Welcome to the PressureFund repository, a hub for upcoming projects by AquaTech, a new and innovative software development company. AquaTech is currently in the early stages of development, but we are constantly generating ideas and working on cutting-edge technology for the AI community and Linux kernel development.

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AquaTech is a forward-thinking software development company founded by a team of passionate developers. Our mission is to create impactful software solutions, focusing on AI-based technologies and contributing to the Linux kernel. We believe in open collaboration and are eager to work with other developers and organizations to drive innovation in these fields.

PacketWorx is a Python-based AI assistant for Wireshark, designed to read, organize, and analyze network packet captures (PCAP files). It leverages pyshark for packet capture processing, pandas for data organization, and advanced machine learning techniques for packet classification and anomaly detection.

• Reads and parses PCAP files and live captures using pyshark.
• Organizes packet information in a structured format using pandas.
• Implements advanced AI technology to classify packets based on their characteristics.
• Utilizes Gradient Boosting Classifier for packet classification.
• Detects anomalies in network traffic using Isolation Forest.
• Suggests filters for Wireshark based on packet analysis.
• Highlights suspicious and anomalous packets in the capture.

To install the required dependencies, run:

pip install pyshark pandas scikit-learn joblib

1. Ensure you have a PCAP file (e.g., example.pcap) that you want to analyze or specify a network interface for live capture.

2. Create a Python script (e.g., packetworx.py) with the provided content.

3. Replace 'example.pcap' with the path to your actual PCAP file or specify a network interface for live capture.

4. Run the script with appropriate options:

# Analyze a pcap file
python packetworx.py --pcap example.pcap

# Analyze live capture from a network interface
python packetworx.py --interface eth0

# Suggest a filter for Wireshark
python packetworx.py --filter

# Highlight suspicious packets
python packetworx.py --highlight

# Highlight anomalous packets
python packetworx.py --anomalies

• Reading PCAP Files or Live Captures: The read_pcap function uses pyshark to read and parse the PCAP file or live capture, extracting relevant packet information.
• Organizing Data: The extracted packet information is stored in a pandas DataFrame for easy manipulation and analysis.
• Training the Model: If a model does not already exist, the script trains a Gradient Boosting Classifier on the packet data using enhanced features and dummy labels.
• Classifying Packets: The trained model is used to classify packets, and the results are added to the DataFrame.
• Anomaly Detection: The script detects anomalies in the network traffic using Isolation Forest and adds the results to the DataFrame.
• Suggesting Filters: The suggest_filter method provides filter suggestions based on the packet analysis.
• Highlighting Suspicious and Anomalous Packets: The highlight_suspicious_packets and highlight_anomalous_packets methods print packets classified as suspicious or anomalous.

AquaCoinAI aims to revolutionize ERC20 governance securities by creating a dual governance system combining community-based and AI-based governance. This innovative system is designed to enhance the security and reliability of the ERC20 blockchain ecosystem and support future cryptocurrency projects.

• Community-Based Governance (50%):

  • Shareholders are given the ultimate authority to vote on implementations and decisions within the ecosystem.
  • Each shareholder gets one vote per proposal to ensure fairness and equal representation.
  • Proposals for new features, improvements, and changes can be submitted by any community member and will be voted on by the shareholders.

• AI-Based Governance (50%):

  • An advanced AI system acts as a guardian and innovator for the ecosystem, continuously analyzing trends, potential threats, and opportunities for improvement.
  • The AI system generates ideas and recommendations for enhancing the ecosystem's functionality, security, and innovation.
  • While the AI can suggest and develop detailed proposals, it does not have the authority to implement changes. All AI-generated ideas must be reviewed and approved by the community through the voting process.

• Proposal Submission: Community members can submit proposals for changes or new features to the ecosystem.

• Review Period: Proposals are reviewed and discussed within the community for a set period, allowing for feedback and modifications.

• Voting Process:

  • Each shareholder is entitled to one vote per proposal to maintain an equitable and honest governance system.
  • Votes are cast during a designated voting period, ensuring transparency and community engagement.
  • A proposal is approved if it receives a majority of votes from participating shareholders.

• AI Proposals: The AI governance system continuously monitors the ecosystem and external factors to generate innovative proposals. These proposals are submitted to the community for review and voting, similar to community-submitted proposals.

1. Initial Development:

   • Develop the foundational AI system and integrate it with the ERC20 governance framework.
   • Implement the community-based voting mechanism and establish guidelines for proposal submission and review.

2. Testing and Iteration:

   • Conduct thorough testing of the AI system and voting process to ensure reliability and security.
   • Gather feedback from early adopters and make necessary adjustments to the governance framework.

3. Launch:

   • Officially launch the AquaCoinAI governance system, inviting community participation and collaboration.
   • Promote the system to existing and new ERC20 projects, highlighting the benefits of dual governance.

4. Continuous Improvement:

   • Continuously enhance the AI system to improve its proposal generation capabilities.
   • Foster a vibrant community of contributors and voters to ensure the ecosystem remains dynamic and innovative.

We welcome contributions from developers, researchers, and enthusiasts who share our vision. If you are interested in collaborating with us, please follow these steps:

1. Fork the Repository: Click the "Fork" button at the top of this page to create a copy of this repository on your GitHub account.
2. Clone the Repository: Clone your forked repository to your local machine using git clone <your-forked-repo-url>.
3. Create a Branch: Create a new branch for your feature or bugfix using git checkout -b <branch-name>.
4. Make Changes: Develop your feature or fix the bug in your branch.
5. Commit Changes: Commit your changes with a clear and concise message using git commit -m "Description of changes".
6. Push Changes: Push your changes to your forked repository using git push origin <branch-name>.
7. Create a Pull Request: Open a pull request to the main repository, detailing the changes you have made.

We will review your pull request and provide feedback. Thank you for your contributions!

For any inquiries, suggestions, or collaborations, feel free to reach out to us:

• Email: [email protected]
• Email: [email protected]
• Email: [email protected]
• GitHub Issues: PressureFund Issues

We look forward to working with you and making impactful contributions to the tech community.

Thank you for visiting the PressureFund repository. Stay tuned for exciting developments and updates from AquaTech!