MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||6|
|Section||Parallel Session II: Water System Technology|
|Published online||07 December 2018|
An Intrusion Detection Model based on Hybrid Classification algorithm
1 College of Computer Science and Engineering, Northwest Normal University, Gansu, China
2 Internet of Things Engineering Research Center, Gansu, China
a Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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