PhD Final Oral Defense: Vacha Dave, Thursday, March 21, 2:00 p.m., ACES 6.442
PhD Defense: Vacha Dave
Date: Thursday, March 21
Time: 2:00 p.m.
Place: ACES 6.442
Research Supervisor: Yin Zhang
Title of dissertation: Measuring, Fingerprinting and Catching Click-spam in ad networks
Advertising plays a vital role in supporting free websites and smartphone apps. Click-spam, i.e., fraudulent or invalid clicks on online ads where the user has no actual interest in the advertiser's site, results in advertising revenue being misappropriated by click-spammers. While ad networks take active measures to block click-spam today, the effectiveness of these measures is largely unknown. Moreover, advertisers and third parties have no way of independently estimating or defending against click-spam.
In this work, we take the first systematic look at click-spam. We propose the first methodology for advertisers to independently measure click-spam rates on their ads. We also develop an automated methodology for ad networks to proactively detect different simultaneous click-spam attacks. We validate both methodologies using data from major ad networks. We then conduct a large-scale measurement study of click-spam across ten major ad networks and four types of ads. In the process, we identify and perform in-depth analysis on seven ongoing click-spam attacks not blocked by major ad networks. Our findings highlight the severity of the click-spam problem, especially for mobile ads.
Next, we present Viceroi, a simple yet general approach to catching click-spam in search ad networks. It is designed around the invariant that click-spam is a business (for click-spammers) that needs to deliver high return on investment (ROI). Evaluation on a large real-world ad network dataset shows that Viceroi catches a diverse range of click- spam attacks without any tuning knobs, has good performance on ROC and precision-recall curves, and is resilient against click-spammers using larger botnets over time.