🔒 Bharat Suraksha Netra The RakShak

🚀 Overview

Bharat Suraksha Netra is an advanced cybersecurity platform designed to assist Indian Public Sector Banks (PSBs) in detecting and mitigating malicious/phishing domains. This system leverages machine learning, deep learning, and advanced feature extraction techniques to enhance security and expedite threat analysis.

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📌 Features

✅ Advanced URL Analysis: Validates and analyzes suspicious URLs using multiple cybersecurity models.
✅ Machine Learning & Deep Learning Integration: Utilizes Random Forest, Gradient Boosting, XGBoost, LightGBM, and Neural Networks.
✅ Real-time Security Insights: Provides risk levels based on predictive analytics.
✅ Automated PDF Security Reports: Generates professional security reports for banks and agencies.
✅ Flask API Backend: A secure API-based infrastructure for URL threat intelligence.
✅ Streamlit Frontend Dashboard: Interactive web interface for real-time security visualization.

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📂 Project Structure

📦 Bharat-Suraksha-Netra
├── 📜 advanced-cybersecurity-frontend.py  # Streamlit-based Web Dashboard
├── 📜 advanced-model-training.py          # ML Model Training & Feature Extraction
├── 📜 advanced-security-report-backend.py # Flask Backend for API & Report Generation
├── 📂 models/                             # Pretrained models storage
├── 📂 reports/                            # Auto-generated security reports
└── 📜 requirements.txt                     # Python dependencies

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⚙️ Installation & Setup

🔹 Prerequisites

• Python 3.8+
• pip (Package Manager)
• Virtual Environment (Recommended)

🔹 Step 1: Clone the Repository

git clone https://github.com/yourusername/bharat-suraksha-netra.git
cd bharat-suraksha-netra

🔹 Step 2: Set up a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # For Windows, use: venv\Scripts\activate

🔹 Step 3: Install Dependencies

pip install -r requirements.txt

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🚀 Running the Application

Step 1️⃣: Start Backend API

python advanced-security-report-backend.py

Step 2️⃣: Run the Web Dashboard

python advanced-cybersecurity-frontend.py

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🛡️ How to Use

1️⃣ Enter a URL in the Streamlit dashboard.
2️⃣ The system analyzes the URL and predicts the risk level.
3️⃣ View detailed security insights and AI-based confidence scores.
4️⃣ Generate a PDF security report for official documentation.

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🤖 Tech Stack

🔹 Frontend: Streamlit, Plotly
🔹 Backend: Flask, TensorFlow, Scikit-learn, XGBoost, LightGBM
🔹 Database: Pandas (for lightweight storage)
🔹 Security APIs: WHOIS, SSL Analysis, IP Geolocation

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📜 License

This project is licensed under the MIT License.

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