Oden's Network - README

Table of Contents

• Introduction
• Overview
• Key Features
• Installation
• Configuration
• Usage
  • Running Oden
  • Commands
• Components
  • System State Management
  • Configuration Management
  • Advanced Prometheus Monitoring
  • Logging System
  • Anomaly Detection
  • Action Execution
  • Notification System
  • Threat Intelligence
  • Oden Brain
  • Oden Policy
  • Oden Dynamics
  • Consciousness Module
  • Chain of Thought (CoT)
  • Health Check
• Development
• Contributing
• License

Introduction

Oden's Network is an advanced cybersecurity and monitoring system designed to detect, analyze, and respond to various threats in real-time. Oden leverages state-of-the-art machine learning, anomaly detection, and AI-driven decision-making to ensure the security and stability of your systems.

Overview

Oden is built with a modular architecture that includes components for monitoring system metrics, detecting anomalies, executing actions, and maintaining overall system health. It integrates with popular tools and services like Prometheus for monitoring, TensorFlow for machine learning, and various APIs for threat intelligence.

Key Features

• Real-time Monitoring: Continuous system metrics collection and analysis.
• Anomaly Detection: Advanced machine learning models to detect unusual behavior.
• Automated Response: AI-driven decision-making to execute predefined actions based on detected anomalies.
• Prometheus Integration: Export key metrics to Prometheus for visualization and alerting.
• Threat Intelligence: Integration with multiple threat intelligence sources.
• Enhanced Logging: Structured logging with alerting capabilities.
• Health Checks: Regular validation and fine-tuning of machine learning models.

Installation

To install Oden, you need to have Python 3.7+ installed on your system. Follow these steps to set up Oden:

1. Clone the repository:

   git clone https://github.com/SuperCaleb/OdensNetwork.git
   cd OdensNetwork

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Configuration

Oden's configuration is managed through the OdenConfig class. Key configuration parameters include log file paths, alert thresholds, metrics intervals, and API keys for threat intelligence sources.

You can customize the configuration by editing the OdenConfig class in the source code or by setting environment variables for sensitive information like API keys and email passwords.

Usage

Running Oden

To start Oden, simply run the main script:

python Oden

Oden will initialize its components, start monitoring, and begin processing system metrics. The Prometheus server will start on port 8000 by default.

Commands

Oden supports a few interactive commands that can be issued via standard input (stdin):

• status: Provides a detailed status update of the system.
• explain_last_action: Explains the reasoning behind the last action taken by Oden.

Components

System State Management

The SystemState class manages the overall state of the system, including active status, bad IPs, metrics history, anomaly history, and more.

Configuration Management

The OdenConfig class defines the configuration parameters for Oden. These include logging settings, metrics intervals, thresholds, and API keys for various integrations.

Advanced Prometheus Monitoring

Oden uses Prometheus to monitor key system metrics like CPU usage, memory usage, anomaly scores, and GPU utilization. The start_prometheus_server function starts a Prometheus metrics server on a separate thread.

Logging System

The OdenLogger class provides an enhanced logging system with structured logging, alert thresholds, and metrics reporting. Logs are written to a rotating file and can trigger alerts based on the volume and severity of log messages.

Anomaly Detection

Oden uses machine learning models to detect anomalies in system metrics. The OdenBrain class manages the training, prediction, and validation of these models. Anomalies are detected based on a combination of reconstruction errors, classification scores, and cluster distances.

Action Execution

Oden can execute predefined actions based on detected anomalies. Actions include terminating processes, encrypting files, transmitting alerts, deleting suspicious files, and more. The execute_action function handles the execution of these actions and updates the effectiveness of each action.

Notification System

Oden can send notifications via email and Slack. The send_notification function formats and sends alerts based on context provided by the system. Email notifications are sent using the SMTP protocol.

Threat Intelligence

Oden integrates with multiple threat intelligence sources to update its list of bad IPs. The refresh_threat_intel function periodically fetches threat intelligence data and updates the system's bad IP list.

Oden Brain

The OdenBrain class is the core of Oden's anomaly detection system. It includes methods for building and training machine learning models, predicting anomalies, and validating model performance.

Oden Policy

The OdenPolicy class implements a Dueling Deep Q-Network (DQN) with Prioritized Experience Replay to decide on actions based on the current state of the system. It uses reinforcement learning to improve its decision-making over time.

