Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

Maximilian Weininger; Kush Grover; Shruti Misra; Jan Kretinsky

doi:10.1109/CDC45484.2021.9683447

Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

Maximilian Weininger, Kush Grover, Shruti Misra, Jan Kretinsky

Informatics 7 - Associate Professorship of Formal Methods for Software Reliability

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	60th IEEE Conference on Decision and Control, CDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3786-3793
Number of pages	8
ISBN (Electronic)	9781665436595
DOIs	https://doi.org/10.1109/CDC45484.2021.9683447
State	Published - 2021
Event	60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States Duration: 13 Dec 2021 → 17 Dec 2021

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2021-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	60th IEEE Conference on Decision and Control, CDC 2021
Country/Territory	United States
City	Austin
Period	13/12/21 → 17/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CDC45484.2021.9683447

Cite this

Weininger, M., Grover, K., Misra, S., & Kretinsky, J. (2021). Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games. In 60th IEEE Conference on Decision and Control, CDC 2021 (pp. 3786-3793). (Proceedings of the IEEE Conference on Decision and Control; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC45484.2021.9683447

@inproceedings{ed0f8fa8611c4783a13c4ed85f4ad16c,

title = "Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games",

abstract = "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.",

author = "Maximilian Weininger and Kush Grover and Shruti Misra and Jan Kretinsky",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",

year = "2021",

doi = "10.1109/CDC45484.2021.9683447",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3786--3793",

booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",

}

Weininger, M, Grover, K, Misra, S & Kretinsky, J 2021, Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games. in 60th IEEE Conference on Decision and Control, CDC 2021. Proceedings of the IEEE Conference on Decision and Control, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 3786-3793, 60th IEEE Conference on Decision and Control, CDC 2021, Austin, United States, 13/12/21. https://doi.org/10.1109/CDC45484.2021.9683447

Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games. / Weininger, Maximilian; Grover, Kush; Misra, Shruti et al.
60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3786-3793 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

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AU - Grover, Kush

AU - Misra, Shruti

AU - Kretinsky, Jan

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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U2 - 10.1109/CDC45484.2021.9683447

DO - 10.1109/CDC45484.2021.9683447

M3 - Conference contribution

AN - SCOPUS:85126025920

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 3786

EP - 3793

BT - 60th IEEE Conference on Decision and Control, CDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 60th IEEE Conference on Decision and Control, CDC 2021

Y2 - 13 December 2021 through 17 December 2021

ER -

Guaranteed Trade-Offs in Dynamic Information Flow Tracking Games

Abstract

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UN SDGs

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