Identifying Generic Features for Malicious URL Detection System

被引：0

作者：

Khan, Hafiz Mohammd Junaid ^{[1
]}

Niyaz, Quamar ^{[1
]}

Devabhaktuni, Vijay K. ^{[1
]}

Guo, Site ^{[2
]}

Shaikh, Umair ^{[3
]}

机构：

[1] Purdue Univ Northwest, Coll Engn & Sci, ECE Dept, Hammond, IN 46323 USA

[2] BeulahWorks, Valparaiso, IN 46385 USA

[3] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA

来源：

2019 IEEE 10TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2019年

关键词：

computer security; malicious URL detection; machine learning; features analysis;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Malicious URLs pose serious cybersecurity threats to the Internet users. It is critical to detect malicious URLs so that they could be blocked from user access. Several techniques have been proposed to differentiate malicious URLs from benign ones. However, the goal of our work is to find the list of substantial features that can be used to classify most of the malicious URLs. In this paper, we select the most significant lexical features from different datasets using Chi-Square and ANOVA F-value. Later, we apply a voting classifier that combines several machine learning algorithms on those selected features.

引用

页码：347 / 352

页数：6

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