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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Processes

ISSN

2227-9717

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

nestrankovano

Kód UT WoS článku

000833302000001

EID výsledku v databázi Scopus

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