Phishing Detection System Based on Classification Confidence and Website Features

doi:10.3969/j.issn.1671-1122.2017.09.026

Abstract

Abstract:

This paper develops an anti-phishing system to combat the increasing amount and severity of phishing attacks. To this end, features based on URLs and Web links are constructed and used to train two Adaboost models, which can detect phishing URLs with a high accuracy. In particular, the confidence of the model on the detected URLs is exploited further to improve the detected results. Extensive experiments conducted on a real-world dataset show the effectiveness of the proposed approach, achieving an accuracy of 96.7% with a missing alarm rate and false alarm rate as low as 3.59% and 2.93%.

Key words: phishing detection, machine learning, statistical analysis, classification confidence

CLC Number:

TP309.1

Xu CHEN, Yukun LI, Huaping YUAN, Wenyin LIU. Phishing Detection System Based on Classification Confidence and Website Features[J]. Netinfo Security, 2017, 17(9): 111-114.

Figures/Tables 6

References 16

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