EE Seminar: Estimating the security level of cryptographic keys against side-channel attacks and using it to estimate a password strength
https://us02web.zoom.us/j/88951206317?pwd=elpRWVRaUG5xUGpQaC9QRU5SUHI3UT09
Meeting ID: 889 5120 6317
Passcode: 476348
Speaker: Liron David
Ph.D student under the supervision of Prof. Avishai Wool
Wednesday, November 18th, 2020, at 15:00
Estimating the security level of cryptographic keys against side-channel attacks and using it to estimate a password strength
Abstract
Efficient rank estimation algorithms are of prime interest in security evaluation against side-channel attacks (SCA). They allow estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over d subkeys (usually key bytes).
In this talk, I will show a novel rank estimation called ESrank. This is the first rank estimation algorithm with a bounded error ratio, which can be tuned to the desired accuracy. Its error ratio is bounded by g2d-2, for any probability distribution, where d is the number of subkey dimensions and g>1 can be chosen according to the desired accuracy. ESrank is also the first rank estimation algorithm that enjoys provable poly-logarithmic time- and space-complexity. The ESrank's main idea is to use exponential sampling to drastically reduce the algorithm's complexity.
Then I will show a novel password strength estimator based on ESrank, called PESrank, which accurately models the behavior of a powerful password cracker. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password. PESrank calculates the rank of a given password in an optimal descending order of likelihood in fractions of a second---without actually enumerating the passwords---so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password.
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