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Glossary

Click Fraud Protection Glossary

25 terms across 6 categories. Definitions of every core concept in click fraud, bot traffic, PPC ad networks, detection techniques and KVKK/GDPR compliance.

Click Fraud

Click Fraud

Also known as: Fake click, Fraudulent click, PPC fraud, Invalid click

A click on an ad with no genuine buyer intent — produced by bots, automation or organized human groups to drain an advertiser's budget or harm a competitor.
Click fraud is the abuse of the pay-per-click (PPC) advertising model. The click is not triggered by a real user with purchase intent but by a competitor, a bot, a click farm, a rogue publisher or a budget-draining attack. Modern click fraud requires far more sophisticated detection than legacy IP repetition: ASN, datacenter signals, behavioral telemetry, device fingerprinting and session quality.
Example: If a competitor clicks your ad 50 times a day and burns through your TRY 10,000 daily Google Ads budget in 9 hours — that is a classic click fraud attack.
Related terms:Invalid Traffic (IVT)Click FarmCompetitor Click FraudBot Traffic

Invalid Traffic (IVT)

Also known as: Invalid traffic, Invalid click activity, IVT

The umbrella category Google and other ad networks use to describe clicks or impressions assumed to come from bots, errors or manipulation rather than genuine intent.
Invalid Traffic (IVT) splits into General Invalid Traffic (GIVT) — overt bot traffic, automation identified through headers, known datacenter traffic — and Sophisticated Invalid Traffic (SIVT) — hidden bots, residential proxy human-like traffic, click farms and coordinated attacks. Standardized by the IAB and the MRC, IVT has become the common language for refund disputes and fraud discussions.
Related terms:Click FraudGIVT (General Invalid Traffic)SIVT (Sophisticated Invalid Traffic)Invalid Click Refund

GIVT (General Invalid Traffic)

Also known as: General invalid traffic

Easily detected invalid traffic — known bots, datacenter IPs, automated browsers.
General Invalid Traffic (GIVT), per the IAB/MRC definition, is invalid traffic that can be identified directly through standard filter lists (e.g. the IAB International Spiders & Bots List), datacenter IP databases or known automation signatures. Google Ads automatic filters catch the majority of GIVT, which is why refund claims based on GIVT alone are rarely approved.
Related terms:Invalid Traffic (IVT)SIVT (Sophisticated Invalid Traffic)

SIVT (Sophisticated Invalid Traffic)

Also known as: Sophisticated invalid traffic

Hidden invalid traffic only catchable via behavioral analysis — residential proxy bots, headless browsers, click farms.
Sophisticated Invalid Traffic (SIVT) is traffic that bypasses classic filter lists and successfully mimics human behavior. Residential proxies (botnets routed through real home IPs), fingerprint spoofing in headless browsers, click farms and coordinated competitor attacks are typical SIVT examples. SIVT detection requires combining device fingerprinting, behavioral signals (mouse movement, scroll, timing) and session context (return rate, conversion, dwell time).
Related terms:Invalid Traffic (IVT)GIVT (General Invalid Traffic)Residential ProxyHeadless Browser

Click Farm

Also known as: Manual click fraud operation

Operations where real low-paid workers click ads on command — hard to distinguish from genuine traffic because the people are real.
Click farms are operations, often in South Asia, where hundreds of low-paid workers click ads from real devices. They evade classic bot detection because behavior, device and network are all human in origin. Detection relies on behavioral clustering signals such as device consistency (does the same device hit thousands of unrelated domains?), session conversion rate (is there ever a meaningful post-click action?) and timing patterns (no resistance, mechanical click cadence).
Related terms:Click FraudSIVT (Sophisticated Invalid Traffic)Competitor Click Fraud

Competitor Click Fraud

Also known as: Competitor click attack, Adversarial clicks

A competitor deliberately clicking your ad over and over to burn your budget or distort your bid ranking.
Competitor click fraud is widespread in B2B SaaS and high-CPC verticals (legal, insurance, healthcare, local services). Signals used to detect it include repeat clicks from the same IP/device, sudden CTR spikes on category-specific keywords, sharp drops in conversion rate, near-zero return-visit duration and concentrated traffic from the same /24 subnet. wall.click pushes those IPs into the Google Ads exclusion list and archives the evidence for unfair-competition claims.
Related terms:Click FraudIP Exclusion

