NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking

Open access


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.

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  • [1] AppBrain.

  • [2] Daniel G Goldstein R Preston McAfee and Siddharth Suri. The Cost of Annoying Ads. In Proceedings of the 22nd international conference on World Wide Web pages 459–470. ACM 2013.

  • [3] Narseo Vallina-Rodriguez Jay Shah Alessandro Finamore Yan Grunenberger Konstantina Papagiannaki Hamed Haddadi and Jon Crowcroft. Breaking for Commercials: Characterizing Mobile Advertising. In Proceedings of the 2012 ACM conference on Internet measurement conference pages 343–356. ACM 2012.

  • [4] Wei Meng Ren Ding Simon P Chung Steven Han and Wenke Lee. The Price of Free: Privacy Leakage in Personalized Mobile In-App Ads. In Network and Distributed System Security Symposium (NDSS) 2016.

  • [5] Apostolis Zarras Alexandros Kapravelos Gianluca Stringhini Thorsten Holz Christopher Kruegel and Giovanni Vigna. The Dark Alleys of Madison Avenue: Understanding Malicious Advertisements. In Proceedings of the 2014 Conference on Internet Measurement Conference pages 373–380. ACM 2014.

  • [6] Adblock Browser.

  • [7] UC Browser.

  • [8] EasyList.

  • [9] PageFair. The state of the blocked web – 2017 Global Adblock Report. 2017.

  • [10] James Hercher. Mobile Ad Blocking Takes Off In Asia Sparked By User Data Costs. 2017.

  • [11] Alex Hern. A proxy war: Apple ad-blocking software scares publishers but rival Google is target. 2016.

  • [12] Adblock Plus for Android.

  • [13] Muhammad Ikram and Mohamed Ali Kaafar. A First Look at Mobile Ad-Blocking Apps. IEEE Network Computing and Application (NCA) 2017.

  • [14] Jingjing Ren Ashwin Rao Martina Lindorfer Arnaud Legout and David Choffnes. ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic. In Proceedings of the 14th Annual International Conference on Mobile Systems Applications and Services pages 361–374. ACM 2016.

  • [15] Anastasia Shuba Anh Le Emmanouil Alimpertis Minas Gjoka and Athina Markopoulou. AntMonitor: System and Applications. arXiv preprint arXiv:1611.04268 2016.

  • [16] Abbas Razaghpanah Narseo Vallina-Rodriguez Srikanth Sundaresan Christian Kreibich Phillipa Gill Mark Allman and Vern Paxson. Haystack: A Multi-Purpose Mobile Vantage Point in User Space. arXiv:1510.01419v3 Oct. 2016.

  • [17] Narseo Vallina-Rodriguez Srikanth Sundaresan Abbas Razaghpanah Rishab Nithyanand Mark Allman Christian Kreibich and Phillipa Gill. Tracking the Trackers: Towards Understanding the Mobile Sdvertising and Tracking Ecosystem. arXiv preprint arXiv:1609.07190 2016.

  • [18] Adblock Plus for Android Removed from Google Play Store.

  • [19] Ben Williams. Adblock Plus and (a little) more. 2016.

  • [20] Georg Merzdovnik Markus Huber Damjan Buhov Nick Nikiforakis Sebastian Neuner Martin Schmiedecker and Edgar Weippl. Block Me If You Can: A Large-Scale Study of Tracker-Blocking Tools. In Security and Privacy (EuroS&P) 2017 IEEE European Symposium on pages 319–333. IEEE 2017.

  • [21] DNS-based Host Blocker for Android.

  • [22] Disconnect.

  • [23] Umar Iqbal Zubair Shafiq and Zhiyun Qian. The Ad Wars: Retrospective Measurement and Analysis of Anti-Adblock Filter Lists. In ACM Internet Measurement Conference (IMC) 2017.

  • [24] Sruti Bhagavatula Christopher Dunn Chris Kanich Minaxi Gupta and Brian Ziebart. Leveraging Machine Learning to Improve Unwanted Resource Filtering. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop pages 95–102. ACM 2014.

  • [25] Jason Bau Jonathan Mayer Hristo Paskov and John C Mitchell. A Promising Direction for Web Tracking Countermeasures. Proceedings of W2SP 2013.

  • [26] David Gugelmann Markus Happe Bernhard Ager and Vincent Lenders. An Automated Approach for Complementing Ad Blockers’ Blacklists. Proceedings on Privacy Enhancing Technologies 2015(2):282–298 2015.

  • [27] Privacy Badger. 2018.

  • [28] Bin Liu Bin Liu Hongxia Jin and Ramesh Govindan. Efficient Privilege De-escalation for Ad Libraries in Mobile Apps. In Proceedings of the 13th Annual International Conference on Mobile Systems Applications and Services pages 89–103. ACM 2015.

  • [29] Paul Pearce Adrienne Porter Felt Gabriel Nunez and David Wagner. AdDroid: Privilege Separation for Applications and Advertisers in Android. In Proceedings of the 7th ACM Symposium on Information Computer and Communications Security pages 71–72. Acm 2012.

  • [30] Marten Oltrogge Yasemin Acar Sergej Dechand Matthew Smith and Sascha Fahl. To Pin or Not to Pin-Helping App Developers Bullet Proof Their TLS Connections. In USENIX Security Symposium pages 239–254 2015.

  • [31] Adblock Plus Library for Android.

  • [32] Anastasia Shuba Evita Bakopoulou and Athina Markopoulou. Privacy Leak Classification on Mobile Devices. In Signal Processing Advances in Wireless Communications (SPAWC) 2017 IEEE 18th International Workshop on. IEEE 2018. To Appear.

  • [33] Anastasia Shuba Evita Bakopoulou Milad Asgari Mehrabadi Hieu Le David Choffnes and Athina Markopoulou. AntShield: On-Device Detection of Personal Information Exposure. arXiv preprint arXiv:1803.01261 2018.

  • [34] Wireshark.

  • [35] AdAway hosts.

  • [36] hpHosts.

  • [37] AdMob.

  • [38] MoPub.

  • [39] Saksham Chitkara Nishad Gothoskar Suhas Harish Jason I Hong and Yuvraj Agarwal. Does this App Really Need My Location?: Context-Aware Privacy Management for Smartphones. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 1(3):42 2017.

  • [40] Andrea Continella Yanick Fratantonio Martina Lindorfer Alessandro Puccetti Ali Zand Christopher Kruegel and Giovanni Vigna. Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis. In Network and Distributed System Security Symposium (NDSS) 2017.

  • [41] Rishab Nithyanand Sheharbano Khattak Mobin Javed Narseo Vallina-Rodriguez Marjan Falahrastegar Julia E. Powles Emiliano De Cristofaro Hamed Haddadi and Steven J. Murdoch. Ad-Blocking and Counter Blocking: A Slice of the Arms Race. In USENIX Workshop on Free and Open Communications on the Internet (FOCI) 2016.

  • [42] Muhammad Haris Mughees Zhiyun Qian and Zubair Shafiq. Detecting Anti Ad-blockers in the Wild. In Privacy Enhancing Technologies Symposium (PETS) 2017.

  • [43] Shitong Zhu Xunchao Hu Zhiyun Qian Zubair Shafiq and Heng Yin. Measuring and Disrupting Anti-Adblockers Using Differential Execution Analysis. In Network and Distributed System Security Symposium (NDSS) 2018.

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