최적의 피싱 웹사이트 탐지를 위한 다중 계층 앙상블 모델: PhishGuard
PhishGuard는 Random Forest, Gradient Boosting, CatBoost, XGBoost 등 다양한 기계 학습 분류기를 결합한 최적화된 앙상블 모델로, 피싱 웹사이트 탐지 정확도를 크게 향상시킨다.
PhishGuard: A Robust Ensemble Model for Optimal Phishing Website Detection
PhishGuard, a multi-layered ensemble model, achieves superior phishing website detection performance by combining the strengths of multiple optimized machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost.
Robust and Adaptable Web Phishing Detection through Federated-Continual Learning and Attention-Based Classifier
A novel hybrid learning paradigm that combines federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data without accumulating data, while leveraging an attention-based classifier model tailored for web phishing detection.
Detecting Phishing Websites Using a Hybrid Deep Learning Model of Artificial Neural Network and Long Short-Term Memory
A hybrid deep learning model combining Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) achieves high accuracy in detecting phishing websites.
A Comprehensive Framework for Phishing Website Detection
Developing a sophisticated stacking ensemble classifier for accurate phishing website detection.
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