Python Bits and Pieces with Cyber Security: Profit-Driven Wallet Detection and Blockchain Monitoring Dashboard

Architecture Overview

Data Source: Use blockchain APIs like Alchemy, Infura, or Etherscan to fetch real-time transactions.
Real-Time Monitoring: Connect to the blockchain node using web3.py or WebSocket APIs to monitor the mempool or blocks.
Transaction Analysis:
- Identify patterns of wallets making profit-driven transactions.
- Cluster wallets into groups (cabals) using techniques like graph analysis or machine learning.
Dashboard Visualization: Use tools like Dash, Plotly, or Streamlit to create a live dashboard for actionable insights.

Step-by-Step Implementation

1. Environment Setup

Install the required libraries:

pip install web3 pandas networkx dash plotly matplotlib requests

2. Monitor Real-Time Blockchain Transactions

Use web3.py to connect to an Ethereum node and monitor transactions in real-time.

Example Code to Fetch Transactions:

from web3 import Web3
import time

# Connect to an Ethereum Node (Infura or Alchemy endpoint)
infura_url = "https://mainnet.infura.io/v3/YOUR_PROJECT_ID"
web3 = Web3(Web3.HTTPProvider(infura_url))

if not web3.isConnected():
    print("Failed to connect to the Ethereum blockchain")
    exit()

print("Connected to Ethereum Blockchain")

def monitor_transactions():
    latest_block = web3.eth.blockNumber
    print(f"Monitoring transactions from block {latest_block}...")

    while True:
        try:
            new_block = web3.eth.blockNumber
            if new_block > latest_block:
                print(f"New Block Found: {new_block}")
                block = web3.eth.getBlock(new_block, full_transactions=True)

                for tx in block.transactions:
                    print(f"Tx Hash: {tx.hash.hex()} | From: {tx['from']} -> To: {tx['to']} | Value: {web3.fromWei(tx['value'], 'ether')} ETH")

                latest_block = new_block
            time.sleep(5)  # Check for new blocks every 5 seconds
        except Exception as e:
            print(f"Error: {e}")
            time.sleep(5)

monitor_transactions()

3. Identifying Profit-Driven Wallet Groups (Cabals)

To identify wallet groups that act together for profit-driven behavior:

Track Repeated Interactions: Analyze wallets that frequently interact with each other.
Profit Behavior: Monitor transactions where wallets buy low and sell high.
Graph Clustering: Use NetworkX to create a graph of wallets and transactions to find tightly connected groups.

Example Code for Wallet Clustering:

import networkx as nx
import pandas as pd

# Sample transaction data (replace with real-time data)
transactions = [
    {"from": "0xWalletA", "to": "0xWalletB", "value": 5},
    {"from": "0xWalletB", "to": "0xWalletC", "value": 2},
    {"from": "0xWalletC", "to": "0xWalletA", "value": 3},
    {"from": "0xWalletD", "to": "0xWalletE", "value": 4},
]

# Build a graph of wallet interactions
G = nx.DiGraph()

for tx in transactions:
    G.add_edge(tx["from"], tx["to"], weight=tx["value"])

# Detect tightly connected components (cabals)
cabals = list(nx.strongly_connected_components(G))

print("Identified Wallet Groups (Cabals):")
for idx, cabal in enumerate(cabals):
    print(f"Group {idx + 1}: {cabal}")

# Visualize the graph
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color="skyblue", node_size=2000, font_size=10, font_weight="bold")
nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): f"{d['weight']} ETH" for u, v, d in G.edges(data=True)})
plt.title("Wallet Interaction Graph")
plt.show()

4. Real-Time Dashboard for Insights

Use Dash and Plotly to build a real-time dashboard to visualize transactions, wallet groups, and actionable insights.

Example Code for Dashboard:

import dash
from dash import dcc, html
import plotly.graph_objs as go

# Sample wallet groups
wallet_groups = {
    "Group 1": ["0xWalletA", "0xWalletB", "0xWalletC"],
    "Group 2": ["0xWalletD", "0xWalletE"],
}

# Real-time transaction data (mocked for now)
transactions = [
    {"block": 1, "wallet": "0xWalletA", "action": "Buy", "value": 10},
    {"block": 2, "wallet": "0xWalletB", "action": "Sell", "value": 12},
    {"block": 3, "wallet": "0xWalletC", "action": "Buy", "value": 8},
]

# Initialize Dash app
app = dash.Dash(__name__)

app.layout = html.Div([
    html.H1("Blockchain Wallet Monitoring Dashboard", style={"textAlign": "center"}),

    # Wallet Groups Section
    html.Div([
        html.H3("Identified Wallet Groups (Cabals):"),
        html.Ul([html.Li(f"{group}: {', '.join(wallets)}") for group, wallets in wallet_groups.items()])
    ]),

    # Real-time Transactions
    dcc.Graph(
        id="transaction-graph",
        figure={
            "data": [
                go.Bar(
                    x=[tx["block"] for tx in transactions],
                    y=[tx["value"] for tx in transactions],
                    text=[tx["wallet"] for tx in transactions],
                    name="Transaction Value",
                )
            ],
            "layout": go.Layout(
                title="Real-Time Transactions",
                xaxis={"title": "Block Number"},
                yaxis={"title": "Transaction Value (ETH)"},
            ),
        },
    ),
])

if __name__ == "__main__":
    app.run_server(debug=True)

How It Works:

Transaction Monitoring:
- Real-time transactions are fetched using web3.py.
Wallet Analysis:
- Transactions are analyzed to identify wallets that frequently interact and form groups (cabals).
- Graph-based clustering finds tightly connected wallets.
Dashboard Insights:
- Wallet groups, transaction activity, and profits are visualized in a live dashboard.

Next Steps for Production-Ready System:

Enhance Data Source: Use WebSockets for near-instant transaction updates.
Profit Analysis: Track wallet balances using on-chain data.
Machine Learning: Use clustering algorithms like DBSCAN or K-Means to identify patterns automatically.
Live Deployment: Host the dashboard on a cloud platform like AWS, Heroku, or Azure.

Python Bits and Pieces with Cyber Security

Monday, December 16, 2024

Profit-Driven Wallet Detection and Blockchain Monitoring Dashboard