Mining Web to Detect Phishing URLs

被引：5

作者：

Basnet, Ram B. ^{[1
]}

Sung, Andrew H. ^{[2
]}

机构：

[1] Sage Technol Partners Inc, Albuquerque, NM 87102 USA

[2] ICASA, New Mexico Tech, Comp Sci & Engn, Socorro, NM USA

来源：

2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 1 | 2012年

关键词：

web mining; phishing detection; phishing URL; anti-phishing; machine learning;

D O I：

10.1109/ICMLA.2012.104

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Proliferation of phishing attacks in recent years has presented an important cybersecurity research area. Over the years, there has been an increase in the technology, diversity, and sophistication of these attacks in response to increased user awareness and countermeasures. In this paper, we propose a novel scheme to automatically detect phishing URLs by mining and extracting Meta data on URLs from various Web services. Applying the proposed approach on real-world data sets, it is demonstrated that Logistic Regression classifier can achieve an overall accuracy of 97.2-99.8%, false positive rate of 0.1-1% and false negative rate of 0.7-6.5% in detecting phishing and non-phishing URLs.

引用

页码：568 / 573

页数：6

共 14 条

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