On detecting and mitigating phishing attacks through featureless machine learning techniques

被引：1

作者：

Martins de Souza, Cristian H. ^{[1
]}

Lemos, Marcilio O. O. ^{[1
]}

Dantas Silva, Felipe S. ^{[1
]}

Souza Alves, Robinson L. ^{[2
]}

机构：

[1] IFRN, Fed Inst Educ Sci & Technol Rio Grande do Norte, LaTARC Res Lab, Natal, RN, Brazil

[2] IFRN, Fed Inst Educ Sci & Technol Rio Grande do Norte, DIATINF, Natal, RN, Brazil

来源：

INTERNET TECHNOLOGY LETTERS | 2020年 / 3卷 / 01期

关键词：

attack detection; attack mitigation; machine learning; neural network; phishing;

D O I：

10.1002/itl2.135

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

The expansion of the Internet has grown the possibilities for fraudulent actions. Among these possibilities, we highlight the phishing activity, created with the objective of capturing user's credentials through a false page similar to the original one. This work proposes PhishKiller, a tool capable of detecting and mitigating phishing attacks by means a proxy approach employed to intercept user-accessed addresses, and featureless machine learning techniques to classify URLs. The proof-of-concept evaluation results revealed that PhishKiller has a more cost-effective compared to state of the art, with an accuracy of 98.30% and taking only 81.68 ms to predict and block malicious websites.

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页数：6

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