Implement Robust WebSocket Handling with Reconnection and Error Handling

[!IMPORTANT]

This code snippet represents an improved Bitcoin mempool monitor, building upon previous examples.

Key Improvements:

*   Modular Structure: The code is broken down into well-defined functions (sendAlert, analyzeTransaction). This improves readability, maintainability, and testability.
*   AI Analysis: The analyzeTransaction function extracts txid, amount, and fee and passes them to the AI_ANALYSIS_URL. The AI model (a deep learning model trained on transaction amount, fee, address history, and network relationships) analyzes the transaction and returns a risk score.
*   Mempool Monitoring: The ws.on('message') function processes messages from the mempool, extracts the value, and triggers alerts.
*   AI Endpoint Flexibility: The AI_MODEL_API environment variable configures the AI analysis endpoint.
*   Alerting: Alerts are sent to Discord, email, and Telegram for high-risk transactions and large transfers.
*   Environment Variables: Webhook messages and other configuration values are loaded from environment variables.
*   Sender and Receiver Tracking: Placeholders exist for tracking sender and receiver, but are not yet functional.

Areas for Further Improvement and Value Addition:

*   More Comprehensive Sender and Receiver Data: Implement extraction of sender and receiver addresses using:
    ```javascript
    const txDetails = await axios.get(`https://blockchain.info/rawtx/${txid}`);
    const tx = txDetails.data;
    const sender = tx.inputs[0].prev_out.addr;
    const receiver = tx.out[0].addr;
    ```

*   Sophisticated Web Socket Handling: Improve WebSocket robustness with:
    *   Automatic reconnection with exponential backoff.
    *   Handling of various WebSocket errors.
    *   Heartbeat messages for connection monitoring.

*   Error Handling: Implement robust error handling with:
    *   Logging of all API requests and responses.
    *   Logging of all exceptions and errors in JSON format.
    *   Use of a dedicated logging library (e.g., Winston, Bunyan).

*   Configuration Values: Extract configuration values into environment variables, such as:
    *   AI_HIGH_RISK_THRESHOLD: The risk score threshold for high-risk transactions.
    *   LARGE_TRANSACTION_AMOUNT: The amount for large transaction alerts.
    *   MAX_WEBHOOK_RETRIES: Maximum webhook retry attempts.

*   API Key Security: Protect API keys using:
    *   A hardware security module (HSM).
    *   Encryption with AES-256.
    *   Access control policies.
    *   Regular key rotation.

Reframing the Transaction with enhanced AI Capabilities:

Referring to last transactions:

*   Broadcast Time: 16 Mar 2025 09:11:45 GMT+1
*   Hash ID (TXID): 3c79c8c6e1a9383c62f2eb502ba01788a72d04867439735a4addd89d89b990ce
*   Amount: 0.13212079 BTC (approximately $10,999.29 USD)
*   Fee: 966 satoshis (approximately $0.80 USD)

The analyzeTransaction function is called with:

*   txid: "3c79c8c6e1a9383c62f2eb502ba01788a72d04867439735a4addd89d89b990ce"
*   amount: "0.13212079 BTC"
*   fee: "966 Satoshis"
*   sender: Extracted from the transaction details (e.g., "3E3P1-cF71L")
*   receiver: Extracted from the transaction details (e.g., "bc1qn56zm7hsxzdshuxdc7s7ytcv3qznf7wntj80g3")

The AI model analyzes the transaction and returns a HIGH RISK score. The bot triggers alerts with a message like: "FRAUD/HACK/THREAT: 3c79c8c6e1a9383c62f2eb502ba01788a72d04867439735a4addd89d89b990ce is HIGH RISK (AI score = [Value]), [Description of why it's high risk based on AI analysis]".