An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250168" target="_blank" >RIV/61989100:27230/22:10250168 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-9717/10/7/1356/htm" target="_blank" >https://www.mdpi.com/2227-9717/10/7/1356/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr10071356" target="_blank" >10.3390/pr10071356</a>

Result language

angličtina

Original language name

An Effective and Secure Mechanism for Phishing Attacks Using a Machine Learning Approach

Original language description

Phishing is one of the biggest crimes in the world and involves the theft of the user&apos;s sensitive data. Usually, phishing websites target individuals&apos; websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack for precisely detecting the problems in a content-based manner as a phishing attack mechanism. There were three input values-uniform resource locators and traffic and web content based on features of a phishing attack and non-attack of phishing website technique features. To implement the proposed phishing attack mechanism, a dataset is collected from recent phishing cases. It was found that real phishing cases give a higher accuracy on both zero-day phishing attacks and in phishing attack detection. Three different classifiers were used to determine classification accuracy in detecting phishing, resulting in a classification accuracy of 95.18%, 85.45%, and 78.89%, for NN, SVM, and RF, respectively. The results suggest that a machine learning approach is best for detecting phishing.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

nestrankovano

UT code for WoS article

000833302000001

EID of the result in the Scopus database

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