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Pregled bibliografske jedinice broj: 940316

Statistical Model Checking of Guessing and Timing Attacks on Distance-bounding Protocols


Alturki, M.A.; Kanovich, Max; Ban Kirigin, Tajana; Nigam, Vivek; Scedrov, Andre; Talcott, Carolyn
Statistical Model Checking of Guessing and Timing Attacks on Distance-bounding Protocols // Workshop on Foundations of Computer Security 2018
Oxford, Velika Britanija, 2018. (predavanje, međunarodna recenzija, neobjavljeni rad, znanstveni)


Naslov
Statistical Model Checking of Guessing and Timing Attacks on Distance-bounding Protocols

Autori
Alturki, M.A. ; Kanovich, Max ; Ban Kirigin, Tajana ; Nigam, Vivek ; Scedrov, Andre ; Talcott, Carolyn

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Skup
Workshop on Foundations of Computer Security 2018

Mjesto i datum
Oxford, Velika Britanija, 8.7.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Distance-bounding protocols, Distance fraud, Probabilistic rewriting, Statistical model checking, MAUDE

Sažetak
Distance-bounding (DB) protocols were proposed to thwart relay attacks on proximity-based access control systems. In a DB protocol, the verifier computes an upper bound on the distance to the prover by measuring the time needed for a signal to travel to the prover and back. DB protocols are, however, vulnerable to distance fraud, in which a dishonest prover is able to manipulate the distance bound computed by an honest verifier. Despite their conceptual simplicity, devising a formal characterization of DB protocols and distance fraud attacks that is amenable to automated formal analysis is non-trivial, primarily because of their real- time and probabilistic nature. In this work, we present a framework, based on rewriting logic, for formally analyzing different forms of distance-fraud, including recently identified timing attacks. We introduce a generic, real- time and probabilistic model of DB protocols and use it to (mechanically) verify false- acceptance and false-rejection probabilities under various settings and attacker models through statistical model checking with MAUDE and PVeStA. Using this framework, we first accurately confirm known results and then define and quantitatively evaluate new guessing-ahead attack strategies that would otherwise be difficult to analyze manually.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-UIP-05-2017-9219

Ustanove
Sveučilište u Rijeci - Odjel za matematiku

Autor s matičnim brojem:
Tajana Ban-Kirigin, (229313)