FraudDroid

automated ad fraud detection for android apps

Feng Dong, Haoyu Wang, Li Li, Yao Guo, Tegawendé F. Bissyandé, Tianming Liu, Guoai Xu, Jacques Klein

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)

Abstract

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. Fraud- Droid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼93%) and recall (∼92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.

Original languageEnglish
Title of host publicationESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Subtitle of host publicationNovember 4–9, 2018 Lake Buena Vista, FL, USA
EditorsGary T. Leavens, Alessandro Garcia, Corina S. Pasareanu
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages257-268
Number of pages12
ISBN (Electronic)9781450355735
DOIs
Publication statusPublished - 2018
EventJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2018 - Lake Buena Vista, United States of America
Duration: 4 Nov 20189 Nov 2018
Conference number: 26th
https://conf.researchr.org/home/fse-2018

Conference

ConferenceJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2018
Abbreviated titleESEC/FSE 2018
CountryUnited States of America
CityLake Buena Vista
Period4/11/189/11/18
Internet address

Keywords

  • ad fraud
  • Android
  • automation
  • mobile app
  • user interface

Cite this

Dong, F., Wang, H., Li, L., Guo, Y., Bissyandé, T. F., Liu, T., ... Klein, J. (2018). FraudDroid: automated ad fraud detection for android apps. In G. T. Leavens, A. Garcia, & C. S. Pasareanu (Eds.), ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4–9, 2018 Lake Buena Vista, FL, USA (pp. 257-268). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3236024.3236045
Dong, Feng ; Wang, Haoyu ; Li, Li ; Guo, Yao ; Bissyandé, Tegawendé F. ; Liu, Tianming ; Xu, Guoai ; Klein, Jacques. / FraudDroid : automated ad fraud detection for android apps. ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4–9, 2018 Lake Buena Vista, FL, USA. editor / Gary T. Leavens ; Alessandro Garcia ; Corina S. Pasareanu. New York NY USA : Association for Computing Machinery (ACM), 2018. pp. 257-268
@inproceedings{d6e14800dbc4410d9081fd24e7fbca27,
title = "FraudDroid: automated ad fraud detection for android apps",
abstract = "Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. Fraud- Droid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼93{\%}) and recall (∼92{\%}). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.",
keywords = "ad fraud, Android, automation, mobile app, user interface",
author = "Feng Dong and Haoyu Wang and Li Li and Yao Guo and Bissyand{\'e}, {Tegawend{\'e} F.} and Tianming Liu and Guoai Xu and Jacques Klein",
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Dong, F, Wang, H, Li, L, Guo, Y, Bissyandé, TF, Liu, T, Xu, G & Klein, J 2018, FraudDroid: automated ad fraud detection for android apps. in GT Leavens, A Garcia & CS Pasareanu (eds), ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4–9, 2018 Lake Buena Vista, FL, USA. Association for Computing Machinery (ACM), New York NY USA, pp. 257-268, Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2018, Lake Buena Vista, United States of America, 4/11/18. https://doi.org/10.1145/3236024.3236045

FraudDroid : automated ad fraud detection for android apps. / Dong, Feng; Wang, Haoyu; Li, Li; Guo, Yao; Bissyandé, Tegawendé F.; Liu, Tianming; Xu, Guoai; Klein, Jacques.

ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4–9, 2018 Lake Buena Vista, FL, USA. ed. / Gary T. Leavens; Alessandro Garcia; Corina S. Pasareanu. New York NY USA : Association for Computing Machinery (ACM), 2018. p. 257-268.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - FraudDroid

T2 - automated ad fraud detection for android apps

AU - Dong, Feng

AU - Wang, Haoyu

AU - Li, Li

AU - Guo, Yao

AU - Bissyandé, Tegawendé F.

AU - Liu, Tianming

AU - Xu, Guoai

AU - Klein, Jacques

PY - 2018

Y1 - 2018

N2 - Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. Fraud- Droid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼93%) and recall (∼92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.

AB - Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. Fraud- Droid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (∼93%) and recall (∼92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detection.

KW - ad fraud

KW - Android

KW - automation

KW - mobile app

KW - user interface

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U2 - 10.1145/3236024.3236045

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BT - ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

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PB - Association for Computing Machinery (ACM)

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Dong F, Wang H, Li L, Guo Y, Bissyandé TF, Liu T et al. FraudDroid: automated ad fraud detection for android apps. In Leavens GT, Garcia A, Pasareanu CS, editors, ESEC/FSE'18 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: November 4–9, 2018 Lake Buena Vista, FL, USA. New York NY USA: Association for Computing Machinery (ACM). 2018. p. 257-268 https://doi.org/10.1145/3236024.3236045