Shujiang Wu (Johns Hopkins University), Pengfei Sun (F5, Inc.), Yao Zhao (F5, Inc.), Yinzhi Cao (Johns Hopkins University)

Browser fingerprints, while traditionally being used for web tracking, have recently been adopted more and more often for defense or detection of various attacks targeting real-world websites. Faced with these situations, adversaries also upgrade their weapons to generate their own fingerprints---defined as adversarial fingerprints---to bypass existing defense or detection. Naturally, such adversarial fingerprints are different from benign ones from user browsers because they are generated intentionally for defense bypass. However, no prior works have studied such differences in the wild by comparing adversarial with benign fingerprints let alone how adversarial fingerprints are generated.

In this paper, we present the first billion-scale measurement study of browser fingerprints collected from 14 major commercial websites (all ranked among Alexa/Tranco top 10,000). We further classify these fingerprints into either adversarial or benign using a learning-based, feedback-driven fraud and bot detection system from a major security company, and then study their differences. Our results draw three major observations: (i) adversarial fingerprints are significantly different from benign ones in many metrics, e.g., entropy, unique rate, and evolution speed, (ii) adversaries are adopting various tools and strategies to generate adversarial fingerprints, and (iii) adversarial fingerprints vary across different attack types, e.g., from content scraping to fraud transactions.

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Mark Huasong Meng (National University of Singapore), Qing Zhang (ByteDance), Guangshuai Xia (ByteDance), Yuwei Zheng (ByteDance), Yanjun Zhang (The University of Queensland), Guangdong Bai (The University of Queensland), Zhi Liu (ByteDance), Sin G. Teo (Agency for Science, Technology and Research), Jin Song Dong (National University of Singapore)

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Automata-Based Automated Detection of State Machine Bugs in Protocol...

Paul Fiterau-Brostean (Uppsala University, Sweden), Bengt Jonsson (Uppsala University, Sweden), Konstantinos Sagonas (Uppsala University, Sweden and National Technical University of Athens, Greece), Fredrik Tåquist (Uppsala University, Sweden)

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Securing Federated Sensitive Topic Classification against Poisoning Attacks

Tianyue Chu (IMDEA Networks Institute), Alvaro Garcia-Recuero (IMDEA Networks Institute), Costas Iordanou (Cyprus University of Technology), Georgios Smaragdakis (TU Delft), Nikolaos Laoutaris (IMDEA Networks Institute)

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Unlocking the Potential of Domain Aware Binary Analysis in...

Dr. Zhiqiang Lin (Distinguished Professor of Engineering at The Ohio State University)

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