What is the difference between network-level filtering and client-side detection for stopping bot clicks?

Direct Answer: Network-level filtering like IP blocklists can be easily bypassed by modern bots using rotating residential proxies. Client-side detection, like BotRefund's, inspects the visitor's device and interaction dynamics—such as mouse acceleration, touch pressure, and headless browser attributes—which are much harder for bots to...

Direct Answer: Rule based systems often flag a visit as bot based on a single tell like straight line mouse movement. BotRefund uses an AI prediction model that weighs 106 independent checks including mouse path browser APIs network signals and device fingerprints. The model...

Direct Answer: Rule-based detection trusts a raw signal by itself, while AI-based detection weighs the complete pattern. BotRefund sends all 106 signals into a prediction AI that evaluates browser, network, device, and behavior evidence together. This approach is what supports the 99 percent accuracy...

Direct Answer: This category includes checks designed to catch bots that try to evade detection or hide automation. The Iframe Webdriver Leak check is one example used by BotRefund. It looks for API mismatches that automated browsers produce when their patches break under inspection....

Direct Answer: It is used to spot automated browsers that try to behave like people reading a page. A real visitor shows pauses and decision-making while scrolling, while a scraper often sends interactions more quickly and uniformly. BotRefund treats this mismatch as one objective...

Direct Answer: The page recommends preserving attribution first, then comparing Meta reporting with web analytics, server logs, call records, email verification, and CRM stages before you touch targeting or budgets. Keep campaign, ad set, creative, placement, click identifier, landing-page URL, and timestamp with each...

Direct Answer: Start by excluding high-risk display placements, narrowing your geographic targeting to people physically in your service area, and adding hidden honeypot fields to your forms. Then install a real-time behavioral firewall like BotRefund to block bots before they trigger your pixels. These...

Direct Answer: The check compares the operating‑system identifier with the set of fonts reported by the browser. Spoofed or virtual browsers often present a mismatch because they cannot perfectly emulate a real device’s font library. When a mismatch is found it becomes a strong...

Direct Answer: The free Starter tier allows up to three hundred bot detections per month. It includes the full verification dashboard and self‑service refund report generation. If you exceed the limit, you can upgrade to a paid plan for unlimited coverage.

Direct Answer: The Headless UA Evader check looks for mismatches that a real browsing session does not normally create, such as tool-specific shortcuts or automation remnants. BotRefund uses this as one of 106 independent checks to build a reliable picture of whether a visit...

Direct Answer: The Keyboard Layout Leak check looks for a mismatch that a real browsing session does not normally create. Virtual machines and spoofed profiles can claim one device while their graphics, fonts, audio, or processor behavior tells another story. This signal adds objective...

Direct Answer: It is looking for a mismatch between how a normal browser runs standard APIs and what an automated browser reveals. Automation tools often patch browser APIs to hide themselves, but those patches can break when checked from another angle. BotRefund uses this...

Direct Answer: The Mobile Menu Probe is one of the 106 independent checks BotRefund uses to spot automated browsing. It looks for a mismatch that a real browsing session does not normally create. Scripts can send clicks and scrolls, but they struggle to reproduce...

Direct Answer: The Permission Lie check looks for a mismatch in browser APIs that a real browsing session does not normally create. Automated browsers often patch or hide APIs, but those changes can break when inspected from another angle. BotRefund uses it as one...

Direct Answer: It is one of 106 independent checks BotRefund uses to spot automated browsers. It looks for a mismatch that a normal browser session does not create, often caused by automation tools patching or hiding APIs. BotRefund treats this as evidence and cross-checks...

Direct Answer: AI models weigh the complete pattern of all detection signals instead of following rigid raw rules, which reduces false positives from normal user variation. BotRefund's AI evaluates how its 106 independent checks align across browser, network, device, and behavior data to make...

Direct Answer: AI prediction allows the system to evaluate the full constellation of signals rather than relying on a single rule. BotRefund model takes all 106 independent checks and determines the likelihood that a visit is automated. This approach adapts to new evasion techniques...

Direct Answer: Corroboration means several independent signals point to the same conclusion instead of one tell being enough. BotRefund uses 106 independent checks and evaluates them with an AI model. The model weighs the complete pattern across browser, network, device, and behavior data. That...

Direct Answer: Independent evidence adds objective facts about a visit. BotRefund collects 106 such facts, including the Error Stack Proxy check. Each fact is cross-checked against others. This corroboration is what allows the tool to reach 99% accuracy instead of relying on a single...

Direct Answer: BotRefund reports an 83% refund approval success rate. This high rate is achieved by building evidence cases that meet the platforms' specific requirements. They have recovered millions across hundreds of enterprise accounts. Their experience negotiating with Google and Meta helps escalate claims...