HoneyMesh AI is an AI-assisted cyber deception platform that simulates a realistic corporate environment to detect, analyze, and report cyber attacks.

The platform combines enterprise honeypot concepts with real-time threat analytics and AI-powered reporting to detect both traditional cyber attacks and emerging AI-related threats.

It provides a centralized security dashboard that visualizes attack activity, calculates threat scores, predicts attacker behavior, and generates executive-level security summaries for security monitoring and incident response.

• Realistic corporate honeypot environment
• HR, Finance, Executive, and Customer portals
• SQL Injection detection
• Brute Force detection
• Cross-Site Scripting (XSS) detection
• AI Prompt Injection detection
• Customer database protection
• Real-time threat scoring
• Threat prediction
• Executive security alert dashboard
• AI-generated threat reports
• Attack timeline visualization
• Security analytics dashboard

• Python
• Flask

• HTML
• CSS
• Jinja2

• SQLite

• Ollama
• Llama 3

• Git
• GitHub

                 Internet User
                       │
                       ▼
          Corporate Web Portal
                       │
                       ▼
         Attack Detection Engine
                       │
                       ▼
              Threat Logging
                       │
                       ▼
         Threat Analytics Engine
                       │
                       ▼
          AI Report Generation
                       │
                       ▼
           Security Dashboard

• Flask handles application routing and backend logic.
• SQLite stores enterprise records, simulated data, and attack logs.
• Rule-based inspection detects SQL Injection, Brute Force, Cross-Site Scripting (XSS), and AI Prompt Injection attacks.
• All detected attacks are logged and visualized through a centralized dashboard.
• Threat analytics calculate threat scores and predict attacker behavior.
• Ollama with Llama 3 generates executive-level AI threat reports and security summaries.

git clone https://github.com/your-username/HoneyMeshAI.git

cd HoneyMeshAI

pip install -r requirements.txt

python app.py

• Machine learning-based anomaly detection
• SIEM integration
• Cloud deployment
• Distributed honeypot architecture
• Automated incident response
• Threat intelligence integration
• Role-based access control
• Email and SMS security alerts
• Docker and Kubernetes deployment

This project is developed for educational and cybersecurity research purposes.