| Autor: |
Eliazer, M., Baalaji, Haree, Abhilash, Chalamcharla Naga |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3075 Issue 1, p1-6, 6p |
| Abstrakt: |
Phishing is a type of cybercrime when unsuspecting individuals are persuaded to give crucial informationto the phishers through spammed messages and phony websites. This is how confidential information gathered is utilized to access money or take people. This study aims to build a phished channel using several machine learning methods. Classification is a machine learning approach that may be used to identify phishers. It creates and tests models using a number of setting combinations, contrasts different machine learning techniques, assesses the accuracy of a created model, and calculates a range of assessment metrics. In the current study, Nave Bayes (NB) and Random Forest (RF) are two machine learning techniques that are compared for their forecast performance, F1Score, precession, and recall. The approach is also improved by employing feature selection methods, which increases the accuracy in detecting phishing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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