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asif-nawaz-ml/network-security-phishing

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App README

🔐 Network Security - Phishing Detection System

Python FastAPI MongoDB Accuracy

📋 Project Overview

An end-to-end Machine Learning project that detects phishing websites using network security features. Built with a complete MLOps pipeline.

🏗️ Project Architecture

MongoDB Atlas → Data Ingestion → Data Validation → Data Transformation → Model Trainer → FastAPI App

📊 Dataset

  • —Source: Phishing Website Dataset
  • —Rows: 11,055
  • —Features: 30 network security features
  • —Target: Result (Phishing or Legitimate)

🎯 Model Performance

MetricTrainTest
Accuracy98.78%97.31%
Precision98.78%97.32%
Recall98.78%97.31%
F1 Score98.78%97.31%

🔧 Tech Stack

  • —Language: Python 3.10
  • —ML: Scikit-learn, XGBoost
  • —Database: MongoDB Atlas
  • —API: FastAPI
  • —Preprocessing: KNN Imputer, Robust Scaler, SMOTETomek
  • —Deployment: Hugging Face Spaces (Docker)
  • —CI/CD: GitHub Actions

📁 Project Structure

NetworkSecurity/ ├── networksecurity/ │ ├── components/ │ │ ├── dataingestion.py │ │ ├── datavalidation.py │ │ ├── datatransformation.py │ │ └── modeltrainer.py │ ├── entity/ │ │ ├── configentity.py │ │ └── artifactentity.py │ ├── pipeline/ │ │ └── training_pipeline.py │ ├── exception/ │ ├── logging/ │ └── utils/ ├── templates/ │ └── index.html ├── app.py ├── Dockerfile ├── requirements.txt └── schema.yaml

🚀 How to Run Locally

1. Clone Repository

bash
git clone https://github.com/Asif5588-M/NetworkSecurity.git
cd NetworkSecurity

2. Create Environment

bash
conda create -n networksecurity python=3.10 -y
conda activate networksecurity

3. Install Dependencies

bash
pip install -r requirements.txt

4. Set Environment Variables

bash
MONGODB_URL="your-mongodb-url"

5. Run App

bash
python app.py

6. Open Browser

http://localhost:7860

🌐 Live Demo

Hugging Face Space

👨‍💻 Author

Asif Malik