📧 MalGuard Classifier

A complete Anamoly Detection project using Hybrid approach Rule Based + ML to classify url as Safe or Malicious. The project includes data preprocessing, Feature Engineering, training ML model, API development and Chrome Extension.

It automatically flags malicious URLs and shows safe URLs with a ✅, providing users with a safer browsing experience.

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📘 1. Project Overview

• Real-time phishing detection in Chrome

• Legitimate websites are marked safe

• Suspicious/phishing URLs are flagged automatically

• Hybrid model: Isolation Forest + Whitelist

• Confusion matrix & evaluation metrics included

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🔄 2. Pipeline Workflow

1. Load Dataset
2. Clean Text
3. Train Model → preprocessing/train.py
4. Evaluate Model → preprocessing/evaluate.py
5. Select Model & Save → models/model.pkl
6. Visualize Metrics → Confusion Matrix
7. Deploy with API and Chrome Extension

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📦 Extension

Install MalGuard (Developer Mode)

1. Download the extension from GitHub
2. Extract the folder
3. Open chrome://extensions/
4. Enable Developer mode
5. Click Load unpacked and select the MalGuard folder

MalGuard will start protecting you immediately.

🔌 API Integration

MalGuard communicates with a backend API for real-time predictions.

Endpoint

GET https://malguard.onrender.com/predict?url=
<encoded_url>

Sample Response

{
  "prediction": "Malicious",
  "raw_value": -1,
  "url": "url"
}

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📈 3. Evaluation Metrics & Graphs

Metrics calculated for test data:

Metric	Score
Accuracy	0.90
Precision	0.90
Recall	0.99
F1 Score	0.94

Class-wise performane

Class	Precision	Recall	F1-score	Support
-1 (Phishing)	1.00	0.66	0.79	126
1 (Legitimate)	0.93	1.00	0.96	569

Visualizations Saved in reports/plots/:

1. Confusion Matrix

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🛠️ 4. Installation

# Clone the repo
git clone https://github.com/roshan-acharya/MalGuard.git
cd MalGuard

# Create virtual environment (optional)
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

🔄 5. Running the Project

• Run Training Pipeline

  python -m pipeline

• Run Flask App

  python -m Api.main

🧰 6. Tech Stack

• Python

• Pandas

• NumPy

• Scikit-learn

• Matplotlib, Seaborn

• Flask

• Pickle (for saving models)

👨‍💻 7. Author

Roshan Acharya
AI/ML Enthusiast