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AI/ML Security

Phishing Detection

ML-Based URL Classifier

99.6%
Accuracy
10K+ phishing URLs
Dataset
Latest
Tech (2026)
₹5,000
Price

What It Solves

Detecting phishing websites using machine learning-based URL classification. Extracts 200+ features from URLs including entropy analysis, path depth, subdomain detection, and Unicode patterns. Uses ensemble of Random Forest and Gradient Boosting for high accuracy detection with low false positives.

Comparison: Base Paper vs This Project

Aspect Base Paper This Project
Method Text-only features ML-based URL classifier
Model Traditional heuristics Random Forest + Gradient Boosting ensemble
Features Static checks only 200+ extracted features (entropy, URL depth, path analysis)
Accuracy ~85% 99.6%
F1-Score ~0.85 0.997
False Positives High (15%+) Low (0.34%)
Interface CLI Professional Streamlit + API

Tech Stack

Pythonscikit-learnFastAPINLPPyTorch

Code Structure

phishing-guard/
├── app.py                     # Main application
├── setup_env.py              # Environment setup
├── train.py                  # Training script
├── email_scanner.py           # Email analysis
├── pyproject.toml            # Project config
│
├── 03_training/              # Training pipeline
│   ├── dataset.py           # Dataset loading
│   ├── train_with_mlflow.py # MLflow integration
│   └── model_manager.py     # Model management
│
├── 02_models/               # Trained models
│   ├── phishing_classifier.joblib
│   ├── feature_columns.joblib
│   ├── feature_scaler.joblib
│   ├── classifier.py
│   └── model_metrics.json   # 99.6% accuracy
│
├── tests/                    # Test suite
│   ├── test_security.py
│   ├── test_comprehensive.py
│   └── test_cases.txt
│
├── docs/                    # Documentation
│   ├── architecture.md
│   ├── development-guide.md
│   ├── FINAL_REPORT.md
│   └── TESTING_GUIDE.md
│
├── diagram_prompts/         # UML diagrams
│   ├── 01_system_architecture.md
│   ├── 02_use_case_diagram.md
│   ├── 03_activity_diagram.md
│   └── ...
│
├── release/                 # Built executables
│   ├── phishing-guard-desktop
│   └── phishing-guard.sh
│
└── viva/                  # Presentation
    ├── Phishing_Guard_Complete_Documentation.pdf
    └── Phishing_Guard_Professional_Presentation.pptx

What's Included

  • Complete source code (Python/scikit-learn)
  • Trained model weights (.joblib)
  • Feature extraction pipeline
  • One documentation type FREE (IEEE Report / PPT / README)
  • Additional documentation: +₹500 per type
  • Base paper + citation
  • Streamlit + FastAPI interface
  • Unit tests + test cases
  • Demo videos
  • Email support (7 days)

Base Paper

Phishing Detection Using ML-Based URL Classification

arXiv, 2024

View Paper →

Get This Project

₹5,000 per project

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What's Included in Documentation

Each project comes with comprehensive documentation to help you understand and present your project.

Project Report

IEEE format documentation

Presentation Slides

PPT for viva presentation

README Guide

Step-by-step setup instructions

Base Paper

Original research paper cited

Note: These are the documents included. Click "View Details" on each project page to see actual project-specific documentation.