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About Phishing Website Detection by Machine Learning Techniques Presentation
This document discusses detecting phishing websites through machine learning techniques. It aims to train models on a dataset containing features extracted from phishing and legitimate URLs to predict whether new URLs are phishing sites. The approach involves collecting a dataset of URLs, extracting 17 features from the URLs, dividing the data into training and test sets, training classifiers like decision trees, random forests, neural networks and evaluating their accuracy on the test set. The XGBoost model achieved the best performance with over 80% accuracy. Developing a browser extension using the model to classify URLs in real-time was proposed as a next step.
Detailed Information
| Author: | ['Munagala Sai Sumanth'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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