NetShield

Your AI-Powered Shield Against Digital Threats

NetShield (formerly CodeGuardians) is a comprehensive cybersecurity command center designed to protect users from modern digital threats. It unifies detection engines for AI-generated deepfakes, phishing attacks, malicious URLs, and unsafe commands into a single platform.

Core Features

1. AI Image Detection

Goal: Detect AI-generated images, deepfakes, and synthetic media.

• Technology: Hugging Face-hosted inference integrated through gradio_client
• How it works:
  • Sends uploaded image input to the configured AI detection endpoint.
  • Returns prediction label with confidence score.

2. Phishing Email Detector

Goal: Analyze emails and messages to detect social engineering and phishing attempts.

• Technology: Hybrid approach (ML + Rules + URL Analysis)
• Features:
  • ML Model: TF-IDF vectorization with a saved scikit-learn phishing classifier.
  • Rule-Based Engine: Detects urgency and social-engineering keywords.
  • URL Scanner: Extracts links and validates each URL with ML + Google Safe Browsing.

3. URL Safety Scanner

Goal: Verify if a website link is safe before clicking.

• Technology: Hybrid service (Custom URL ML Model + Google Safe Browsing API)
• Features:
  • Machine Learning Analysis: Lexical/structural URL feature-based detection.
  • Real-time Verification: Cross-checks links with Google Safe Browsing.
  • Combined Verdict: Returns a final risk status for user action.

4. Dangerous Command Analysis

Goal: Help users avoid risky shell commands.

• Technology: Static analysis and pattern matching
• How it works:
  • Detects destructive and suspicious shell patterns.
  • Flags escalation/network/obfuscation risk patterns.

5. Password Toolkit

Goal: Generate strong passwords and check breach exposure.

• Technology: Cryptographically secure RNG + HaveIBeenPwned API
• Features:
  • Generator: Configurable high-entropy password generation.
  • Strength Meter: Real-time complexity feedback.
  • Breach Check: k-Anonymity password leak lookup.

6. Browser Extension

Goal: Real-time support while browsing.

• Technology: Chrome Extension Manifest V3 (JavaScript/HTML/CSS)
• Features:
  • Password-field detection support.
  • Popup utilities for phishing and password-related actions.

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Tech Stack

System Architecture

graph TD
    User([User/Source]) --> WebUI[Frontend Static Pages]
    User --> BrowserExt[Chrome Extension MV3]

    WebUI --> API[Flask Backend]
    BrowserExt --> API

    API --> URLModel[URL ML Model - scikit-learn]
    API --> PhishModel[Phishing Text Model - scikit-learn + TF-IDF]
    API --> SafeBrowsing[Google Safe Browsing API]
    API --> AIDetect[Hugging Face Inference via gradio_client]

    WebUI --> PwnedAPI[HaveIBeenPwned Password API]

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Front-end (frontend/)

• HTML5 Pages: Multi-page interface in frontend/pages.
• Vanilla JavaScript: Feature logic in frontend/js.
• CSS3: Shared styles in frontend/css/style.css with static assets in frontend/assets.

Browser Extension (extension/)

• Manifest V3: Defined in extension/manifest.json.
• Modules: background/, content/, popup/, and utils/.

Back-end (backend/)

• Flask (Python 3): App entry and routing in backend/app.py.
• Services: Modular detection/safety services in backend/services.
• Server & Deployment: gunicorn, Procfile, and vercel.json.
• Config: Environment loading through python-dotenv.

ML / AI

• Stored Models: .pkl files in backend/model loaded via joblib.
• Training Utilities: Model/data scripts in backend/utils.
• Libraries: scikit-learn, pandas, Pillow.
• Image AI Integration: Hugging Face endpoint client via gradio_client.

External Integrations

• Google Safe Browsing API
• HaveIBeenPwned API (k-Anonymity range endpoint)

Installation & Setup

Prerequisites

• Python 3.8 or higher

1. Clone the Repository

git clone https://github.com/athrvas017/Code_Guardians.git
cd Code_Guardians

2. Create Virtual Environment

python -m venv venv

3. Activate Virtual Environment

# Windows (PowerShell)
.\venv\Scripts\Activate.ps1

# Windows (Command Prompt)
.\venv\Scripts\activate.bat

# Linux/macOS
source venv/bin/activate

4. Install Dependencies

pip install -r backend/requirements.txt

5. Set Environment Variables

Create a .env file in the project root (or use .env.example) and set:

• GOOGLE_API_KEY
• HF_API_URL
• HF_API_TOKEN (if required by your HF endpoint)
• SECRET_KEY (optional)

Running the Application

Start the Backend Server

cd backend
python app.py

The app runs on http://127.0.0.1:5000 by default.

Navigation

Route	Feature
/	Home / Dashboard
/ai-detection	AI Image Detection
/phishing	Phishing Detector
/url-safety	URL Safety Checker
/password	Password Toolkit
/awareness	Cyber Awareness Guide
/command-analysis	Dangerous Command Analysis

Project Structure

Code_Guardians/
|-- backend/
|   |-- app.py
|   |-- requirements.txt
|   |-- model/             # Trained .pkl models/vectorizers
|   |-- services/          # Detection and safety services
|   |-- templates/         # Flask templates
|   `-- utils/             # Training and feature utilities
|-- frontend/
|   |-- pages/             # Static HTML pages
|   |-- js/                # Frontend scripts
|   |-- css/
|   `-- assets/
|-- extension/
|   |-- manifest.json
|   |-- background/
|   |-- content/
|   |-- popup/
|   `-- utils/
`-- README.md

Roadmap Snapshot

• Partially Implemented: Database layer integration, password manager module.
• Future Enhancements: Advanced password manager, improved URL model, improved AI image model.