We propose Cardano VigilADA, a three-layered machine learning (ML) framework designed to enhance transparency and security within the Cardano network:

Layer 1: Real-Time Data Aggregation and Anomaly Detection

• Utilize ML algorithms such as Random Forest, XGBoost, K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Stochastic Gradient Descent (SGD) to analyze transaction data in real-time.
• Aggregate public data from Cardano blockchain explorers to extract behavioral features and score transactions and addresses for anomaly risk.
• Issue real-time alerts for suspicious activities, providing immediate insights into potential threats.

Layer 2: Model Context Protocol (MCP) Server Integration

• Develop an MCP server to expose analytics and insights, enabling seamless interoperability with agentic frameworks such as ElizaOS.
• Allow AI agents to access real-time transaction data, fraud alerts, and behavioral metrics directly, facilitating autonomous monitoring and reporting.

Layer 3: Agentic Framework Tooling

• Integrate with agentic frameworks like ElizaOS to enable AI agents to perform comprehensive analyses, including: -- Tracking and analyzing specific issues (e.g., fraud detection) based on user prompts. -- Providing reasoning paths that led to conclusions. -- Citing data sources and links used in the analysis, ensuring transparency and reproducibility.

Cardano VigilADA aims to fill a critical gap in the Cardano ecosystem by providing a comprehensive, real-time anomaly detection framework. Through the integration of advanced ML techniques and agentic frameworks, it offers a scalable and transparent solution to enhance the network’s security and trustworthiness.

The Cardano ecosystem currently lacks a robust, real-time system capable of automatically detecting anomalous or fraudulent activities on-chain. This absence creates blind spots for exchanges, dApps, auditors, and regulators, undermining trust and impeding broader adoption.

Cardano VigilADA provides continuous AI transaction monitoring, detecting anomalies and fraud, enhancing Cardano's security, transparency, and trust. Additionally, it opens real-time data through an MCP server, allowing LLMs to consume and query data and derive analytical insights.

An ML‑powered transaction monitoring layer will:

• Increase security by flagging suspicious patterns within seconds of block confirmation.
• Improve transparency for compliance teams through rich analytics and alert feeds.
• Enhance user and developer confidence via public dashboards and open APIs.

Develop a real-time, ML-powered system to monitor Cardano transactions, detect potential fraud/anomalies, provide alerts, and offer insights into on-chain activity, enhancing ecosystem security, transparency, and visibility.

Deliverables

• A real-time ML-driven backend capable of ingesting full Cardano ledger data and detecting anomalies.
• A lightweight web UI and API for visual analytics and alert management.
• An MCP server exposing analytics for integration with agentic frameworks.
• Tooling and documentation for integrating with ElizaOS and similar frameworks.
• State-of-the-art research and specifications supporting the framework’s development.

Technical Feasibility Our approach leverages proven ML techniques for fraud detection and anomaly analysis, ensuring a high degree of accuracy and efficiency. The integration with MCP servers and agentic frameworks like ElizaOS is facilitated by existing plugins and protocols, streamlining development and deployment.

Community Impact By providing real-time insights into on-chain activities, Cardano Vigilada enhances transparency and trust within the ecosystem. It empowers stakeholders—including developers, auditors, and regulators—with tools to detect and respond to anomalies promptly. Furthermore, the integration with agentic frameworks fosters community engagement by enabling AI-driven analyses and reporting.

Phase 1 (Month 1-4): Data aggregation and initial ML model development.

Phase 2 (Month 5-9): MCP server development and integration with agentic frameworks.

Phase 3 (Month 10-12): Web UI development, testing, and deployment.

Total Budget: 1,456,000 USD, covering development, testing, and deployment costs.

Regular ML model updates, system monitoring, and a dedicated support team ensuring ongoing accuracy and reliability. Continuous maintenance of the MCP server to ensure data availability and integration.

The project's source code and accompanying materials will be hosted openly on GitHub for public access. To ensure the initiative's longevity, the development group will actively seek persistent financial resources via Catalyst frameworks, Intersect's specialized funding initiatives, and alternative monetary support avenues.