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.

To install HashFlow: Clone the repository, then run 'pip install -r requirements.txt' to install dependencies. Apply database migrations using 'python manage.py migrate', create a superuser with 'python manage.py createsuperuser' if needed, and start the server via 'python manage.py runserver'. Access the app at http://127.0.0.1:8000. Ensure Python 3.8+ and pip are installed. For production, configure ALLOWED_HOSTS, use Gunicorn + Nginx, and enable HTTPS. All security middleware is pre-configured.

<h2>What is HashFlow?</h2>
<p><strong>HashFlow</strong> is a powerful, educational, and interactive <strong>Hash Algorithm Visualizer</strong> built using Django and modern web technologies. Whether you're a student learning cryptography, a developer testing hash outputs, or a cybersecurity enthusiast, HashFlow makes understanding hashing algorithms like <strong>MD5, SHA-1, SHA-256, and SHA-512</strong> intuitive and engaging.</p>

<h2>Key Features</h2>
<ul>
  <li><strong>Multiple Hash Algorithms</strong>: Choose between MD5, SHA-1, SHA-256, and SHA-512 — all in one place.</li>
  <li><strong>Interactive Step-by-Step Visualization</strong>: Watch how your input text transforms through each stage of the hashing process in real-time.</li>
  <li><strong>Advanced Visualization Mode</strong>: Dive deep into the internal workings of each algorithm with animated flow diagrams and byte-level transformations.</li>
  <li><strong>Dark Theme UI</strong>: A modern, eye-friendly interface designed for long sessions and low-light environments.</li>
  <li><strong>History Tracker</strong>: Never lose your previous hash results — view, copy, or compare past hashing attempts with ease.</li>
  <li><strong>Security-First Design</strong>: Built with CSRF protection, input sanitization, rate limiting, and Content Security Policy (CSP) headers to ensure safe usage.</li>
  <li><strong>Responsive Design</strong>: Works seamlessly across desktops, tablets, and mobile devices.</li>
</ul>

<h2>Applications & Use Cases</h2>
<ul>
  <li><strong>Educational Tool</strong>: Perfect for computer science students studying cryptography and data integrity.</li>
  <li><strong>Developer Utility</strong>: Quickly generate hash values for passwords, files, or API keys.</li>
  <li><strong>Cybersecurity Training</strong>: Demonstrate weaknesses of MD5/SHA-1 and strengths of SHA-256+ in penetration testing workshops.</li>
  <li><strong>Classroom Demo App</strong>: Teachers can use this to visually explain hashing concepts without complex code.</li>
  <li><strong>Freelancers & Agencies</strong>: Integrate as a micro-tool in security audit reports or client dashboards.</li>
</ul>

<h2>Technology Stack</h2>
<p>HashFlow leverages a robust combination of modern and secure technologies:</p>
<ul>
  <li><strong>Backend</strong>: Django (Python) – for secure routing, session management, and business logic.</li>
  <li><strong>Frontend</strong>: HTML5, CSS3, JavaScript (ES6+) – for dynamic interactivity and visualization.</li>
  <li><strong>Styling</strong>: Bootstrap 5 + Custom CSS – ensures responsiveness and aesthetic appeal.</li>
  <li><strong>Icons</strong>: Font Awesome – clean, scalable icons for UX enhancement.</li>
  <li><strong>Security</strong>: Django middleware, CSP headers, rate limiting via <code>django-ratelimit</code>, and CSRF tokens.</li>
  <li><strong>Visualization Engine</strong>: Vanilla JS with Canvas & DOM manipulation for real-time hashing animation.</li>
</ul>

<h2>Why Choose This Project?</h2>
<ul>
  <li><strong>Ready-to-Deploy</strong>: Comes with full setup guide and requirements.</li>
  <li><strong>Beginner-Friendly Codebase</strong>: Clean, well-commented code ideal for learning or customization.</li>
  <li><strong>Monetization Ready</strong>: Easily extendable into a SaaS tool with user accounts, API access, or premium features.</li>
  <li><strong>SEO-Optimized Landing Page</strong>: Included in the package for immediate online visibility.</li>
</ul>

<h2>What You Get</h2>
<ul>
  <li>Full Source Code</li>
  <li>Detailed Installation Guide</li>
  <li>Project Setup Support (Optional Add-on)</li>
  <li>Source Code Explanation (Optional Add-on)</li>
  <li>Project Report (PDF) – Ideal for submissions (Optional Add-on)</li>
</ul>

<p>Start your journey into cryptographic visualization today with <strong>HashFlow – The Smart Way to Learn Hashing</strong>.</p>

Interactive Hash Algorithm Visualizer in Django with real-time MD5, SHA-1, SHA-256 & SHA-512 encryption. Dark UI, step-by-step animation, history tracking & security features. Source code starts at ₹99 only on codeaj.

