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Understanding Malvertising Through Ad-Injecting Browser Extensions

Published: 18 May 2015 Publication History

Abstract

Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.

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Information & Contributors

Information

Published In

cover image ACM Other conferences
WWW '15: Proceedings of the 24th International Conference on World Wide Web
May 2015
1460 pages
ISBN:9781450334693

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 18 May 2015

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Author Tags

  1. adware
  2. browser extension
  3. malvertising

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WWW '15
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  • IW3C2

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WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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  • (2023)An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript DetectorsProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598146(1420-1432)Online publication date: 12-Jul-2023
  • (2023)An End-to-End Analysis of Covid-Themed Scams in the WildProceedings of the 2023 ACM Asia Conference on Computer and Communications Security10.1145/3579856.3582831(509-523)Online publication date: 10-Jul-2023
  • (2023)AdCPG: Classifying JavaScript Code Property Graphs with Explanations for Ad and Tracker BlockingProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3623084(3505-3518)Online publication date: 15-Nov-2023
  • (2023)Malvertisements Detection using urlscan.io, Pulsedive, and SucuriSiteCheck2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG)10.1109/SEB-SDG57117.2023.10124634(1-8)Online publication date: 5-Apr-2023
  • (2023)JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN58367.2023.00041(339-351)Online publication date: Jun-2023
  • (2023)Behavioral analysis of cybercrime: Paving the way for effective policing strategiesJournal of Economic Criminology10.1016/j.jeconc.2023.1000342(100034)Online publication date: Dec-2023
  • (2022)Characterizing Cryptocurrency-themed Malicious Browser ExtensionsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35706036:3(1-31)Online publication date: 8-Dec-2022
  • (2022)Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy TheoriesProceedings of the ACM Web Conference 202210.1145/3485447.3512142(2707-2718)Online publication date: 25-Apr-2022
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