Pixalate is an MRC-accredited company for the detection and filtration of Sophisticated Invalid Traffic ("SIVT") desktop and mobile web impressions.
According to the Media Rating Council’s (MRC) standards for Invalid Traffic Detection and Filtration Guidelines, there are two types of invalid traffic:
Two related examples of SIVT is “adware and malware.”
What are “adware and malware” in the MRC definition of SIVT?
According to the MRC, adware and malware are considered SIVT.
- Malware: Malicious software installed on a user’s machine with or without their consent
- Adware: Content which attempt to install malware on a user’s computer in the context of what appears to be a legitimate ad.
Malware installed on a user’s computer can be used for a variety of nefarious means. In the advertising world, the malware can hijack the user’s computer and simulate legitimate activity to defraud advertisers.
An example of both adware and malware is the Chinese “Fireball” attack that infected over 250 million computers. The “Fireball” adware was also installing software (malware) on the infected computers, which hijacked the infected devices and simulated web traffic to generate and steal ad revenue.
MRC-accredited ad fraud detection and prevention companies must be able to identify and filter adware and malware.
What are some other examples of SIVT?
Adware and malware are just two examples of Sophisticated Invalid Traffic (SIVT) as defined by the MRC. To learn about some of the other examples of SIVT, click on any of the examples below:
- Differentiating human and IVT traffic when originating from the same or similar source
- Bots and spiders or other crawlers masquerading as legitimate users
- Hijacked devices, user sessions, ad tags, and ad creative
- Hidden/stacked/covered or otherwise intentionally obfuscated ad serving
- Invalid proxy traffic
- Incentivized manipulation of measurements
- Falsified viewable impression decisions
- Falsely represented sites
- Cookie stuffing, recycling, or harvesting
- Manipulation or falsification of location data