Hash Algorithm Visualizer - MD5 SHA-1 SHA-256 SHA-512 Visual Tool | codeaj Marketplace

A sleek Django-powered web app that visually demonstrates how popular hash algorithms work — from input to encrypted output — with step-by-step animation, dark-theme UI, history tracking, and enterprise-grade security.

1. Clone the repository and navigate to the project root.
2. Create a virtual environment: python -m venv .venv
3. Activate it:
   - Windows: .venv\Scripts\Activate.ps1 (PowerShell)
   - macOS/Linux: source .venv/bin/activate
4. Upgrade pip: pip install --upgrade pip
5. Install dependencies: pip install -r requirements.txt
   - For Windows, if python-magic fails: pip install python-magic-bin==0.4.27
6. Set environment variables:
   - SECRET_KEY="your-secret-key"
   - VIRUSTOTAL_API_KEY="your-vt-api-key"
7. Run the app: python app.py
8. Access the dashboard at http://localhost:5000
9. On first run, the model will auto-train. To retrain manually: python train_model.py
10. For real-time monitoring, go to /real_time and select a directory to watch.

<div class="project-description">
  <h2>Overview</h2>
  <p class="lead">
    MalwareGuard AI is a powerful, open-source <strong>AI-driven malware detection platform</strong> built using <strong>Flask, TensorFlow, YARA, and VirusTotal API</strong>. It delivers real-time threat analysis for executable files, documents, and compressed archives using a hybrid approach combining <strong>machine learning, signature-based scanning, and behavioral heuristics</strong>.
  </p>

  <h3>Why MalwareGuard AI?</h3>
  <p>
    Traditional antivirus tools often rely on outdated signature databases. MalwareGuard AI goes beyond by using <strong>deep learning models</strong> trained on synthetic yet realistic features, combined with <strong>static binary analysis, entropy checks, PE inspection, and dynamic threat intelligence</strong> from VirusTotal. This multi-layered approach ensures high detection rates even for zero-day threats.
  </p>

  <h3>Key Features</h3>
  <ul class="feature-list">
    <li><strong>File Upload & Scan</strong> – Upload files up to 100MB and receive instant risk analysis with a final verdict: <em>Benign, Suspicious, or Malicious</em>.</li>
    <li><strong>Real-Time Directory Monitoring</strong> – Monitor any folder recursively; new files are automatically scanned upon arrival.</li>
    <li><strong>Static Analysis Engine</strong> – Extracts MD5, SHA1, SHA256 hashes, entropy, strings, URLs, emails, and file type using <code>pefile</code>, <code>python-magic</code>, and custom heuristics.</li>
    <li><strong>YARA Rule Scanning</strong> – Load custom YARA rules from <code>data/yara_rules/</code>. Sample rules included for crypto-mining and malware indicators.</li>
    <li><strong>VirusTotal Integration</strong> – Automatically checks file reputation via hash lookup or upload. Supports API key injection via environment variables.</li>
    <li><strong>AI-Powered Classification</strong> – A TensorFlow-based neural network classifies files using 20+ synthetic features. Model auto-trains if missing.</li>
    <li><strong>Weighted Risk Scoring</strong> – Final risk score = <code>0.4*ML + 0.3*YARA + 0.3*VirusTotal</code>, ensuring balanced, intelligent verdicts.</li>
    <li><strong>RESTful API</strong> – Integrate into CI/CD, SOC workflows, or third-party tools using <code>POST /api/scan</code> with multipart upload support.</li>
    <li><strong>Web Dashboard</strong> – Intuitive UI built with Jinja2 templates for upload, real-time monitoring, and result visualization.</li>
  </ul>

