https://github.com/sayakpaul/phishing-websites-detection

Experiments to detect phishing websites using neural networks
https://github.com/sayakpaul/phishing-websites-detection

cybersecurity keras-tensorflow machine-learning neural-network phishing-attacks scikit-learn

Last synced: 10 days ago
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Experiments to detect phishing websites using neural networks

• Host: GitHub
• URL: https://github.com/sayakpaul/phishing-websites-detection
• Owner: sayakpaul
• Created: 2019-05-03T08:58:30.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-24T12:00:37.000Z (about 5 years ago)
• Last Synced: 2025-01-10T06:44:10.809Z (13 days ago)
• Topics: cybersecurity, keras-tensorflow, machine-learning, neural-network, phishing-attacks, scikit-learn
• Language: Jupyter Notebook
• Size: 6 MB
• Stars: 18
• Watchers: 1
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Phishing-Websites-Detection

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sayakpaul/Phishing-Websites-Detection/master)

The aim of the experiments conducted in the accompanying notebook is to present an idea of how modern _phishing website attacks_ can be prevented using machine learning. To do this, we are going to use the [Phishing Websites' Dataset](https://archive.ics.uci.edu/ml/datasets/phishing+websites). The viewers are requested to take a look at [this paper](https://archive.ics.uci.edu/ml/machine-learning-databases/00327/Phishing%20Websites%20Features.docx) by the authors of the dataset. The paper discusses the data generation strategy in details and how the authors were able to come up with the most significant set of features for _detecting phishing websites_. 

The machine learning models shown here can be easily served as REST API endpoints which can further be used in conjunction with add-ons to detect phishing websites in real-time.