BotRefund’s Signals for Detecting Automated Traffic

Direct answer

BotRefund detects automated traffic by analyzing dozens of independent signals that fall into three categories: behavioural cues (e.g., ghost clicks, honeypot traps, robotic mouse movements, lack of human‑like tremor, super‑fast input speed, grid‑aligned paths, missing clicks or scrolling, and abnormal session lengths), network clues such as suspicious ports, and timing‑synchronisation anomalies that reveal scripted interactions.

Key signals BotRefund monitors

• Ghost click detection – catches clicks that occur without a natural human intent sequence.
• Honeypot trap interactions – watches for bots that respond to hidden or deceptive page elements.
• Robotic linear mouse movements – flags unnaturally straight pointer paths.
• Absence of human‑like mouse tremor – looks for the tiny jitter typical of real users.
• Superhuman input speed (<1 ms) – identifies actions faster than a person could perform.
• Grid‑aligned movement patterns – detects movement that snaps to precise lines instead of natural curves.
• Absence of clicks or scrolling – highlights sessions that stay too static.
• Unnatural session durations – catches visits that are too short, too long, or overly uniform.
• Suspicious ports – a network check for mismatched connection details that real browsers rarely produce.
• Monitor sync anomaly – spots mismatched timing and hesitation that scripts can’t mimic.

How the signals work together

Each cue is an independent piece of evidence. BotRefund cross‑checks them against one another and feeds the combined pattern into an AI model that predicts with high accuracy whether a visit is human or automated.