Measuring and Disrupting Anti-Adblockers Using Differential Execution Analysis

Zhu, Shitong; Hu, Xin; Qian, Zhiyun; Shafiq, Zubair; Yin, Heng

doi:10.14722/ndss.2018.23331

Cited by 35 publications

(44 citation statements)

References 15 publications

(31 reference statements)

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“…Apps may also attempt to use anti ad-blockers to detect presence of ad-blockers [41,42]. While we did not observe such behavior, if and when that happens, we can block anti ad-blocking scripts that are downloaded from third-parties [23] or use more sophisticated dynamic analysis techniques to circumvent anti ad-blocking logic that is part of the app [43].…”

Section: Discussionmentioning

confidence: 91%

NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking

Shuba

Markopoulou

Shafiq

2018

Proceedings on Privacy Enhancing Technologies

Self Cite

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Although advertising is a popular strategy for mobile app monetization, it is often desirable to block ads in order to improve usability, performance, privacy, and security. In this paper, we propose NoMoAds to block ads served by any app on a mobile device. NoMoAds leverages the network interface as a universal vantage point: it can intercept, inspect, and block outgoing packets from all apps on a mobile device. NoMoAds extracts features from packet headers and/or payload to train machine learning classifiers for detecting ad requests. To evaluate NoMoAds, we collect and label a new dataset using both EasyList and manually created rules. We show that NoMoAds is effective: it achieves an F-score of up to 97.8% and performs well when deployed in the wild. Furthermore, NoMoAds is able to detect mobile ads that are missed by EasyList (more than one-third of ads in our dataset). We also show that NoMoAds is efficient: it performs ad classification on a per-packet basis in real-time. To the best of our knowledge, NoMoAds is the first mobile ad-blocker to effectively and efficiently block ads served across all apps using a machine learning approach.

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Section: Discussionmentioning

confidence: 91%

NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking

Shuba

Markopoulou

Shafiq

2018

Proceedings on Privacy Enhancing Technologies

Self Cite

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“…In Reference [61], static program analysis is applied to JavaScript source code to identify JavaScript that loads and displays ads. Anti-adblockers scripts are detected on 30.5% of the Alexa top 10K websites [99]. JavaScript rewriting and API hooking is proposed in Reference [99] to counter anti-adblocker scripts.…”

Section: Ad Blocking and Anti-adblockingmentioning

confidence: 99%

Threats to Online Advertising and Countermeasures

Cai

Yee

Gu³

et al. 2020

Digital Threats

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Online advertising, also known as web advertising or Internet marketing, is the means and process of promoting products and services on the Internet, and it has been one of the important business models for the Internet. Due to its lucrative nature and its large scale of adoption, it has also been a target for malicious parties with various attack aims such as getting a cut of online advertising revenues, obtaining a user's privacy, and spreading malware. Over the years, a great deal of research has been conducted on online advertising. Recently, the health of the online advertising ecosystem has become more of a concern for both advertisers and regular Internet users. Advertising budgets have been abused, and Internet users' privacy and security have been infringed. In this article, we broadly study threats to online advertising and trace the root causes from a systems point of view. Existing threat mitigation strategies are also reviewed and analyzed. To protect online advertising, which has been an essential funding source of many free Internet services, several challenges still need to be addressed, including the need for transparency of the advertising ecosystem and software vulnerabilities on the client-side. To overcome these challenges, we conclude by brainstorming some innovative ideas on some potentially interesting and useful research directions. CCS Concepts: • Security and privacy → Web application security; Domain-specific security and privacy architectures;

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“…Alternatively, the ad-blocker's adversaries may try to detect its presence, to display warnings or deny access to the user. A common strategy (used by 30% of publishers in the Alexa top-10k) adds fake ad-content (honeypots) to a page and uses JavaScript to check if the ads were blocked [95]. This practice leads to an orthogonal arms race on ad-block detection [57,58,60] (see Appendix A).…”

Section: Threat Model and Adversariesmentioning

confidence: 99%

“…Attack C4: Detection with adversarial honeypots. To detect adblocking, publishers can use honeypots that falsely trigger adblockers [95]. The false positives in Figures 5-6 are innocuous elements that are falsely classified as ads or ad-disclosures.…”

Section: Originalmentioning

confidence: 99%

AdVersarial

Tramèr

Dupré

Rusak

et al. 2019

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

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Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe attacks on multiple perceptual ad-blocking techniques, and unveil a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks.We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the adblocker's detection pipeline, that enable publishers or ad networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries.On one hand, we show that perceptual ad-blocking must visually classify rendered web content to escape an arms race centered on obfuscation of page markup. On the other, we present a concrete set of attacks on visual ad-blockers by constructing adversarial examples in a real web page context. For seven ad-detectors, we create perturbed ads, ad-disclosure logos, and native web content that misleads perceptual ad-blocking with 100% success rates. In one of our attacks, we demonstrate how a malicious user can upload adversarial content, such as a perturbed image in a Facebook post, that fools the ad-blocker into removing another users' non-ad content.Moving beyond the Web and visual domain, we also build adversarial examples for AdblockRadio, an open source radio client that uses machine learning to detects ads in raw audio streams.• Security and privacy → Web application security; • Computing methodologies → Machine learning approaches.

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Measuring and Disrupting Anti-Adblockers Using Differential Execution Analysis

Cited by 35 publications

References 15 publications

NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking

NoMoAds: Effective and Efficient Cross-App Mobile Ad-Blocking

Threats to Online Advertising and Countermeasures

AdVersarial

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