Denial of Service (DoS) Attack Detection: Performance Comparison of Supervised Machine Learning Algorithms

被引：2

作者：

Li, Zhuolin ^{[1
]}

Zhang, Hao ^{[1
]}

Shahriar, Hossain ^{[2
,3
]}

Lo, Dan ^{[1
]}

Qian, Kai ^{[1
]}

Whitman, Michael ^{[3
]}

Wu, Fan ^{[4
]}

机构：

[1] Kennesaw State Univ, Dept Comp Sci, Kennesaw, GA 30144 USA

[2] Kennesaw State Univ, Dept Informat Technol, Kennesaw, GA 30144 USA

[3] Kennesaw State Univ, Inst Cybersecur Workforce Dev, Kennesaw, GA 30144 USA

[4] Tuskegee Univ, Dept Comp Sci, Tuskegee, AL 36088 USA

来源：

2020 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH) | 2020年

基金：

美国国家科学基金会;

关键词：

Denial of Service; Cybersecurity; Naive Bayes; Artificial Neural Network; Logistic Regression;

D O I：

10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00088

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Denial of Service ( DoS) is one of the common attempts in security hacking for making computation resources unavailable or to impair geographical networks. In this paper, we detect Denial of Service (DoS) attack from publicly available datasets using Logistic regression, Naive Bayes algorithm and artificial neural networks. The results from our experiments indicate that the accuracy, ROC curve and balanced accuracy of artificial neural network were higher than Naive Bayes algorithm and logistic regression for slightly imbalanced distribution dataset.

引用

页码：469 / 474

页数：6

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