HoneyMesh AI

Project Description

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.

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Screenshots

Login Portal

Corporate Dashboard

Attack Monitoring

Threat Analytics

AI Threat Report

Executive Dashboard

Customer Portal

Threat Timeline

Attack Detection

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Features

• 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

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Technology Stack

Backend

• Python
• Flask

Frontend

• HTML
• CSS
• Jinja2

Database

• SQLite

AI

• Ollama
• Llama 3

Version Control

• Git
• GitHub

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System Architecture

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

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Implementation

• 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.

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Getting Started

Clone the Repository

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

Navigate to the Project

cd HoneyMeshAI

Install Dependencies

pip install -r requirements.txt

Run the Application

python app.py

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Future Scope

• 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

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License

This project is developed for educational and cybersecurity research purposes.