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WebCypher - Real-Time Phishing Website Detector Chrome Extension

WebCypher Logo

WebCypher is a Google Chrome extension that helps users detect phishing websites in real-time. With the increasing sophistication of online scams and phishing attacks, it is essential to have a reliable tool that can protect users from falling victim to such fraudulent activities. WebCypher is designed to analyze website URLs and alert users if they are potentially accessing a phishing website.

Installation

To install WebCypher using a zip file, follow these steps:

  1. Visit the WebCypher repository on GitHub: WebCypher Repository.

  2. Click on the "Code" button and select "Download ZIP" to download the extension as a zip file.

  3. Extract the contents of the zip file to a folder on your computer.

  4. Open the Google Chrome browser and type chrome://extensions in the address bar.

  5. Enable the "Developer mode" option located at the top right corner of the Extensions page.

  6. Click on the "Load unpacked" button.

  7. In the file explorer window, navigate to the folder where you extracted the contents of the zip file.

  8. Select the folder and click "Open" to load the extension.

  9. The WebCypher extension will be installed and visible in the list of extensions on the Chrome Extensions page.

  10. Ensure the toggle switch next to WebCypher is enabled to activate the extension.

How to Use

Using WebCypher is simple:

  1. Ensure that the WebCypher extension is enabled (the icon will be visible in the Chrome toolbar).

  2. Browse the internet as you normally would.

  3. As you visit websites, WebCypher will automatically analyze the URLs in real-time.

  4. If WebCypher detects a potential phishing website, a pop-up notification will appear, alerting you of the potential threat.

  5. Exercise caution when visiting websites that trigger a warning and avoid entering sensitive information if the website is flagged as suspicious.

Reporting False Positives

WebCypher strives to provide accurate phishing detection, but false positives may occur. If you believe a website has been incorrectly flagged as a phishing website, please report it to us through the following steps:

  1. Click on the WebCypher icon in the Chrome toolbar.

  2. Select the "Report False Positive" option from the drop-down menu.

  3. Provide the necessary information, including the URL and a brief explanation of why you believe it is a false positive.

  4. Click "Submit" to send the report to our team for review.

Privacy and Data Security

WebCypher is committed to protecting your privacy and maintaining the security of your data. The extension does not collect or store any personal information or browsing history. All analysis and detection are performed locally on your device.

Disclaimer

WebCypher is an extension designed to enhance your online security; however, it is important to note that no security tool is perfect. While WebCypher employs advanced techniques to detect phishing websites, it may not catch every instance or variation of a phishing attack. It is crucial to remain vigilant and exercise caution when sharing personal information online.

Thank you for choosing WebCypher!

Phishing URL Detection API

Table of Content

Introduction

The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mockup websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. To see project click here.

Directory Tree

├── pickle
│   ├── model.pkl
├── Phishing URL Detection.ipynb
├── Procfile
├── README.md
├── app.py
├── feature.py
├── phishing.csv
├── requirements.txt

Technologies Used

Result

Accuracy of various model used for URL detection


ML Model Accuracy f1_score Recall Precision
0 Gradient Boosting Classifier 0.974 0.977 0.994 0.986
1 CatBoost Classifier 0.972 0.975 0.994 0.989
2 XGBoost Classifier 0.969 0.973 0.993 0.984
3 Multi-layer Perceptron 0.969 0.973 0.995 0.981
4 Random Forest 0.967 0.971 0.993 0.990
5 Support Vector Machine 0.964 0.968 0.980 0.965
6 Decision Tree 0.960 0.964 0.991 0.993
7 K-Nearest Neighbors 0.956 0.961 0.991 0.989
8 Logistic Regression 0.934 0.941 0.943 0.927
9 Naive Bayes Classifier 0.605 0.454 0.292 0.997

Feature importance for Phishing URL Detection

image

Conclusion

  1. The final take away form this project is to explore various machine learning models, perform Exploratory Data Analysis on phishing dataset and understanding their features.
  2. Creating this notebook helped me to learn a lot about the features affecting the models to detect whether URL is safe or not, also I came to know how to tuned model and how they affect the model performance.
  3. The final conclusion on the Phishing dataset is that the some feature like "HTTTPS", "AnchorURL", "WebsiteTraffic" have more importance to classify URL is phishing URL or not.
  4. Gradient Boosting Classifier currectly classify URL upto 97.4% respective classes and hence reduces the chance of malicious attachments.