首页|A lightweight data representation for phishing URLs detection in IoT environments
A lightweight data representation for phishing URLs detection in IoT environments
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NSTL
Elsevier
Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). Hence, it is advantageous to detect a phishing attack by analyzing URLs before accessing them. With the raising of the Internet of Things (IoT), phishing attacks are moving to this field due to the number of IoT devices and the amount of personal information they handle. Although several approaches were proposed for phishing attacks detection, the URL-based Machine Learning approaches obtain better performance results, but all of them are dependent on the feature set used. Contradictorily, only a few works on selecting the best-suited feature set for improving the phishing detection process have been published. The present research explores how to obtain a feature set that substantially enhances the phishing detection rate in IoT environments. Hence, a feature selection algorithm was adopted and extended for getting the most representative feature set. When Random Forest is used with the proposed data representation, the phishing URL attacks discovery rate is 99.57%. (c) 2022 Published by Elsevier Inc.
URLPhishing attacksMachine learningCustom feature setInternet of ThingsSELECTION
Bustio-Martinez, Lazaro、Alvarez-Carmona, Miguel A.、Herrera-Semenets, Vitali、Feregrino-Uribe, Claudia、Cumplido, Rene
NSTL
Elsevier
