Total Sessions
Blocked Sessions
Block Rate
Click anywhere in this blue box multiple times to test click fraud detection:
Training Data Points: 0
Model Accuracy: 0%
Last Updated: Never
Predicted 15% increase in fraud attempts next week
Expected shift toward medium-risk traffic patterns
tc_prod_xk7m9n2p4q8r1s5t
Rate Limit: 10,000 requests/hour
tc_dev_a3b6c9d2e5f8g1h4
Rate Limit: 1,000 requests/hour
// Real-time fraud detection
const response = await fetch('/api/v1/analyze', {
method: 'POST',
headers: {
'Authorization': 'Bearer tc_prod_xk7m9n2p4q8r1s5t',
'Content-Type': 'application/json'
},
body: JSON.stringify({
ip: userIP,
userAgent: navigator.userAgent,
sessionId: sessionId,
fingerprint: deviceFingerprint
})
});
const analysis = await response.json();
if (analysis.riskScore > 80) {
// Block or challenge user
showCaptchaChallenge();
}
import requests
# Analyze visitor risk
def analyze_visitor(ip, user_agent, session_id):
response = requests.post(
'https://your-domain.com/api/v1/analyze',
headers={
'Authorization': 'Bearer tc_prod_xk7m9n2p4q8r1s5t',
'Content-Type': 'application/json'
},
json={
'ip': ip,
'userAgent': user_agent,
'sessionId': session_id
}
)
return response.json()
# Usage
result = analyze_visitor('192.168.1.1', 'Mozilla/5.0...', 'sess_123')
print(f"Risk Score: {result['riskScore']}%")
High-risk IP addresses and proxy detection
Transaction risk scoring and geolocation data
Phishing and malware detection feeds
Internal ML-generated threat indicators