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.

View More Papers

Towards Privacy-Preserving Platooning Services by means of Homomorphic Encryption

Nicolas Quero (Expleo France), Aymen Boudguiga (CEA LIST), Renaud Sirdey (CEA LIST), Nadir Karam (Expleo France)

Read More

On the Feasibility of Profiling Electric Vehicles through Charging...

Ankit Gangwal (IIIT Hyderabad), Aakash Jain (IIIT Hyderabad) and Mauro Conti (University of Padua)

Read More

OBI: a multi-path oblivious RAM for forward-and-backward-secure searchable encryption

Zhiqiang Wu (Changsha University of Science and Technology), Rui Li (Dongguan University of Technology)

Read More

Trellis: Robust and Scalable Metadata-private Anonymous Broadcast

Simon Langowski (Massachusetts Institute of Technology), Sacha Servan-Schreiber (Massachusetts Institute of Technology), Srinivas Devadas (Massachusetts Institute of Technology)

Read More