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POSTER: AdHoneyDroid -- Capture Malicious Android Advertisements

Published: 03 November 2014 Publication History

Abstract

In this paper we explore the problem of collecting malicious smartphone advertisements. Most smartphone app contains advertisements and also suffers from vulnerable advertisement libraries. Malicious advertisements exploit the ad library vulnerability and attack victim smartphones. Similar to the traditional honeypots, we need an effective way to capture malicious ads. In this paper, we provide our approach named AdHoneyDroid. We build a crawler to gather apps on the android marketplaces and manually collect ad libraries and their vulnerabilities. Then AdHoneyDroid executes the apps and detects malicious advertisements. In our approach, we adopt the idea of API sandbox and TaintDroid to detect the attack event. We store the malicious advertisements in a database for future analysis. Malicious ads can help security analysts have a better understanding of current mobile attacks and also disclose the attack payloads.

References

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Israel Mojica Ruiz, Meiyappan Nagappan, Bram Adams, Thorsten Berger, Steffen Dienst, and Ahmed Hassan. On the relationship between the number of ad libraries in an android app and its rating. IEEE Software, 99(PrePrints):1, 2014.
[2]
FireEye Inc. Js-binding-over-http vulnerability and javascript sidedoor: Security risks affecting billions of android app downloads. http://goo.gl/eAFHEK.
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Google Inc. Android platform version distribution. http://developer.android.com/about/dashboards/index.html#Platform.
[4]
William Enck, Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N. Sheth. Taintdroid: An information flow tracking system for real-time privacy monitoring on smartphones. Commun. ACM, 57(3):99--106, March 2014.
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Google Inc. Google play. https://play.google.com/store.
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Google Inc. @javascriptinterface. http://developer.android.com/guide/webapps/webview.html#BindingJavaScript.
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Google Inc. addjavascriptinterface. http://developer.android.com/reference/android/webkit/WebView.html#addJavascriptInterface(java.lang.Object, java.lang.String).
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Michael C. Grace, Wu Zhou, Xuxian Jiang, and Ahmad-Reza Sadeghi. Unsafe exposure analysis of mobile in-app advertisements. In Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks, WISEC '12, pages 101--112, New York, NY, USA, 2012. ACM.
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Ryan Stevens, Clint Gibler, Jon Crussell, Jeremy Erickson, and Hao Chen. Investigating user privacy in android ad libraries. Citeseer.
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Shashi Shekhar, Michael Dietz, and Dan S. Wallach. Adsplit: Separating smartphone advertising from applications. In Proceedings of the 21st USENIX Conference on Security Symposium, Security'12, pages 28--28, Berkeley, CA, USA, 2012. USENIX Association.
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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, ASIACCS '12, pages 71--72, New York, NY, USA, 2012. ACM.
[12]
H. Kawabata, T. Isohara, K. Takemori, A Kubota, J. Kani, H. Agematsu, and M. Nishigaki. Sanadbox: Sandboxing third party advertising libraries in a mobile application. In Communications (ICC), 2013 IEEE International Conference on, pages 2150--2154, June 2013.

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      cover image ACM Conferences
      CCS '14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security
      November 2014
      1592 pages
      ISBN:9781450329576
      DOI:10.1145/2660267
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 03 November 2014

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      Author Tags

      1. android
      2. attack detection
      3. malicious ads

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      CCS '14 Paper Acceptance Rate 114 of 585 submissions, 19%;
      Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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