随机森林算法在入侵检测系统中的应用及优化

Application and Optimization of Random Forest Algorithms in Intrusion Detection System

为了提高入侵检测系统的检测精度,增加检测率及降低误报率,将随机森林算法引入到入侵检测系统中,提出了一种对随机森林的优化方法。该算法经各种降维处理及参数优化,最后应用于入侵检测模型中。经过多种数据集测试后的实验结果表明,相比于传统的入侵检测模型,引入随机森林算法后的模型,系统检测率最高提至95.2%,误报率最低降至2%以下,较好地提高了入侵检测系统的精度。

In this paper,the random forest algorithm is introduced into the intrusion detection system,and an optimization method for the random forest algorithm is proposed.After various dimension reduction and parameter optimization,the algorithm is finally applied to the intrusion detection model.Tested with multiple data sets,the results of the experiment show that compared with the traditional intrusion detection model,the system detection rate of the model with the random forest algorithm was improved to 95.2%,the false alarm rate was reduced to 2%.