UI Computer Science Assistant Professor Zubair Shafiq, along with co-authors Anastasia Shuba and Athina Markopoulou at the University of California-Irvine, were awarded the 2018 Andreas Pfitzmann Best Student Paper Award for "NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking,” which was published in the 18th annual Privacy Enhancing Technologies Symposium (PETS 2018). Held this year in Barcelona, Spain from July 24-27, 2018, PETS is the top research venue dedicated to the study and advancement of privacy enhancing technologies.
The Andreas Pfitzmann Best Student Paper Award is named in memory of the internationally recognized German computer scientist who died in 2010. The award is given to papers written solely or primarily by a student who is presenting his/her work at the conference. It is based on the scientific quality of the paper, its expected impact on the field, and the quality of the student’s presentation.
Paper abstract:
Although advertising is a popular strategy for mobile app monetization, it is often desirable to block ads in order to improve usability, performance, privacy, and security. In this paper, we propose NoMoAds to block ads served by any app on a mobile device. NoMoAds leverages the network interface as a universal vantage point: it can intercept, inspect, and block outgoing packets from all apps on a mobile device. NoMoAds extracts features from packet headers and/or payload to train machine learning classifiers for detecting ad requests. To evaluate NoMoAds, we collect and label a new dataset using both EasyList and manually created rules. We show that NoMoAds is effective: it achieves an F-score of up to 97.8% and performs well when deployed in the wild. Furthermore, NoMoAds is able to detect mobile ads that are missed by EasyList (more than one-third of ads in our dataset). We also show that NoMoAds is efficient: it performs ad classification on a per-packet basis in real-time. To the best of our knowledge, NoMoAds is the first mobile ad-blocker to effectively and efficiently block ads served across all apps using a machine learning approach.