  <h3>Technology Stack</h3>
  <p>
    This project leverages cutting-edge technologies to deliver robust, scalable, and secure malware detection:
  </p>
  <ul class="tech-list">
    <li><strong>Backend:</strong> Flask, Werkzeug</li>
    <li><strong>Machine Learning:</strong> TensorFlow 2.13, scikit-learn, numpy, pandas</li>
    <li><strong>Binary Analysis:</strong> pefile, python-magic (or python-magic-bin on Windows)</li>
    <li><strong>Threat Intelligence:</strong> VirusTotal API v2</li>
    <li><strong>Signature Detection:</strong> yara-python</li>
    <li><strong>Frontend:</strong> HTML5, Jinja2, CSS, JavaScript</li>
  </ul>

  <h3>Real-World Applications</h3>
  <ul class="use-cases">
    <li><strong>Cybersecurity Firms:</strong> Use as a lightweight threat analyzer in SOC environments.</li>
    <li><strong>Academic Research:</strong> Study ML-based malware detection and feature engineering.</li>
    <li><strong>DevOps & CI/CD:</strong> Integrate into pipelines to scan build artifacts before deployment.</li>
    <li><strong>Incident Response:</strong> Deploy in isolated VMs for forensic file analysis.</li>
    <li><strong>Enterprises:</strong> Monitor shared folders or user uploads for suspicious content.</li>
  </ul>

  <h3>How It Works</h3>
  <ol class="workflow">
    <li><strong>File Upload</strong> – User uploads a file via web UI or API.</li>
    <li><strong>Static Feature Extraction</strong> – Extracts file size, entropy, imports, sections, strings, and hashes.</li>
    <li><strong>YARA Scanning</strong> – Matches against active YARA rules; generates risk score.</li>
    <li><strong>VirusTotal Check</strong> – Queries VT for existing reports or uploads the file.</li>
    <li><strong>ML Prediction</strong> – Scales features and runs through a trained neural network.</li>
    <li><strong>Final Verdict</strong> – Aggregates scores into a final risk score and classification.</li>
    <li><strong>Result Display</strong> – Shows detailed report with confidence, matches, and hashes.</li>
  </ol>

  <h3>Security & Privacy</h3>
  <p>
    All uploaded files are <strong>automatically deleted after scanning</strong>. For API usage, files are not stored. However, <strong>VirusTotal may retain uploaded files</strong>—ensure compliance with your organization's data policies. Always run this tool in an <strong>isolated environment (e.g., VM or sandbox)</strong> when analyzing untrusted binaries.
  </p>

  <h3> Extensibility & Customization</h3>
  <p>
    MalwareGuard AI is designed for developers and security professionals:
  </p>
  <ul>
    <li>Add custom YARA rules to <code>data/yara_rules/</code>.</li>
    <li>Extend feature extraction in <code>models/feature_extractor.py</code>.</li>
    <li>Retrain the model with real-world datasets using <code>train_model.py</code>.</li>
    <li>Adjust risk weights in <code>app.py</code> for your threat model.</li>
  </ul>

  <div class="callout">
    <h4>Pro Tip:</h4>
    <p>Replace the synthetic dataset with real malware/benign samples for production use. Combine with sandboxing tools like Cuckoo for dynamic analysis.</p>
  </div>
</div>

Download MalwareGuard AI – a powerful Flask-based malware detection tool using TensorFlow, YARA, and VirusTotal API. Real-time scanning, REST API, and web dashboard included. Only ₹99 for source code.

AI-Based Malware Detection System | Flask & TensorFlow | CodeAJ Marketplace

MalwareGuard AI – A Flask-based malware detection system combining static analysis, YARA rules, VirusTotal API, and deep learning for accurate threat detection. Features a web dashboard, real-time monitoring, and REST API.

To install and run this project:  
1. Clone the repository: git clone https://github.com/codeaj/image_steganography.git  
2. Create a virtual environment: python -m venv venv  
3. Activate it:  
   - On Windows: venv\Scripts\activate  
   - On Mac/Linux: source venv/bin/activate  
4. Install dependencies: pip install -r requirements.txt  
5. Run GUI: python run.py  
6. For CLI: python cli.py hide input.png "Secret Message" password output.png  
7. To extract: python cli.py extract output.png password  
Ensure you use PNG images for best results. Test with small messages first.

