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Phishing Website Detection Using Deep Learning Models
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AI
DOI：10.1109/ACCESS.2024.3486462.png)
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Abstract
En 中文
This research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ensemble learning, and deep learning algorithms. Cybersecurity is essential for protecting data and networks from threats. Detecting phishing websites helps prevent fraud and safeguard personal information. To evaluate the efficacy of our proposed method, the top features using information gain, gain ratio, and PCA are used to predict and identify a website as phishing or non-phishing. The proposed system is trained using a dataset that covers 11,055 websites. The ensemble learning model applied achieved an impressive 99% accuracy in predicting phishing websites, surpassing previous models, and setting a new benchmark in the field. The findings highlight the effectiveness of combining deep learning architectures with ensemble learning, offering not only improved accuracy but also adaptability to emerging phishing techniques.
Keywords:
Phishing
Blocklists
Accuracy
Uniform resource locators
Protocols
Internet
Accesslists
Principal component analysis
IP networks
Feature extraction
Deep learning
ensemble learning
feature selection
GRU
LSTM
machine learning
phishing detection
RNN
RF
XGBoost
Journal
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3.6
Papers:
9.8W
Citations:
29.4W
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