Troy Raeder
Papers
1
Total Citations
25
H-Index
1
About
Troy Raeder is a leading researcher in computational advertising, machine learning, and fraud detection, whose work bridges the gap between large-scale data analytics and real-world security challenges. He is best known for pioneering methods to combat online display advertising exchange fraud, most notably through his highly cited 2013 paper on using co-visitation networks to detect fraudulent traffic patterns at scale. This work, which has garnered 25 citations, introduced a novel graph-based approach to identifying non-human traffic generated by bots and click farms, fundamentally changing how the industry approaches ad fraud detection. Raeder’s research has had significant practical impact, helping to protect billions of dollars in digital advertising spend by enabling more robust, real-time fraud prevention systems. His contributions extend to the broader field of data mining, where he has explored how behavioral data from web browsers can be leveraged for both model building and decision-making. Through his innovative use of network analysis and machine learning, Raeder has established himself as a key figure in the fight against online fraud, making the digital advertising ecosystem safer and more trustworthy for businesses and consumers alike.
Research Focus
Key Achievements
Top Papers
- 1