BuzzCity has been working with SMU and NUS to improve its ability to identify and manage click fraud.  In a series of crowd-sourcing and development events, researchers analysed data including publisher ID, campaign ID, handset, IP address and the time of the click to try and establish how fraudsters resemble humans and evade detection. Click fraudsters, the researchers found, have reached a 'high level of sophistication' to help them mimic legitimate users in large volumes. Fraud detection is challenging for many data mining and machine learning algorithms, they said, because the practice involves many variables and may only account for a small fraction of all the clicks.