BotRefund's Bot Detection Approach: What the Data Shows

How BotRefund detects bots

BotRefund builds a bot-or-human verdict from 106 independent checks across browser, network, device, and behavior layers. Each check contributes one piece of evidence; the final decision comes from an AI model that weighs the full pattern instead of trusting any single rule.

Behavioral signals (client-side)

• Ghost click detection — catches clicks that occur without the natural sequence of human intent (no prior hover, scroll, or read time).
• Honeypot trap interactions — watches for bots that click hidden or intentionally deceptive page elements real users never see.
• Pointer behavior — flags robotic linear mouse movements and grid-aligned paths that snap to precise lines instead of natural curves.
• Motion behavior — looks for the absence of humanlike mouse tremor (the tiny imperfections and jitter typical of real movement).
• Speed behavior — identifies superhuman input speeds (<1 ms) faster than a person can realistically perform.
• Engagement behavior — highlights sessions with no clicks or scrolling, staying too static to match a real browsing journey.
• Session behavior — catches unnatural session durations that are too short, too long, or too uniform to be human.

Technical & network signals (server-side)

• Suspicious Ports — detects mismatches between connection, location, language, and timing that proxy rotation, location masking, or browser spoofing create.
• Monitor Sync Anomaly — checks for timing and movement mismatches between rendered frames and input events that scripts struggle to reproduce.

Decision logic

Every signal is kept as evidence, not a verdict. BotRefund cross-checks each anomaly against independent browser, network, device, and behavior data, then feeds the complete pattern into its prediction AI. The company states this corroboration approach yields 99% accuracy.

What a comparison with ClickCease would require

The supplied source pack contains only BotRefund documentation. To compare fairly you would need ClickCease's equivalent signal list, its evidence-combination method (rule-based vs. AI-weighted), its refund/recovery process with ad platforms, setup time, and any independent accuracy benchmarks. None of that data is present here.

Next step if you're evaluating BotRefund

1. Run the free bot audit — add the BotRefund script (≈1 minute, no credit card) to see your site's actual bot traffic breakdown.
2. Review the audit's signal-by-signal report to verify which of the 106 checks are firing on your traffic.
3. If bot volume justifies it, engage the refund workflow: BotRefund compiles evidence, files disputes with Google and Meta, and pursues recovery back to 2017.

Verification: After the audit, confirm that the dashboard shows non-zero counts across multiple behavioral categories (ghost clicks, honeypot hits, pointer anomalies) — not just a single rule — before committing to a paid plan.