Bots & Automation

Bot Traffic

Also known as: Automation traffic, Non-human traffic

Web traffic generated by scripts or software rather than humans. It splits into good (search bots) and bad (attacks, scrapers, fraud).
Bot traffic accounts for roughly 30-50% of internet traffic (Imperva Bad Bot Report). Good bots: Googlebot, Bingbot, monitoring tools, social link previewers. Bad bots: scrapers, credential stuffing, click bots, scalpers, DDoS. In the PPC context the most critical category is click bots programmed to click ads — they run in headless browsers, execute JS and mimic mouse movement.
Related terms:Headless BrowserClick FraudGIVT (General Invalid Traffic)

Headless Browser

Also known as: Headless Chrome, Puppeteer

A browser driven by code with no visual UI — runs JS like real Chrome/Firefox and bypasses classic filters.
A headless browser is a fully featured browser automated through Puppeteer, Playwright or Selenium with no visible window. Modern fraud operations rely heavily on headless Chrome because it executes JS like a real user, stores cookies and produces a fingerprint. Detection relies on WebDriver navigator properties, fingerprint inconsistencies (timezone vs IP geography), missing plugin signatures and unnatural mouse trajectories.
Related terms:Bot TrafficDevice FingerprintSIVT (Sophisticated Invalid Traffic)

Network & Infrastructure

Residential Proxy

Also known as: Home IP proxy

A proxy network that lets a bot or attacker exit the internet through real home IPs — bypassing datacenter filters.
Residential proxy networks route traffic through real home devices, often with or without their consent. The attacker appears to come from a real home IP in any country, neutralizing datacenter ASN filters and IP reputation lists. Detection requires behavioral anomaly analysis (a real user normally has one browser session per site, while bots over residential proxies fan out across dozens of sessions in minutes), TLS fingerprinting and fingerprint inconsistencies.
Related terms:ASN (Autonomous System Number)Datacenter IPSIVT (Sophisticated Invalid Traffic)

Datacenter IP

Also known as: Hosting IP

IP blocks belonging to cloud providers like AWS, Google Cloud, DigitalOcean or OVH — real end users almost never click ads from these, so they are nearly always a bot signal.
Datacenter IPs are public IP blocks owned by hosting providers and indicate that traffic originates from a server rather than from an end user's browser session. They are identified via ASN (Autonomous System Number) lookups. It is virtually impossible for a real user to click an ad from a datacenter IP, which is why datacenter traffic is the first filter layer of any click fraud protection tool.
Related terms:ASN (Autonomous System Number)Residential ProxyGIVT (General Invalid Traffic)

ASN (Autonomous System Number)

Also known as: Autonomous System Number

The number that identifies the organization (ISP, hosting provider, university, etc.) responsible for an IP block — a critical signal for fraud detection.
An ASN is the unique number assigned to each organization that runs IP routing through BGP on the public internet. Examples: AS15169 = Google, AS16509 = Amazon AWS, AS14061 = DigitalOcean. Knowing an IP's ASN tells you whether it belongs to a consumer ISP (likely a real end user) or to a hosting provider (likely a bot). The wall.click decision engine queries ASN data for every click and scores traffic from risky ASNs accordingly.
Related terms:Datacenter IPResidential Proxy

Ad Network

Invalid Click Refund

Also known as: Click refund, Invalid click activity form

The official Google Ads process for requesting that clicks you consider invalid be credited back to your invoice.
Google Ads automated filters detect a baseline volume of invalid clicks each month and refund them as automatic credits. SIVT (sophisticated invalid traffic) often slips past those filters; in that case advertisers can file a manual refund request via the "Invalid Click Activity" form. A successful claim requires a structured report including date range, IP list, conversion anomaly and behavioral evidence. wall.click generates this report automatically in Google's expected format.
Related terms:SIVT (Sophisticated Invalid Traffic)Invalid Traffic (IVT)Click Fraud

PPC (Pay-Per-Click)

Also known as: Pay-per-click

An ad pricing model in which the advertiser pays only when their ad is clicked, not when it is shown.
Pay-per-click (PPC) is the model where advertisers pay per click, not per impression. Google Ads, Microsoft Ads, Meta Ads and TikTok Ads are the main providers. Click fraud attacks the economics of PPC directly: every fake click is billed like a real one and deducted from the advertiser's budget.
Related terms:Click FraudGoogle Ads

CTR (Click-Through Rate)

Also known as: Click-through rate

The share of users who click after seeing your ad. Under click fraud, CTR must always be read alongside conversion rate.
CTR = (clicks / impressions) × 100. A high CTR is normally interpreted as a sign of strong creative; under click fraud, CTR rises artificially. A rising CTR coupled with a flat or falling conversion rate is one of the earliest and strongest signals of click fraud.
Related terms:PPC (Pay-Per-Click)Click FraudConversion Rate

