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Knowing your enemy: understanding and detecting malicious web advertising

Published: 16 October 2012 Publication History

Abstract

With the Internet becoming the dominant channel for marketing and promotion, online advertisements are also increasingly used for illegal purposes such as propagating malware, scamming, click frauds, etc. To understand the gravity of these malicious advertising activities, which we call malvertising, we perform a large-scale study through analyzing ad-related Web traces crawled over a three-month period. Our study reveals the rampancy of malvertising: hundreds of top ranking Web sites fell victims and leading ad networks such as DoubleClick were infiltrated.
To mitigate this threat, we identify prominent features from malicious advertising nodes and their related content delivery paths, and leverage them to build a new detection system called MadTracer. MadTracer automatically generates detection rules and utilizes them to inspect advertisement delivery processes and detect malvertising activities. Our evaluation shows that MadTracer was capable of capturing a large number of malvertising cases, 15 times as many as Google Safe Browsing and Microsoft Forefront did together, at a low false detection rate. It also detected new attacks, including a type of click-fraud attack that has never been reported before.

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      cover image ACM Conferences
      CCS '12: Proceedings of the 2012 ACM conference on Computer and communications security
      October 2012
      1088 pages
      ISBN:9781450316514
      DOI:10.1145/2382196
      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 16 October 2012

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      Author Tags

      1. malvertising
      2. online advertising
      3. statistical learning

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      CCS'12
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      CCS'12: the ACM Conference on Computer and Communications Security
      October 16 - 18, 2012
      North Carolina, Raleigh, USA

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      Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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      October 14 - 18, 2024
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      • (2024)Cross-Country Examination of People’s Experience with Targeted Advertising on Social MediaExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650780(1-10)Online publication date: 11-May-2024
      • (2024)Detecting and Understanding Self-Deleting JavaScript CodeProceedings of the ACM Web Conference 202410.1145/3589334.3645540(1768-1778)Online publication date: 13-May-2024
      • (2024)Targeted and Troublesome: Tracking and Advertising on Children’s Websites2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00118(1517-1535)Online publication date: 19-May-2024
      • (2024)New Perspectives in Information Retrieval: Deep Discovery of Specific Category Websites Driven by Dynamic Keywords2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)10.1109/ICECAI62591.2024.10675086(90-95)Online publication date: 31-May-2024
      • (2024)A Method to Detect Threat in Advertisement URL and its Content2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)10.1109/CONECCT62155.2024.10677073(1-6)Online publication date: 12-Jul-2024
      • (2024)A Study on Machine Learning and Deep Learning Techniques for Identifying Malicious Web ContentSN Computer Science10.1007/s42979-024-03099-35:7Online publication date: 16-Aug-2024
      • (2024)Utilizing DNS and VirusTotal for Automated Ad-Malware DetectionWeb Engineering10.1007/978-3-031-62362-2_31(393-396)Online publication date: 16-Jun-2024
      • (2023)Problematic advertising and its disparate exposure on facebookProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620554(5665-5682)Online publication date: 9-Aug-2023
      • (2023)A First Look at the Privacy Harms of the Public Suffix ListProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624836(383-390)Online publication date: 24-Oct-2023
      • (2023)Validating Multimedia Content Moderation Software via Semantic FusionProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598079(576-588)Online publication date: 12-Jul-2023
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