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.

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Machine Unlearning of Features and Labels

Alexander Warnecke (TU Braunschweig), Lukas Pirch (TU Braunschweig), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Konrad Rieck (TU Braunschweig)

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DARWIN: Survival of the Fittest Fuzzing Mutators

Patrick Jauernig (Technical University of Darmstadt), Domagoj Jakobovic (University of Zagreb, Croatia), Stjepan Picek (Radboud University and TU Delft), Emmanuel Stapf (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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WIP: Augmenting Vehicle Safety With Passive BLE

Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

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Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University), Garikayi Madzudzo (Horiba Mira Ltd)

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