Seatext library / BotRefund evidence
BotRefund’s Bot‑Traffic Detection Signals
BotRefund identifies bot traffic using a suite of behavioral and network signals, such as ghost clicks, honeypot traps, robotic mouse movements, and anomalous network ports. These signals are combined in an AI model to...
✓ Built for advertisers who need clear, refund-ready traffic evidence.
Key signals BotRefund monitors
BotRefund evaluates a range of independent checks to decide whether a visit is automated. The most prominent signals are:
- Ghost click detection – catches click activity that happens without the natural sequence of human intent.
- Trap behavior (honeypot) – watches for bots that respond to hidden or intentionally deceptive page elements.
- Pointer behavior – flags unnaturally straight mouse paths that rarely appear in real user sessions.
- Motion behavior – looks for the tiny imperfections and jitter typical of human movement, which bots lack.
- Speed behavior – identifies interactions that happen faster than a person could realistically perform (under 1 ms).
- Path behavior – detects grid‑aligned movement patterns that snap to precise lines instead of natural curves.
- Engagement behavior – highlights sessions that stay too static, showing an absence of clicks or scrolling.
- Session behavior – catches visit lengths that are too short, too long, or too uniform to be human.
- Suspicious ports – one of 106 independent checks that looks for mismatched network, location, and timing data often produced by proxy rotation or browser spoofing.
- Monitor sync anomaly – examines timing and movement inconsistencies that scripts struggle to reproduce, adding another layer of evidence.
Each signal on its own is not a verdict; BotRefund’s AI model cross‑checks them with other browser, network, and device data to reach a 99 % accurate classification.
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