Explore innovative cyber security projects focused on securing data, detecting threats, and protecting networks. Ideal for learning ethical hacking, intrusion detection, and advanced security protocols.

1. Clone the Repository\n bash\n git clone [repository-url]\n cd upi-fraud-detection\n ``````bash\n pip install -r requirements.txt\n ``````bash\n python train_model.py\n ``````bash\n python app.py\n ``````\n http://localhost:5000\n

<h2>Overview</h2>
    <p>This <strong>best Python project</strong> leverages state-of-the-art machine learning to detect fraudulent Unified Payments Interface (UPI) transactions in real time. Designed as a robust <strong>final year project</strong>, it combines Flask backend, interactive frontend, and a powerful Random Forest Classifier trained on over <strong>6.3 million transaction records</strong>. Ideal for engineering students looking for <strong>unique projects for final year students</strong> in Computer Science, Data Science, or Cybersecurity.</p>


    <h3>Key Features</h3>
    <ul>
      <li><strong>✅ Real-Time Fraud Detection:</strong> Analyzes live transaction patterns instantly.</li>
      <li><strong>✅ 99%+ Accuracy:</strong> Achieved via Random Forest model fine-tuned on massive datasets.</li>
      <li><strong>✅ Advanced AI Algorithm:</strong> Trained on 6.3M+ real-world UPI transaction records.</li>
      <li><strong>✅ Modern Cybersecurity UI:</strong> Dark-themed dashboard with Chart.js visualizations.</li>
      <li><strong>✅ Detailed Analytics Dashboard:</strong> Shows fraud probability, confidence score & insights.</li>
      <li><strong>✅ Responsive Design:</strong> Fully mobile-friendly and cross-browser compatible.</li>
      <li><strong>✅ Secure API Endpoints:</strong> Encrypted inputs, validation, and anomaly detection.</li>
    </ul>



    <h3>📊 Dataset & Model Performance</h3>
    <p>The system uses a comprehensive dataset of <strong>6,362,621 transaction records</strong> with 11 key features including:</p>
    <ul>
      <li>Transaction Type (Payment/Transfer/Cash-out)</li>
      <li>Amount Transferred</li>
      <li>Origin & Destination Account Balances (Before/After)</li>
      <li>Time Step (Simulated Timeline)</li>
      <li>Fraud Label (Binary Classification)</li>
    </ul>
    <p><strong>Model Metrics:</strong></p>
    <table>
      <tr>
        <td><strong>Training Accuracy</strong></td>
        <td>99.2%</td>
      </tr>
      <tr>
        <td><strong>Testing Accuracy</strong></td>
        <td>98.9%</td>
      </tr>
      <tr>
        <td><strong>Algorithm Used</strong></td>
        <td>Random Forest Classifier</td>
      </tr>
    </table>


    <h3>🔧 Technology Stack</h3>
    <p><strong>Backend:</strong> Python 3.8+, Flask, Scikit-learn, Pandas, NumPy</p>
    <p><strong>Frontend:</strong> HTML5, CSS3, JavaScript, jQuery, Bootstrap 5, Chart.js, Font Awesome</p>
    <p><strong>ML Pipeline:</strong> Feature Engineering, StandardScaler, Train/Test Split (80/20), Model Persistence</p>


    <h3>🎓 Applications & Use Cases</h3>
    <ul>
      <li>Banking & Fintech security monitoring systems</li>
      <li>Final year projects for B.Tech / MCA / BCA / Diploma students</li>
      <li>Academic research in AI-driven financial crime prevention</li>
      <li>Integration into digital wallet platforms for added safety</li>
      <li>Capstone project for data science and machine learning courses</li>
    </ul>
    <p>This is one of the <strong>best python projects</strong> for those interested in merging finance with artificial intelligence — making it an ideal choice among <strong>final year college projects</strong>.</p>


    <h3>💡 Why Choose This Project?</h3>
    <p>Perfect for students who want a professional-grade, deployable, and presentation-ready <strong>final year project</strong> with full source code, documentation, and explanation support.</p>
    <p><em>Add-ons available: Project Setup | Source Code Explanation | Complete Project Report</em></p>

Explore our AI-Powered UPI Fraud Shield, a unique project for final year students with real-time detection and 99% accuracy. Get source code from 99 rupees at projects.codeaj.com – perfect for final year college projects and best Python projects.

