> For the complete documentation index, see [llms.txt](https://traceonai.gitbook.io/traceonai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://traceonai.gitbook.io/traceonai/getting-started/publish-your-docs/rugtrace.md).

# RugTrace

RugTrace employs decentralized machine learning for on-chain predictive analytics, aiming to proactively identify and mitigate potential threats within the blockchain ecosystem. Features include:

* **Multivariate Transaction Forensics**: Examines multiple transaction variables, including timing, amounts, and counterparties, to assess risk levels and detect suspicious activities.
* **Wallet Clustering**: Groups wallets based on behavioral patterns to identify networks of potentially malicious actors and understand their operational structures.
* **Neural Behavioral Modeling**: Predicts future actions of wallets by analyzing historical data, enabling early detection of fraudulent schemes and coordinated attacks.
* **Decentralized Machine Learning**: Leverages decentralized learning frameworks to train models across distributed data sources, enhancing privacy and model robustness.
* **Anomaly Detection**: Identifies deviations from normal transaction behaviors, flagging potential security incidents for further investigation.
* **Integration with Security Protocols**: Works in conjunction with other security measures to provide a layered defense strategy against evolving threats.