How to Find the IP Addresses Behind Fraudulent Ad Clicks

To find the IP address behind fraudulent clicks you have to go outside Google Ads entirely, because the platform does not record that information anywhere you can reach. Not in reports, not in the interface, not in the API. The click data and the visitor data live in two different places, and joining them is the whole job.

This is the step most exclusion projects skip. People block an address they found in a log file, feel productive, and never establish that the address was responsible for a single paid click. Before adding anything to a list, read our guide to Google Ads IP exclusions so you know what the block will and will not do once it is in place.

Where the Data Actually Lives

Every visit to your site touches a server, and that server records the requesting address by default. That log is your source of truth for who showed up. What it does not know is which of those visitors arrived from a paid ad and which came from organic search, a bookmark, or a crawler.

The bridge is the click identifier. Google appends a unique parameter to the landing page URL on every ad click. If your logs capture the full query string, that parameter is sitting in them, and it is what turns a list of visitors into a list of people you paid for. No parameter in the log means no reliable link, and anything you build on top of that is inference.

Check what your server is keeping first

Plenty of hosting setups log the path but discard the query string, which quietly removes the only field that matters here. Others rotate logs on a short cycle, so by the time you notice a spending problem the relevant days are gone. Confirm both before you start analyzing anything. Extending retention costs nothing and there is no way to recover a period you did not keep.

How to Find the IP Address Behind Fraudulent Clicks, Step by Step

The mechanics are not complicated once the logging is right.

• Pull the log range that matches the spending anomaly. Work from a specific window with a specific campaign in mind rather than a whole month of everything.
• Filter to requests carrying the Google click identifier. Everything without it is not a paid click and does not belong in this analysis.
• Group by address. You are looking for addresses appearing repeatedly across paid clicks, not addresses appearing a lot overall.
• Cross-reference against behavior. Pull session duration, pages viewed and conversions for those same visits from your analytics.
• Look up what each address belongs to. A residential ISP, a corporate network and a hosting provider are three different situations and warrant three different responses.

An alternative to log parsing is capturing the address at page load and writing it into analytics as a custom dimension alongside the click identifier. That puts both fields in one interface and makes the reporting far easier. It also means you only collect data from the point you implement it, so it does nothing about the spend you are currently annoyed by.

Keep the campaign attached to the address

One refinement that pays for itself: tag your landing page URLs so the log tells you which campaign and which keyword produced each visit, not just that a paid click happened. Without that, you get a list of addresses and no idea which part of the account they are draining. With it, you can often spot that the whole problem sits on two keywords or one match type, which is a cheaper fix than any blocklist.

Reading the Result Without Fooling Yourself

A repeated address is not evidence of fraud. It is evidence of repetition, and there are dull explanations for most repetition.

Shared networks are the big one. An office, a university, a coworking space or a hospital puts a large number of genuine people behind one address. Mobile carriers do the same at much larger scale through shared gateways. Your own team clicking ads to check they are live shows up identically to an attacker, and so does an agency, a client, or anyone running a rank checker.

What deserves attention is the combination: the same address, repeatedly, across paid clicks, with sessions that end almost immediately, no meaningful page depth, and no conversions across a long window. One of those signals on its own means nothing. Several together on an address that resolves to hosting infrastructure rather than a consumer ISP is a different matter. This pattern is the same one we describe in our guide to bot traffic in Google Ads.

Why the Manual Approach Runs Out of Road

Suppose you do all of this correctly and come away with a clean list of offending addresses. You add them, and the waste continues at roughly the same rate from addresses you have never seen.

That is the expected outcome for anything automated, and it is not a failure of your analysis. You identified the source accurately. The source simply is not tied to the address you identified, so blocking it moved the traffic rather than stopping it. Log analysis is inherently backward-looking: you are reading a record of clicks you already paid for.

Google's own invalid traffic systems work on a similar delay, detecting after the fact and issuing credits without disclosing what triggered them. We cover the limits of that in our piece on Google's invalid click refunds. The credits help. They arrive after the impression was served and the click was billed, which is why continuous detection is worth more than either the log or the credit. That is the gap automated click fraud protection is built to close, by identifying the traffic as it arrives instead of waiting for you to read about it later.

What to Do With What You Find

Block the addresses you are confident about. A competitor on a fixed office connection, a hosting range with no plausible reason to be clicking service ads, a single address generating clicks at three in the morning with nothing else attached to it: those are worth the slots.

Leave alone anything you cannot explain, anything sitting behind a carrier gateway, and anything that has ever converted. The cost of a wrong exclusion is invisible, because you never see the customer you shut out. That asymmetry is exactly why the identification work matters more than the blocking work.