AI-Powered UPI Fraud Detection System: Best Python Project for Final Year Students | CodeAj Marketplace

An innovative UPI fraud detection system using advanced machine learning to analyze transactions in real-time, preventing fraud with 99%+ accuracy. Ideal as a final year college project or best Python project for students seeking unique projects

Extract Zip file
cd phishguard

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python app/app.py

# Access the app
Open http://localhost:5000 in your browser

<h2>Overview</h2>
<p><strong>PhishGuard</strong> is a cutting-edge cybersecurity solution designed to protect users from phishing attacks using advanced machine learning algorithms. This web application analyzes email content in real-time and provides a comprehensive assessment of whether an email is legitimate or malicious.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>🤖 AI-Powered Analysis:</strong> Utilizes a trained machine learning model on a dataset of over 82,000 emails for highly accurate phishing detection.</li>
  <li><strong>⚡ Real-Time Detection:</strong> Instantly analyzes email content and provides results in under 500ms.</li>
  <li><strong>📊 Detailed Reports:</strong> Offers confidence scores, phishing probability, and actionable insights for each analysis.</li>
  <li><strong>🔒 Privacy Focused:</strong> No data is stored during or after the analysis process, ensuring user privacy.</li>
  <li><strong>📱 Responsive Design:</strong> Works seamlessly across desktops, tablets, and mobile devices.</li>
  <li><strong>🎯 High Accuracy:</strong> Achieves a 99.9% accuracy rate in identifying phishing emails.</li>
</ul>

<h2>Applications</h2>
<p>PhishGuard is ideal for:</p>
<ul>
  <li>Individual users to verify suspicious emails before opening</li>
  <li>Organizations to integrate into internal cybersecurity training</li>
  <li>Developers looking to enhance phishing detection in their own applications</li>
  <li>Students and researchers in cybersecurity and AI fields</li>
</ul>

<h2>Technical Highlights</h2>
<ul>
  <li>TF-IDF Vectorization for text processing</li>
  <li>Support Vector Machine (SVM) Classifier for detection</li>
  <li>Custom-built Flask backend for secure and fast API handling</li>
  <li>Bootstrap 5 for modern, responsive UI</li>
  <li>Custom animations and UI enhancements using CSS3</li>
</ul>

<h2>Use Cases</h2>
<ul>
  <li>Email verification for end-users</li>
  <li>Integration with email clients as a plugin</li>
  <li>Educational tool for cybersecurity students</li>
  <li>Enterprise cybersecurity dashboard integration</li>
</ul>

<h2>Why PhishGuard?</h2>
<p>With phishing attacks becoming more sophisticated, traditional rule-based detection systems are no longer sufficient. PhishGuard leverages AI to understand the nuances of phishing emails, making it one of the most effective tools for real-time email threat detection.</p>

PhishGuard uses AI and machine learning to detect phishing emails in real-time with 99.9% accuracy. Download source code or get setup & explanation on codeAj Marketplace.

PhishGuard - AI-Powered Phishing Email Detection Tool | codeAj Marketplace

PhishGuard is an advanced AI-powered phishing email detection system built with Python, Flask, and Scikit-learn that analyzes email content in real-time to identify and flag potential phishing attempts with high accuracy.

1. Clone the repository:
   Extract the downloaded zip file
   cd vulnerability_scanner

2. Create and activate a virtual environment:
   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install dependencies:
   pip install -r requirements.txt

4. Apply migrations:
   python manage.py migrate

5. Start development server:
   python manage.py runserver

6. Access at:
   http://localhost:8000

<h2>What is WebSafe – Website Vulnerability Scanner?</h2>
<p><strong>WebSafe</strong> is a modern, open-source <em>Django-based website vulnerability scanner</em> designed to detect and report common security flaws in web applications. Whether you're a developer, ethical hacker, or cybersecurity enthusiast, this tool empowers you to test and secure websites efficiently.</p>

<h3>🔍 Key Features</h3>
<ul>
  <li><strong>Advanced Vulnerability Detection:</strong> Detects SQL Injection, XSS, CSRF, cookie misconfigurations, and insecure headers.</li>
  <li><strong>Real-time Scan Results:</strong> Interactive dashboard with categorized risk levels (High, Medium, Low) and actionable insights.</li>
  <li><strong>Modern UI/UX:</strong> Bootstrap-powered responsive interface with visual charts and clean design for better readability.</li>
  <li><strong>User-Friendly Interface:</strong> Simple scan form where users can enter URLs and instantly get results.</li>
</ul>

<h3>🛠️ Technology Stack</h3>
<p>This project uses the following technologies and libraries:</p>
<ul>
  <li>Django Framework</li>
  <li>Python Requests Library</li>
  <li>BeautifulSoup4 for HTML parsing</li>
  <li>Bootstrap & Chart.js for interactive visuals</li>
  <li>SQLite / PostgreSQL (for database support)</li>
</ul>

<h3>💼 Applications</h3>
<ul>
  <li>Educational purposes for students learning cybersecurity</li>
  <li>Penetration testing tools for developers and QA teams</li>
  <li>Security audits of internal or personal websites</li>
  <li>Raise awareness about web security best practices</li>
</ul>

