Vik Vanderlinden, Wouter Joosen, Mathy Vanhoef (imec-DistriNet, KU Leuven)

Performing a remote timing attack typically entails the collection of many timing measurements in order to overcome noise due to network jitter. If an attacker can reduce the amount of jitter in their measurements, they can exploit timing leaks using fewer measurements. To reduce the amount of jitter, an attacker may use timing information that is made available by a server. In this paper, we exploit the use of the server-timing header, which was created for performance monitoring and in some cases exposes millisecond accurate information about server-side execution times. We show that the header is increasingly often used, with an uptick in adoption rates in recent months. The websites that use the header often host dynamic content of which the generation time can potentially leak sensitive information. Our new attack techniques, one of which collects the header timing values from an intermediate proxy, improve performance over standard attacks using roundtrip times. Experiments show that, overall, our new attacks (significantly) decrease the number of samples required to exploit timing leaks. The attack is especially effective against geographically distant servers.

View More Papers

Semi-Automated Synthesis of Driving Rules

Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

Read More

Adversarial Robustness for Tabular Data through Cost and Utility...

Klim Kireev (EPFL), Bogdan Kulynych (EPFL), Carmela Troncoso (EPFL)

Read More

AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot...

Hossein Fereidooni (Technical University of Darmstadt), Jan Koenig (University of Wuerzburg), Phillip Rieger (Technical University of Darmstadt), Marco Chilese (Technical University of Darmstadt), Bora Goekbakan (KOBIL, Germany), Moritz Finke (University of Wuerzburg), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More

REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder...

Wenjie Qu (Huazhong University of Science and Technology), Jinyuan Jia (University of Illinois Urbana-Champaign), Neil Zhenqiang Gong (Duke University)

Read More