<h2>Overview</h2>
  <p>
    Looking for a <strong>best Python project</strong> that combines <strong>cybersecurity, cryptography, and digital forensics</strong>? This <strong>Image Steganography with Encryption</strong> project is a cutting-edge solution designed for <strong>final year engineering and computer science students</strong>. It enables users to securely hide encrypted messages within image files using advanced <strong>AES-256 encryption</strong> and <strong>Least Significant Bit (LSB) steganography techniques</strong>.
  </p>

  <h3> Why This is the Best Final Year Project?</h3>
  <ul>
    <li><strong>Real-World Application</strong>: Used in secure data transmission, military communication, and digital watermarking.</li>
    <li><strong>Highly Secure</strong>: Messages are encrypted using <strong>AES-256-CBC</strong> before embedding, making it nearly impossible to crack.</li>
    <li><strong>Two Interfaces</strong>: Comes with both <strong>GUI (Graphical User Interface)</strong> and <strong>CLI (Command Line Interface)</strong> for flexibility.</li>
    <li><strong>Perfect for Final Year College Projects</strong>: Well-documented, modular code, and supports full project report & explanation add-ons.</li>
    <li><strong>Unique Project Idea</strong>: Stands out among typical college projects like chat apps or CRUD systems.</li>
  </ul>

  <h3> Key Features</h3>
  <ul>
    <li><strong>AES-256 Encryption</strong>: All messages are encrypted using industry-standard AES-256 with PBKDF2 key derivation.</li>
    <li><strong>LSB Steganography</strong>: Data is hidden in the least significant bits of pixel values, making it invisible to the human eye.</li>
    <li><strong>Supports Multiple Formats</strong>: Works with PNG, JPEG, BMP, and TIFF images (PNG recommended for lossless embedding).</li>
    <li><strong>Capacity Checker</strong>: Automatically calculates how much data can be hidden in a given image.</li>
    <li><strong>PSNR Analysis</strong>: Measures image quality after steganography to ensure minimal distortion.</li>
    <li><strong>CLI & API Support</strong>: Ideal for automation, scripting, and integration into larger systems.</li>
    <li><strong>Open-Source & Extensible</strong>: Easy to modify and enhance with new features like QR embedding or multi-layer encryption.</li>
  </ul>

  <h3>Applications & Use Cases</h3>
  <ul>
    <li>Secure communication in defense and intelligence</li>
    <li>Digital watermarking for copyright protection</li>
    <li>Data hiding in cloud storage for privacy</li>
    <li>Academic research in cybersecurity and cryptography</li>
    <li>Ideal for <strong>final year projects in CSE, IT, and Cybersecurity</strong></li>
  </ul>

  <h3>How It Works</h3>
  <ol>
    <li>User inputs a secret message and password.</li>
    <li>The message is encrypted using <strong>AES-256</strong> with a randomly generated salt and IV.</li>
    <li>The encrypted data is converted to Base64 and embedded into the image using <strong>LSB technique</strong>.</li>
    <li>The output image looks identical but contains hidden encrypted data.</li>
    <li>To extract, the user provides the correct password to decrypt and retrieve the original message.</li>
  </ol>

  <h3>Project Add-Ons (Optional)</h3>
  <p>When purchasing this project, you can also opt for:</p>
  <ul>
    <li><strong>Project Setup Assistance</strong> – We help you install and run the project on your system.</li>
    <li><strong>Source Code Explanation</strong> – 1-on-1 session to explain the complete code logic and architecture.</li>
    <li><strong>Project Report & PPT</strong> – Professionally written report with diagrams, flowcharts, and presentation slides (ideal for submission).</li>
  </ul>

  <h3>Who Should Choose This Project?</h3>
  <p>This is one of the <strong>best Python projects for final year students</strong> who want to explore:</p>
  <ul>
    <li>Cryptography & Encryption</li>
    <li>Digital Image Processing</li>
    <li>Information Security</li>
    <li>Python GUI Development (Tkinter/PyQt)</li>
    <li>Real-world cybersecurity applications</li>
  </ul>

Secure Image Steganography Project using AES-256 & LSB technique. Perfect for final year college projects, cybersecurity students, and Python developers. Includes GUI, CLI, and full documentation. Only ₹99.

Image Steganography with AES-256 Encryption | Final Year Python Project | CodeAJ Marketplace

Hide secret messages securely inside images using AES-256 encryption and LSB steganography. This advanced Python-based project is perfect for final year students seeking a unique, secure, and real-world applicable project with GUI & CLI support.

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.

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