An Intrusion Detection Model based on Hybrid Classification algorithm

摘要：

　　Due to using the single classification algorithm can not meet the performance requirements of intrusion detection,combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data,an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed.The model first preprocesses the NSL-KDD intrusion detection data set.And then by exploiting the advantages of KNN algorithm in data values,the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance.Finally,by naive Bayes to get the final result.The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy,sensitivity,false detection rate,specificity,and missed detection rate.

作者: Manfu Ma Wei Deng Hongtong Liu Xinmiao Yun

作者单位: College of Computer Science and Engineering,Northwest Normal University,Gansu,China;Internet of Thin College of Computer Science and Engineering,Northwest Normal University,Gansu,China

会议类型: 国际会议

会议名称: 2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)

会议地点: 北京

会议语种:英文

页码: 1-6

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）