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Garg, Puneet. Application of ensemble Machine Learning models for phishing detection on web networks. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Application of ensemble Machine Learning models for phishing detection on web networks
Name: Puri, Navyah
Organization :
Guru Tegh Bahadur Institute of Technology. Department of Information Technology
Email :
purinavyah08@gmail.com
Name: Saggar, Pranay
Organization :
Guru Tegh Bahadur Institute of Technology. Department of Information Technology
Email :
pranaysaggar@gmail.com
Name: Kaur, Amandeep
Organization :
Guru Tegh Bahadur Institute of Technology. Department of Information Technology
Email :
amandeep.gtbit@gmail.com
Name: Garg, Puneet
Organization :
ABES Engineering College
Email :
puneetgarg.er@gmail.com
LCSH:
Phishing
LCSH:
Computer networks
-- Security measures
LCSH:
Internet fraud
LCSH:
Web sites
-- Security measures
LCSH:
Machine learning
Abstract:
Phishing is a technique of fraud and identity
stealing that includes convincing Website visitors to
provide confidential info and details such as their user id,
secret key, payment info, and so on. It is one of the real
safety concerns that the online revolution is worried
about, and it may cost businesses and users money. The
use of SHAP values to better comprehend the model
employed in phishing URL detection is the research's
highlight. This research examines multiple machine
learning models for detecting phishing by examining
various aspects of the website's URL. The dataset that
was used to train the model is open source, consisting of
datasets from Alexa, UCI, Phishtank, and Kaggle. There
are 11,055 rows and 32 columns in the data set. The data
were normalized using the SMOTE analysis technique,
which resulted in a larger data set. This data was then fed
into a variety of classification and ensemble models
(K-means, Random forest, decision tree, CatBoost
classifier, LightGBM classifier, AdaBoost, and voting
classifier). The Accuracy and F1 values of the models
were compared. The model's accuracy was tested before
and after using smote. After putting all of the strategies to
the test, we observed that CatBoost Classifier produced
the best results for accuracy and F1 value. To conclude,
SHAP values are a crucial part in model interpretation
and are utilized to identify important features in the
model and how they impact the output of the model. This
model can be used by authorities and companies to stop
phishing attacks and identify suspicious sites before
someone is harmed by them.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2024-05-17
Issued:
2024-05-17
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Computer Society. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT 2022) (pp.296-303). Los Alamitos, CA : IEEE Computer Society
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.03527 วินาที
Puri, Navyah
| Title | Contributor | Type |
|---|---|---|
| Application of ensemble Machine Learning models for phishing detection on web networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Puri, Navyah;Saggar, Pranay;Kaur, Amandeep;Garg, Puneet | บทความ/Article |
Saggar, Pranay
| Title | Contributor | Type |
|---|---|---|
| Application of ensemble Machine Learning models for phishing detection on web networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Puri, Navyah;Saggar, Pranay;Kaur, Amandeep;Garg, Puneet | บทความ/Article |
Kaur, Amandeep
| Title | Contributor | Type |
|---|---|---|
| Application of ensemble Machine Learning models for phishing detection on web networks
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Puri, Navyah;Saggar, Pranay;Kaur, Amandeep;Garg, Puneet | บทความ/Article |
Garg, Puneet
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| Application of ensemble Machine Learning models for phishing detection on web networks
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