The following descriptions explain the different types of invalid traffic with corresponding examples. These examples are meant to be illustrative to help represent the differences between categories and only represent a subset of possible invalid behaviors & tactics.
Ad traffic originating from servers in data centers whose IPs are linked to invalid activity (typically non-human traffic). These are usually known data center IPs that are likely included in an industry list, such as the Trustworthy Accountability Group (TAG) Data Center IP List.
Example: TAG data center IP list
A program or automated script that requests content and declares itself as non-human through a variety of identification mechanisms. These crawlers are usually included in the IAB International Spiders and Bots List.
Example: IAB spiders & bots blacklist
Ad traffic that includes one or more attributes (e.g. user cookie) associated with known irregular patterns, such as auto-refresh traffic or duplicate clicks.
Example: False Representation
A program or automated script that requests web content (including digital ads) without user involvement and without declaring itself as a crawler, such as and primarily referring to botnets.
An ad request for inventory that is different from the actual inventory being supplied, including ad requests where the actual ad is rendered to a different website or application, device, or other target (such as geography).
Examples: Spoofed measurement, domain spoofing, emulators masquerading
Misleading User Interface
A web page, application, or other visual element modified to falsely include one or more ads. This includes rendering ads that are not visible to the user or tricking users to click on an ad.
Examples: Stacked ads, ads hiding
A browser, application, or other program that triggers an ad interaction without a user’s consent, such as an unintended click, an unexpected conversion, or false attribution for installation of a mobile application.
Examples: Pop-unders, aggressive pop-ups, forced new window
Invalid traffic that cannot be classified using any of the other categories in the taxonomy or sensitive invalid traffic that cannot be disclosed.
Examples: Machine learning models, sensitive invalid traffic