Conversion Rate

Also known as: Conversion rate, CR

The share of clickers that complete the goal action (purchase, form fill, etc.). Bot traffic erodes this metric.
Conversion Rate = (conversions / clicks) × 100. The fundamental ROI signal wall.click measures is how conversion rate improves after fraud clicks are removed. As fraud is stripped, visible ROAS (Return on Ad Spend) and CR rise structurally because the denominator (total clicks) shrinks while the numerator (real conversions) holds.
Related terms:CTR (Click-Through Rate)PPC (Pay-Per-Click)Click Fraud

Smart Bidding

Also known as: Automated bidding, Google Ads ML bidding

Google Ads' machine-learning-driven automated bidding strategies. Click fraud signals contaminate the Smart Bidding model.
Smart Bidding covers automated bid strategies such as Target CPA, Target ROAS and Maximize Conversions. These strategies look at conversion history to predict which click is valuable. Click fraud traffic pollutes the model because it learns bot clicks as part of a "normal traffic distribution". When wall.click strips out fraud, Smart Bidding is trained on cleaner data and the cost of acquiring real customers drops.
Related terms:Google AdsPPC (Pay-Per-Click)Conversion Rate

MCC (Manager Account)

Also known as: Manager Account, My Client Center

Google Ads' top-level hierarchy that lets an agency or holding manage multiple advertiser accounts from a single panel.
MCC (Manager Account, previously My Client Center) is the hierarchy that lets one user manage multiple Google Ads client accounts. It is critical for marketing agencies and large holdings. wall.click supports MCC linking — a single wall.click account can build separate protection rules and reporting for every client account under the MCC.
Related terms:Google AdsPPC (Pay-Per-Click)

Detection & Protection

Device Fingerprint

Also known as: Browser fingerprint

A technique that recognizes the same device across sessions by computing a unique combination of browser and device attributes (User-Agent, resolution, fonts, canvas hash, audio context, etc.).
Device fingerprinting is the practice of recognizing a device without cookies or logins. The computed signals include User-Agent, screen resolution, installed font list, WebGL renderer, canvas hash, audio fingerprint, timezone, language and plugin list. The resulting combination achieves more than 99% uniqueness. In fraud detection, the same fingerprint clicking ads from different IPs and different domains is a strong bot/click-farm signal.
Related terms:Headless BrowserSIVT (Sophisticated Invalid Traffic)Behavioral Signal

Behavioral Signal

Also known as: Behavior signal, Mouse / scroll telemetry

A visitor's interaction pattern on the site: mouse trajectory, scroll speed, click timing, keyboard events — one of the strongest modern signals separating bot from human.
Behavioral signals are used to estimate whether a session is human or bot. Real humans show non-linear mouse trajectories, accelerating/decelerating scroll, several seconds of dwell time and natural keyboard delays. Bots (especially headless) show pixel-perfect straight mouse moves, instant mechanical clicks, zero scroll and zero keyboard events. Even modern click farms get caught through human-clustering patterns where thousands of devices share near-identical behavior signatures across unrelated sites.
Related terms:Device FingerprintHeadless Browser

IP Exclusion

Also known as: IP exclusion list, Google Ads IP exclusion

A Google Ads campaign-level list that prevents specific IPs from seeing your ad. wall.click manages it automatically.
IP Exclusion is a Google Ads feature that prevents specific IP addresses or /24 subnets from seeing your ad and therefore clicking it. The campaign limit is 500 IPs. Manual maintenance is impractical, so click fraud protection tools rotate the list continuously and update it based on risk scoring. The API is not exposed for Smart Campaigns — it works only on Standard and Enhanced campaigns.
Related terms:Google AdsClick FraudCompetitor Click Fraud

ML-Based Click Fraud Detection

Also known as: Machine learning fraud detection, AI fraud detection

A detection approach that scores suspicious clicks with a trained model instead of static rules — catches transitional patterns.
Machine learning-based click fraud detection scores risk on a multi-dimensional feature space (IP, ASN, fingerprint, behavior, session quality) rather than fixed rules like "more than 10 clicks in 5 minutes → block". Benefits: adapts to new attack types, lower false positive rate. Drawback: can become a black box — which is why wall.click follows an explainable AI approach and exposes the triggering signals for every block.
Related terms:Behavioral SignalDevice FingerprintSIVT (Sophisticated Invalid Traffic)

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