Abstract
With the widespread use of mobile devices, mobile online advertising is taking more and more market share. Cost per click and cost per view are the most popular pricing modes in mobile internet advertising, which take effective clicks or displaying duration as the charging basis. However, at the same time, ad fraud, which uses illegal and invalid clicks to fraud advertisers in order to obtain unreasonable income, become a serious problem. Most of the previous studies on click fraud in website focused on network traffic data analysis. This makes them cannot solve the placement fraud problem, which use invalid placement to mislead user to click on it in mobile apps. In this paper, we propose a joint crowdsourcing and data analyzing based placement click fraud detection system. For the characteristic of placement fraud in mobile apps, automatic processing cannot cover every possible fraud. To overcome this, our report system provides a platform to find all possible placement fraud through crowdsourcing. Report system has three main services: a monitor service for monitoring user’s call; a layout service for recording the screen; a data service for recording the backend data. Because the placement fraud only appears when users use the apps, the report system based on crowdsourcing can cover every possible placement fraud. We implement our system in 10 tablets with 500 apps to evaluate its effectiveness. Experiment result shows that our approach can record enough data to analysis which app has placement fraud. What’s more, our system can figure out some special placement fraud which pop ads when user is using other apps. This placement fraud cannot be solved through automatic method in previous studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
IAB Internet Advertising Revenue Report, 2012 Full Year Results. https://www.iab.com/wp-content/uploads/2015/05
IAB Internet Advertising Revenue Report, 2016 Full Year Results. https://www.iab.com/wp-content/uploads/2016/04
The Truth About Mobile Click Fraud. http://www.imgrind.com/the-truth-aboutmobile-click-fraud
Metwally, A., Agrawal, D., El Abbadi, A.: Duplicate detection in click streams. In: 14th International Conference on World Wide Web, pp. 12–21. ACM (2005)
Metwally, A., Agrawal, D., Abbadi, A.E.: Using association rules for fraud detection in web advertising networks. In: 31st International Conference on Very Large Data Bases, pp. 169–180. VLDB Endowment (2005)
Blundo, C., Cimato, S.: SAWM: a tool for secure and authenticated web metering. In: 14th International Conference on Software Engineering and Knowledge Engineering, pp. 641–648. ACM (2002)
Immorlica, N., Jain, K., Mahdian, M., Talwar, K.: Click fraud resistant methods for learning click-through rates. In: Deng, X., Ye, Y. (eds.) WINE 2005. LNCS, vol. 3828, pp. 34–45. Springer, Heidelberg (2005). https://doi.org/10.1007/11600930_5
The Lan’s Gifts v. Google Report. http://googleblog.blogspot.com/pdf/Tuzhilin_Report.pdf
Kantardzic, M., Walgampaya, C., Wenerstrom, B., Lozitskiy, O., Higgins, S., King, D.: Improving click fraud detection by real time data fusion. In: IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2008, pp. 69–74 (2008)
Costa, R.A., de Queiroz, R.J., Cavalcanti, E.R.: A proposal to prevent click-fraud using clickable CAPTCHAs. In: 2012 IEEE Sixth International Conference Software Security and Reliability Companion (SERE-C), pp. 62–67. IEEE (2012)
Bots Mobilize. http://www.dmnews.com/bots-mobilize/article/291566/
Blizard, T., Livic, N.: Click-fraud monetizing malware: a survey and case study. In: 2012 7th International Conference Malicious and Unwanted Software (MALWARE), pp. 67–72. IEEE (2012)
Miller, B., Pearce, P., Grier, C., Kreibich, C., Paxson, V.: What’s clicking what? Techniques and innovations of today’s clickbots. In: Holz, T., Bos, H. (eds.) DIMVA 2011. LNCS, vol. 6739, pp. 164–183. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22424-9_10
Liu, B., Nath, S., Govindan, R., Liu, J.: DECAF: detecting and characterizing ad fraud in mobile apps. In: NSDI, pp. 57–70 (2014)
Crussell, J., Stevens, R., Chen, H.: Madfraud: investigating ad fraud in android applications. In: 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 123–134. ACM (2014)
Dave, V., Guha, S., Zhang, Y.: Measuring and fingerprinting click-spam in ad networks. ACM SIGCOMM Comput. Commun. Rev. 42(4), 175–186 (2012)
AdMob Publisher Guidelines and Policies. http://support.google.com/admob/answer/1307237?hl=en&topic=1307235
Microsoft pubCenter Publisher Terms and Conditions. http://pubcenter.microsoft.com/StaticHTML/TC/TCen.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, B., Wu, F., Chen, G. (2018). Placement Fraud Detection on Smart Phones: A Joint Crowdsourcing and Data Analyzing Based Approach. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-8890-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8889-6
Online ISBN: 978-981-10-8890-2
eBook Packages: Computer ScienceComputer Science (R0)