Oden Dynamics

The OdenDynamics class models the dynamics of the system by predicting future states based on current metrics and actions. It helps in estimating the impact of actions and planning proactive steps.

Consciousness Module

The ConsciousnessModule class simulates a rudimentary form of consciousness by maintaining a self-state, assessing emotional impact, and reflecting on actions. It enhances Oden's decision-making by considering the system's overall well-being.

Chain of Thought (CoT)

The OdenCoT class manages the chain of thought for Oden, assessing anomalies, selecting actions, evaluating responses, and planning proactive steps. It integrates with the Consciousness Module for enhanced decision-making.

Health Check

The health_check function periodically validates the performance of Oden's machine learning models and fine-tunes them if necessary. It ensures that the models remain accurate and effective over time.

Development

To contribute to Oden's development, follow these steps:

1. Fork the repository and clone your fork.
2. Create a new branch for your feature or bugfix.
3. Implement your changes and write tests if applicable.
4. Commit your changes and push your branch to your fork.
5. Create a pull request to the main repository.

Contributing

We welcome contributions from the community! Please read our CONTRIBUTING.md file for guidelines on how to contribute to Oden.

License

Oden is licensed under the MIT License. See the LICENSE file for more details.

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Thank you for using Oden's Network! For any questions or support, please contact us at [email protected].

EyeOfOden AI Vision System

Overview

EyeOfOden is an advanced computer vision system designed to provide real-time object detection, tracking, and scene analysis. It utilizes the YOLOv8-X model for high-accuracy object detection and the DeepSORT algorithm for object tracking. Additionally, it offers text-to-speech capabilities to announce detected objects and actions within a scene.

Features

• Real-time Object Detection: Uses YOLOv8-X for detecting various objects with high accuracy.
• Object Tracking: Employs DeepSORT for tracking objects across frames.
• Text-to-Speech Announcements: Announces detected objects and their actions using a text-to-speech engine.
• Scene Summarization: Provides periodic summaries of the scene, including object counts and notable actions.

Requirements

• Python 3.7+
• OpenCV
• PyTorch
• NumPy
• pyttsx3
• ultralytics (YOLO)
• deep_sort_realtime

Installation

1. Clone the repository:

   git clone https://github.com/SuperCaleb/OdensNetwork.git
   cd OdensNetwork

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the EyeOfOden AI Vision System:

   python "Oden's Advanced Computer Vison Program"

2. Functionality:

   • Object Detection: The program captures frames from the webcam and detects objects using the YOLOv8-X model.
   • Object Tracking: Detected objects are tracked across frames using DeepSORT.
   • Text-to-Speech Announcements: The program announces new objects entering the scene, fast-moving objects, and provides periodic summaries of the scene.
   • Scene Analysis: Determines object locations, distances, movements, and actions.

3. Controls:

   • Press q to exit the program.

How It Works

1. Initialization:

   • The YOLOv8-X model is loaded for object detection.
   • The DeepSORT tracker is initialized for tracking objects.
   • The webcam is accessed for capturing video frames.
   • A text-to-speech engine is set up to run in a separate thread for non-blocking speech synthesis.

2. Main Loop:

   • Frames are captured from the webcam.
   • Objects are detected in each frame using the YOLOv8-X model.
   • Detected objects are tracked across frames using DeepSORT.
   • The program determines the location, distance, movement, and action of each tracked object.
   • Text-to-speech announcements are made for new objects and notable events.
   • A summary of the scene is generated and announced periodically.
   • The processed frames are displayed in a window.

3. Cleanup:

   • The webcam is released, and the OpenCV window is closed.
   • The text-to-speech thread is terminated gracefully.

Example Output

The program provides real-time output through the OpenCV window and text-to-speech announcements. Example announcements include:

• "A new person has entered the scene on the left."
• "A fast car is moving fast right on the center."
• "Currently, there are 2 persons, 1 car, and 1 bicycle. The closest person is walking on the right."

Troubleshooting

• Failed to open webcam: Ensure your webcam is connected and accessible.
• Missing dependencies: Make sure all required packages are installed correctly.
• Other errors: Check the console output for error messages and troubleshoot accordingly.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss your ideas.

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