Плагины/[MAX] ML Anti XRay
[MAX] ML Anti XRay

[MAX] ML Anti XRay

Automatically flag potential xrayers with Machine Learning

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AI Anti-Xray Detection Plugin

This plugin is designed to help Minecraft server administrators detect and prevent x-ray cheating using a simple AI model. It monitors players' ore mining behavior and collects various data points to determine if a player is likely using x-ray mods.


Key Features

  • AI-Driven Detection:
    The plugin uses two sets of model weights (for legit and x-ray behavior) to compute a confidence score indicating the likelihood that a player is x-raying.

  • Comprehensive Data Collection:

    • Distance Calculation: Tracks the distance from the last mined ore.
    • Exposure Analysis: Determines whether an ore was naturally visible or revealed due to nearby block breaking.
    • Ore Vein Grouping: Data is only recorded once per ore vein to avoid duplicate counts.
    • Mining History: Maintains a rolling history of ore mining events (default: 10 minutes).
    • Rotation from last ore: Checks how big of a rotation you'd have to do in order to reach an ore from the last one.
  • Dynamic Training System:
    Use the /train <legit|xray> command to adjust the AI model weights based on player behavior. This allows the model to learn and improve over time:

    • Legit Training: Adjusts the model if a player is confidently a legitimate miner.
    • Xray Training: Adjusts the model when a player is detected as using x-ray mods.
  • Customizable Configuration:
    Configure the plugin via config.yml to set:

    • Flag threshold (confidence percentage).
    • Flag messages.
    • List of ores to monitor.
    • Mining time window for data collection.
    • And more...
  • Automatic File Management:
    The plugin automatically creates necessary folders and files (e.g., ML/legit.dat, ML/xray.dat) if they do not exist, ensuring smooth first-time setup.


How It Works

  1. Data Collection:

    • When a player breaks a block, the plugin checks if it's an ore of interest.
    • If a non-ore block is broken, the plugin scans adjacent blocks for ores and marks them as "revealed."
    • When an ore is mined, the plugin checks if it was naturally exposed (i.e., visible without needing to break any adjacent block) before recording the event.
  2. Feature Extraction:
    The plugin calculates:

    • Player rotation difference to the ore.
    • Distance from the previous ore break.
    • Exposure status (using the revealed ore list).
    • Count of similar ores mined within the configured time window.
  3. AI Prediction:
    The collected features are processed through a weighted model, and a confidence score is computed that indicates the likelihood of x-ray usage.

  4. Training & Adaptation:
    The /train command allows administrators or trusted players to train the model based on observed behavior, updating the weights stored in ML/legit.dat or ML/xray.dat.

  5. Flagging Suspicious Behavior:
    If the confidence score exceeds the configured threshold, the plugin flags the player which then notifies staff of their potentially unfair behaviour.


Usage

  • Installation:
    Place the plugin jar into your server's plugins folder. On first run, the necessary configuration and ML data files will be created automatically.

  • Configuration:
    Edit config.yml in the plugin’s data folder to customize the detection thresholds, messages, and ore list.

  • Commands:

    • /train <legit|xray>
      Use this command to train the AI model based on a player's recent mining behavior.

Conclusion

This AI Anti-Xray Detection Plugin leverages in-game data to help maintain a fair play environment on your server by dynamically learning and adapting to player behavior. Enjoy a smarter, self-improving system for combatting x-ray cheats!

Feel free to reach out with feedback or suggestions to improve the plugin further. Happy mining!

https://bstats.org/plugin/bukkit/MLAntiXray/25104

Совместимость

Minecraft: Java Edition

1.20–1.21.81.18–1.19.41.17–1.17.1

Создатели

bbb908

bbb908

Владелец

Детали

Лицензия:Apache-2.0
Опубликован:9 месяцев назад
Обновлён:4 месяца назад