Phishing Detection

Introduction

Phishing attacks are a major threat in today’s digital world. Millions fall victim to deceptive websites and emails each year.

At Betsi Eco Hub, we’ve developed a machine learning model to accurately detect phishing attempts. This model uses advanced data analysis and visualization techniques to boost security.

Skills Used

• Data Analysis: Interpreting complex data to discover meaningful patterns.
• Machine Learning (Unsupervised Learning): Algorithms that learn from data without labeled examples.
• Data Visualization: Representing data insights using graphical tools.
• Python Programming: Coding in Python to build and test the model.

Project Overview

We used the Phishing Website Dataset to train and test our model. After preprocessing the data, we applied the Isolation Forest algorithm to detect anomalies and potential phishing threats.

Key Findings

• Most Significant Indicator: “Using the IP Address Long URL to Hide the Suspicious Part” had the highest impact, with a mean anomaly score of 0.537127.
• Top Features: The five most critical indicators for phishing detection were:
  1. Using the IP Address Long URL to Hide the Suspicious Part
  2. Sub Domain and Multi Sub Domains HTTPS
  3. Domain Registration Length
  4. Using URL Shortening Services
  5. Redirecting using ‘//’
• Algorithm Performance: The Isolation Forest algorithm successfully identified anomalies, pinpointing potential phishing threats. The visualizations below illustrate these results.
• Data Preprocessing: Essential for improving the accuracy of the model.
• Unsupervised Learning Success: The Isolation Forest algorithm effectively identified anomalies without needing labeled data, showcasing the strength of unsupervised learning.

Visualizations

Anomaly Detection using Isolation Forest

Conclusion

This project showcases our ability to apply advanced machine learning techniques to real-world challenges. The results emphasize the importance of data preprocessing and the effectiveness of unsupervised learning for anomaly detection. We’re excited to apply these skills in future data science and cybersecurity projects.

Additional Information

• Project Date: 07/13/2024
• Client: Internal Project
• Category: Data Science, Cybersecurity
• Tags: Machine Learning, Data Visualization, Phishing Detection, Python

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