Feature Selection in Phishing Attack Detection Using Bee Algorithm and Logistic Regression Method

Phishing attacks are one of the most damaging attacks for internet users. Detecting these attacks is one of the main challenges in the internet security due to their lack of unpredictable nature. Machine learning techniques are suitable methods to detect these attacks. The accuracy of these methods...

Full description

Saved in:
Bibliographic Details
Published in2024 9th International Conference on Technology and Energy Management (ICTEM) pp. 1 - 4
Main Authors Razzaghi-Asl, Nazila, Tanha, Jafar, Imanzadeh, Soodabeh, Samadi, Negin, Mostafaei, Sahar Hassanzadeh, Nojavan, Sayyad
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.02.2024
Subjects
Accuracy
Bee algorithm
Bees algorithm
Feature extraction
Feature selection
Forestry
Internet security
Logistic regression
Phishing
Phishing attack
Random forest algorithm
Online AccessGet full text

Cover

More Information
Summary:Phishing attacks are one of the most damaging attacks for internet users. Detecting these attacks is one of the main challenges in the internet security due to their lack of unpredictable nature. Machine learning techniques are suitable methods to detect these attacks. The accuracy of these methods is highly dependent on the features of the data. This paper proposes a hybrid feature selection approach based on Bee algorithm and Logistic Regression method, which detects phishing attacks accurately by selecting an appropriate feature subset. The results show that the proposed approach has been able to increase the detection accuracy of phishing attacks by 1.23%, using a random forest algorithm, in comparison to previous works with 6.66% less features.
DOI:10.1109/ICTEM60690.2024.10631946