shows
better performance comparing to other proposed methods.

本文解决入侵检测系统中分类器存在的问题，提出使用顺序向后搜索和信息增益进行改进的特征选择方法以及利用离散随机变量和概率质量函数将名义网络特征转换为数值型特征的方法，并组合使用已知的标准化方法来实现混合标准化方法，通过广泛的比较研究证明改进后的方法在性能上表现更好。