Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

WEININGER, Maximilian; Kush GROVER; Shruti MISRA a Jan KŘETÍNSKÝ. Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games. In 2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, December 14-17, 2021. IEEE, 2021, s. 3786-3793. ISBN 9781665436595. Dostupné z: https://doi.org/10.1109/CDC45484.2021.9683447.

Základní údaje

Originální název

Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

Autoři

WEININGER, Maximilian; Kush GROVER; Shruti MISRA a Jan KŘETÍNSKÝ

Vydání

2021 60th IEEE Conference on Decision and Control (CDC), Austin, TX, USA, December 14-17, 2021, od s. 3786-3793, 8 s. 2021

Nakladatel

IEEE

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Další údaje

Typ výsledku

Stať ve sborníku

Označené pro přenos do RIV

Ne

Organizační jednotka

Fakulta informatiky

ISBN

9781665436595

ISSN

DOI

https://doi.org/10.1109/CDC45484.2021.9683447

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Anotace

V originále

We consider security risks in the form of advanced persistent threats (APTs) and their detection using dynamic information flow tracking (DIFT). We model the tracking and the detection as a stochastic game between the attacker and the defender. Compared to the state of the art, our approach applies to a wider set of scenarios with arbitrary (not only acyclic) information-flow structure. Moreover, multidimensional rewards allow us to formulate and answer questions related to trade-offs between resource efficiency of the tracking and efficacy of the detection. Finally, our algorithm provides results with probably approximately correct (PAC) guarantees, in contrast to previous (possibly arbitrarily imprecise) learning-based approaches.