<h3>⚙️ Installation Guide</h3>
<ol>
  <li>Repository: <code>Extract the provided zip file</code></li>
  <li>Create and activate virtual environment: <code>python -m venv venv && source venv/bin/activate</code></li>
  <li>Install dependencies: <code>pip install -r requirements.txt</code></li>
  <li>Migrate database: <code>python manage.py migrate</code></li>
  <li>Run server: <code>python manage.py runserver</code></li>
</ol>

<h3>🛡️ Security Considerations</h3>
<p>Please note that this tool should only be used on websites you own or have explicit permission to test. Unauthorized scanning may violate laws or terms of service agreements.</p>

<h3>🎯 Who Should Use This?</h3>
<ul>
  <li>Web Developers looking to secure their applications</li>
  <li>Cybersecurity Students and Enthusiasts</li>
  <li>QA Engineers performing security checks</li>
  <li>Organizations conducting internal audits</li>
</ul>

<h3>📈 Why Choose WebSafe?</h3>
<p>Unlike generic scanners, WebSafe offers a customizable and lightweight solution with real-time reporting and an intuitive UI. It's ideal for beginners and advanced users alike who want a simple yet effective way to evaluate website security risks.</p>

Download the Website Vulnerability Scanner built using Django. Detect SQL Injection, XSS, CSRF, and more with this easy-to-use open-source tool.

Website Vulnerability Scanner in Django | SQLi, XSS, CSRF Detector

A powerful Django-based web application that scans websites for common security vulnerabilities including SQL Injection, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF).

CodeAJ

A Django-based tool to scan websites for SQL Injection, XSS, CSRF, and other vulnerabilities.

WebSafe – Website Vulnerability Scanner

This is a Django-based secure file storage application that uses AES-256 encryption for files and offers optional face recognition authentication. The system ensures high-level data protection by encrypting files before storing them and decrypting them only during download. It includes password-based login, CSRF protection, and secure password hashing using Django’s built-in authentication system. Face recognition is an optional feature powered by the `face_recognition` library. The frontend is built with HTML, CSS, and Bootstrap for a modern, responsive UI. To set up this project: 1. Clone the repository. 2. Create a virtual environment using `python -m venv venv` and activate it (`source venv/bin/activate` on Linux/macOS or `venv\Scripts\activate` on Windows). 3. Install dependencies via `pip install -r requirements.txt`. 4. Set up environment variables by copying `.env.example` to `.env` and updating values like `SECRET_KEY`. 5. Run database migrations with `python manage.py migrate`. 6. Create a superuser using `python manage.py createsuperuser`. 7. Start the development server using `python manage.py runserver`. All files uploaded are automatically encrypted using the `cryptography` library with AES-256 encryption. This project is ideal for users who need secure file storage with advanced privacy features and can be deployed as a private cloud solution.

<section class="project-description">
  <h2>Overview</h2>
  <p>
    The <strong>Secure File Vault</strong> is a robust web-based file storage system developed using <strong>Django</strong>, offering top-tier security through <strong>AES-256 encryption</strong>. Designed for both personal and enterprise use, this project ensures your files remain private and protected at all times.
  </p>

  <h3>Key Features</h3>
  <ul>
    <li><strong>Secure File Upload & Storage:</strong> Files are automatically encrypted using AES-256 before being stored on the server.</li>
    <li><strong>Password-Based Authentication:</strong> Users can securely log in using strong password authentication.</li>
    <li><strong>Optional Face Recognition Login:</strong> Enhance security by enabling facial recognition as an alternative login method.</li>
    <li><strong>Modern Responsive UI:</strong> Built with Bootstrap for a sleek and user-friendly experience across devices.</li>
    <li><strong>On-the-Fly Decryption:</strong> Files are decrypted instantly during download, ensuring seamless access without compromising security.</li>
    <li><strong>CSRF Protection & Secure Password Hashing:</strong> Leverages Django's built-in security features to protect against common vulnerabilities.</li>
  </ul>

  <h3>Technologies Used</h3>
  <p>
    This project integrates powerful technologies such as Python (Django), HTML/CSS/Bootstrap for frontend design, and the <code>cryptography</code> library for AES encryption. SQLite is used as the default database for simplicity and ease of setup.
  </p>

  <h3>Use Cases</h3>
  <ul>
    <li>Personal cloud storage for sensitive documents</li>
    <li>Small business file sharing platform</li>
    <li>Educational institutions managing confidential data</li>
    <li>Developers looking to implement secure storage solutions</li>
  </ul>

  <h3>Why Choose Secure File Vault?</h3>
  <p>
    With increasing cyber threats, traditional file-sharing platforms may not offer sufficient protection. The <strong>Secure File Vault</strong> fills this gap by combining military-grade encryption with optional biometric authentication, making it ideal for users who prioritize privacy and control over their digital assets.
  </p>
</section>

Build your own secure file storage system with Django, AES encryption, and optional face authentication. Download the source code starting at ₹99 only at projects.codeaj.com.

