Open Source Invalid Traffic Detection
Built over 2 years to meet the traffic filtering needs of the most demanding buying platforms, trading desks, exchanges and verification companies.
Detects invalid banner, video and in-app traffic on both mobile and desktop.
Leverages a proven method widely used in finance, intelligence and physics.
Available on permissive Apache license and guaranteed to remain 100% free.
Easy to deploy docker containers for a scalable fully featured pre-bid solution.
“Nameles appears to be exactly the sort of solution that WFA and our members were calling for in our ad fraud compendium. We should be encouraging the emergence of open source solutions which unify our industry through combatting a common enemy. We would encourage DSPs and our other industry partners to investigate this opportunity with a view to leveraging it in complement to existing tools designed to combat ad fraud.”Stephan Loerke
BUILT AND TESTED FOR SCALE
NEGLIGIBLE COST OF OPERATION
BUILT BY A HIGHLY REGARDED TEAM
BACKED BY 3 SCIENTIFIC PAPERS
PERMISSIVE LICENCING TERMS
## GETTING STARTED WITH NAMELES IN 5 MINUTES
$ wget https://goo.gl/yNYzLJ
$ chmod +x setup && ./setup
Detects display, video and in-app based ad fraud, web scraping and other forms of invalid traffic from both mobile and desktop sources.
Entropy measurement is one of the most widely used methods for detecting anomalies in large datasets with many unknowns across a wide range of problems.
Highly optimized C++ codes run on minimal computing resource and scale to meet the needs of even the largest advertising technology companies.
Low Operation Cost
Pre-bid filtering of 300,000 QPS traffic stream have the total-cost-of-operation of less than $5,000 per month with below 50ms decision delay.
The permissive apache license allows reuse, modification, and reselling to meet the needs of all advertising technology companies.
Built by Experts
Built in close collaboration between the most experienced ad fraud experts, leading adtech innovators, and highly regarded academic researchers.
An entropy-based methodology for detecting Online Advertising Fraud at scale
Introduces the Nameles method for conducting mystery shopping studies for programmatic media buying. The paper highlights startling findings for the first time shedding light into Google’s black box, exposing gross misreporting. READ
Can Intermediaries in Programmatic Advertising Obtain Economical Benefit from Invalid Traffic Filtering!?
A detailed technical overview of the methodology, technical specification, and deployment in actual use of Nameles, the first truly scalable online ad fraud solution. The paper provides a robust economic analysis of the impact ad fraud has on platform profitability. READ