基于多特征选取和类完全加权的入侵检测

为提升入侵检测系统的整体性能,文中提出一种新的算法。首先使用孤立点滤除算法进行数据前期处理,通过特征选取算法筛选出各分类器中最佳的特征空间,以增强各分类算法的训练模型。再进一步运用十倍交叉验证法对分类模型实施性能评估,把具有最佳捕捉率的分类模型作为预测测试样本类别时的加权分类模型,最后得出整体推论结果。仿真实验表明该算法整体分类准确率提高到96%,成本值减低为0.198 3,能够成功地改善网络异常入侵检测的分类性能。

Intrusion Detection Based on Multiple Feature Selection and Class Fully Weighted

In order to improve the performance of intrusion detection system,a new algorithm is proposed. Firstly,the outlier deletion al-gorithm is used to obtain the training data in the data preprocessing phase. Secondly,the multiple feature selection algorithm is used to find out the best feature space for the classifiers,and then the training models of the classifiers could be well trained. Furthermore,the ten fold-cross validation is applied to evaluate the performances of the classification models,and the classification models with best recalls are used as the weighted classification models in the class fully weighted algorithm to predict the classes of test data. Finally,the inference re-sults are concluded. Simulation results show that the classification accuracy of this algorithm reaches 96%,the cost value is 0. 198 3,can enhance performance of the network intrusion detection system.