Fabian Fäßler's research while affiliated with Technische Universität Berlin and other places
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Publications (3)
Although numerous attacks revealed the vulnerability of different PUF families to non-invasive Machine Learning (ML) attacks, the question is still open whether all PUFs might be learnable. Until now, virtually all ML attacks rely on the assumption that a mathematical model of the PUF functionality is known a priori. However, this is not always the...
Although numerous attacks revealed the vulnerability of different PUF families to non-invasive Machine Learning (ML) attacks, the question is still open whether all PUFs might be learnable. Until now, virtually all ML attacks rely on the assumption that a mathematical model of the PUF functionality is known a priori. However, this is not always the...
Citations
... We showed that, under the assumption that the nonlinear effects were relatively small, the PUF still acts as a polynomial in the challenge components b i , with the degree of the polynomial determined by the highest order of polarization susceptibility, and thus can be learned with access to polynomially many CRPs in polynomial time (Eqs. 17,18). ...
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... The security analysis of these advanced applications and protocols 2 relies on assuming that a PUF behaves like a random oracle; upon receiving a challenge, a uniform random response with replacement is selected, measurement noise is added, and the resulting response is returned. This assumption turns out to be too strong because (1) in practical implementations, the PUF returns biased response bits, and (2) classical ML and advanced ML attacks [11][12][13][14][15][16][17] demonstrate that a prediction model for response bits with accuracy typically up to 75% can be trained and this defeats the random oracle assumption. For example, FPGA implementations of the interpose PUF in [18] showed that the bias of individual Arbiter PUFs ranges from 50.2% to 61.6%. ...
