How to Build a Real-Time Ad Fraud Dashboard with Python and WebSocket

Monitor your ad traffic quality in real-time. Here's a complete implementation using Python, WebSocket, and a simple frontend. Architecture Ad Traffic → Collector → Analysis Engine → WebSocket Server → Dashboard ↓ Alert System Backend (Python + FastAPI) from fastapi import FastAPI, WebSocket import asyncio import json app = FastAPI() connected_clients = set() class TrafficAnalyzer: def __init__(self): self.stats = { 'total_visits': 0, 'bot_detected': 0, 'human_verified': 0, 'suspicious': 0 } def analyze(self, visit): self.stats['total_visits'] += 1 # Three-layer check ip_score = self.check_ip(visit['ip']) fp_score = self.check_fingerprint(visit['fingerprint']) behavior_score = self.check_behavior(visit['mouse_data']) combined = (ip_score + fp_score + behavior_score) / 3 if combined > 70: self.stats['human_verified'] += 1 verdict = 'human' elif combined > 40: self.stats['suspicious'] += 1 verdict = 'suspicious' else: self.stats['bot_detected'] += 1 verdict = 'bot' return { 'verdic