Accurate Deauthentication Attack Detection using SVM Classifier in Comparison with Decision Tree Classifier

被引：0

作者：

Janardhan, B. ^{[1
]}

Karthikeyan, P. R. ^{[1
]}

机构：

[1] Saveetha Univ, Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Elect & Commun Engn, Chennai 602105, Tamil Nadu, India

来源：

JOURNAL OF PHARMACEUTICAL NEGATIVE RESULTS | 2022年 / 13卷

关键词：

Deauthentication attack; SVM; Novel Features Selection; Decision Tree Classifier; Machine Learning; Entropy; DOS ATTACKS; NETWORKS;

D O I：

10.47750/pnr.2022.13.S04.091

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Aim: This study aims to compare the accuracy of Support Vector Machine Classifier to Decision Tree Classifier in detecting deauthentication attacks. Materials and Methods: The dataset used in this study consists of 61,000 records. For testing, 9,604 records calculated using G power are used to achieve a 95 % confidence level in accuracy with 1% margin error. Each record consists of 42 attributes/features. In order to detect deauthentication attacks, SVM and Decision Tree are used. Results: The accuracy of SVM was 87.02%, P<0.05, whereas the accuracy of the Decision Tree Classifier was 71.81%, P<0.05. Conclusion: Comparing SVM to Decision Tree Classifier, the present study found that SVM performed significantly better in detecting deauthentication attacks.

引用

页码：796 / 803

页数：8

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