文章基本信息

标题：Random Forest, Support Vector Machine and Nearest Centroid Methods for Classifying Network Intrusion
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作者：Sanjiban Sekhar Roy ; Dishant Mittal ; Marenglen Biba 等
期刊名称：Annals. Computer Science Series
印刷版ISSN：1583-7165
电子版ISSN：2065-7471
出版年度：2016
卷号：14
期号：1
页码：9-17
出版社：Mirton Publishing House, Timisoara
摘要：Software systems that are capable of controlling a network of computers for malicious intervention which focus on defraud and inspecting information are known as intrusion detection systems (IDS). Constantly changing and the complicated nature of intrusion activities on computer networks cannot be dealt with IDSs that are currently operational. In this paper, a Random Forests method based on the averaging method is proposed as a novel method to predict the types of intrusion attacks. Support vector classification model and nearest centroid classification model are used as the comparison models. The experimental results indicated that the intended model performed as well as the most advanced models like decision trees and outperforms the state of the art techniques like support vector classification models and nearest centroid classification model for the mentioned dataset with respect to parameters such as accuracy, the detection rate and false alarm
关键词：Intrusion detection system; Random forests; Support vector classification; Accuracy; Detection rate; False Alarm