Secure File Vault – AES Encrypted File Storage with Face Recognition | codeaj Projects

A secure file storage application built with Django that uses AES encryption and optional face recognition for enhanced security. Store, encrypt, and manage files safely with a modern responsive interface.

A secure file storage application built with Django that uses AES encryption and optional face recognition for enhanced security.

Secure File Vault

1. Extract the ZIP file and navigate into the folder:
   cd phishing-detection

2. Create a virtual environment and activate it:
   python -m venv venv
   # Windows: venv\Scripts\activate
   # macOS/Linux: source venv/bin/activate

3. Install dependencies:
   pip install -r requirements.txt

4. Add trained model files:
   - Place 'phishing_elm_model.h5' into static/models/
   - Place 'scaler.pkl' into static/models/

5. Run the Flask app:
   python app.py

6. Open your browser at: http://127.0.0.1:5000

<h2>Overview</h2>
<p>Protect your users and yourself from fraudulent websites with this powerful <strong>Phishing Website Detection Web Application</strong>. Using <strong>Extreme Learning Machine (ELM)</strong>, this system offers quick and intelligent classification of phishing websites. Whether you're a cybersecurity student, researcher, or enthusiast, this project is a practical showcase of applying machine learning for real-world security.</p>

<h2>Key Features</h2>
<ul>
  <li>🔍 URL-based phishing detection system</li>
  <li>💡 Advanced input mode for custom feature entries</li>
  <li>⚡ Real-time predictions with confidence score</li>
  <li>📱 Responsive design – works across mobile and desktop</li>
  <li>📘 Informative About page to explain detection methodology</li>
</ul>

<h2>Applications</h2>
<ul>
  <li>🛡️ Internet safety and web browsing protection</li>
  <li>🎓 Machine Learning & Cybersecurity academic projects</li>
  <li>💻 Integration into browser extensions or antivirus tools</li>
</ul>

<h2>Who Should Buy This?</h2>
<p>This project is ideal for:
<ul>
  <li>✔️ Final-year students in Computer Science or Cybersecurity</li>
  <li>✔️ Beginners exploring ML in security</li>
  <li>✔️ Developers building safe browsing tools</li>
</ul>
</p>

<h2>Why Choose CodeAJ?</h2>
<p>At <strong>CodeAJ Marketplace</strong>, you not only get the source code at just ₹99 but also have options for <strong>complete setup, explanation, and report preparation</strong> at affordable add-on prices. With 500+ happy students, you're in the right place to accelerate your project success.</p>

Detect fake websites with our ELM-based phishing detection web app. Get the complete source code for just ₹299 with optional setup, explanation & report services.

Phishing Website Detection Web App using ELM | CodeAJ Marketplace

A smart phishing website detector built with Extreme Learning Machine (ELM) that predicts the legitimacy of websites using URL analysis and advanced features.

An AI-powered phishing website detection system using Extreme Learning Machine with real-time prediction and manual input options. Ideal for ML or cybersecurity projects.

Phishing Website Detection Web App using ELM

<p class="paraa">

Fake App Detection is a sophisticated application developed to enhance mobile security by detecting potential threats in mobile applications. Built on a foundation of HTML, CSS, JavaScript, and Python Django, with the backend analysis powered by AndroGuard, Fake App Detection offers a comprehensive solution for users concerned about the safety of their mobile apps.
<br><br>
The core functionality of Fake App Detection lies in its ability to analyze mobile applications for dangerous permissions and malicious activities. By leveraging AndroGuard's powerful analysis capabilities, the application can identify apps that request excessive permissions or engage in suspicious behavior, helping users make informed decisions about the apps they install.
<br><br>
Using Fake App Detection is simple yet effective. Users can upload an APK file, which is then analyzed by the backend system. The analysis results are then presented to the user, highlighting any potential security risks found in the app.
<br><br>
With the rise of mobile threats, Fake App Detection provides a valuable tool for users to protect themselves against malicious apps. By empowering users with information about the security risks posed by mobile apps, Fake App Detection helps create a safer mobile ecosystem for everyone.
            </p>

Fake App Detection is a powerful tool designed to analyze mobile applications for potential security risks. Using AndroGuard for backend analysis, the application..

Fake Android App Detection

Fake App Detection is a powerful tool designed to analyze mobile applications for potential security risks. Using AndroGuard for backend analysis, the application.

Cyber Security Projects

AI-Powered UPI Fraud Shield: Real-Time Detection System for Secure Transactions

PhishGuard - AI-Powered Phishing Email Detection Tool

WebSafe – Website Vulnerability Scanner (SQLi, XSS, CSRF Checker)

Secure File Vault – AES Encrypted Cloud Storage with Face Authentication

AI-Powered Phishing Website Detection using ELM - Secure Web Validator

Fake App Detection