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Intelligent analysis to detect phishing websites using machine learning ensemble techniques

Furkejuvvon:
Publikašuvnnas:Human-Intelligent Systems Integration vol. 6, no. 1 (Dec 2024), p. 39
Almmustuhtton:
Springer Nature B.V.
Fáttát:
Machine learning
Datasets
Accuracy
Personal information
Internet
Browsing
Neural networks
Websites
Phishing
Support vector machines
Classification
Sensitivity analysis
Algorithms
Decision trees
Cybercrime
Learning algorithms
Fraud
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Abstrákta:The Internet has grown to be a vital part of our everyday existence. Web browsing is the most popular Internet service. A lot of people use their browser for banking, online shopping, bill paying, and mobile phone recharging. Due to the extensive use of this service, users are exposed to many security risks, including cybercrime. One kind of online danger that lures consumers into connecting with a phoney website is cyber phishing. This study paper’s primary objective is to safeguard sensitive user data. The suggested model is created in three stages. In the first phase, we select a dataset to train on and subsequently use the dataset to test classifiers. After applying the three classifiers in step 2 and finishing all of the predictions in step 3, we found that XGBoost performed better than the machine learning techniques AdaBoost and Gradient boosting.
ISSN:2524-4876
2524-4884
DOI:10.1007/s42454-024-00053-9
Gáldu:Advanced Technologies & Aerospace Database