Template Attacks

By Suresh Chari, Josyula R. Rao, Pankaj Rohatgi. Workshop on Cryptographic Hardware and Embedded Systems, 2002

1,630 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/f7a0d42044b26be8d509310ba20fd8d665943eba

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Citations in the corpus

The World is Not Enough: Another Look on Second-Order DPA (2010, methodology): “This reference shows that in the context of (so-called) standard firstorder DPA and when provided with the same leakage model, the most popular distinguishers such as using distance-of-means tests [8], correlation analysis and Gaussian templates [3] require approximately the same number of traces to”
Mutual Information Analysis (2008, methodology): “difference of means [9], Pearson correlation coefficient [2], Bayesian classification [3], as a side-channel distinguisher.”
Trojan Detection using IC Fingerprinting (2007, background): “…initial attack techniques such as Differential Power/EM Analysis dealt with the problem of noise by averaging it out over multiple samples, later work on template attacks and its variants [7] [23] actually build statistical models for the noise and used them to classify individual noisy signals.”
Statistical Analysis of Second Order Differential Power Analysis (2009, methodology): “For further information about Profiling SCA, the different studies conducted, for instance, in [2], [3], [4] may be read.”
A Stochastic Model for Differential Side Channel Cryptanalysis (2005, background): “Probably the most sophisticated strategy is a template based attack ([4]) which aims to optimize Simple Power Analysis (SPA) and requires a precise characterization of the noise.”
Machine learning in side-channel analysis: a first study (2011, methodology): “[5] originally proposed the following method to choose the most relevant components of the power traces, as part of TAs: let Mi be the statistical average of the power traces associated with the subkey i , i = 1, .”
Breaking Cryptographic Implementations Using Deep Learning Techniques (2016, background): “The set of profiled attacks includes Template attacks [10] and Stochastic cryptanalyses (aka Linear Regression Analyses) [16,47,48].”
Template Attacks in Principal Subspaces (2006, background): “the time samples are considered independent) the classification errors reported by [2] were up to 35% for similar keys.”
Masking against Side-Channel Attacks: A Formal Security Proof (2013, background): “Note that in practice, power and electromagnetic leakages can realistically be modeled by a multivariate deterministic function g of the processed data with an additional multivariate Gaussian noise N [5,27,36,38].”
Introduction to differential power analysis (2011, background): “Template attacks [51,52] seek to make maximal use of a small number of traces from a target device.”
Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing (2017, methodology): “The potentially huge dimensionality of X lets such an estimation a very complex problem, and the most popular way adopted until now to estimate the conditional probability is the one that led to the well-established Gaussian TA [5] (aka Quadratic Discriminant Analysis [12]).”
Provably Secure Higher-Order Masking of AES (2010, background): “Some of these data are sensitive in the sense that they are related to the secret key, and the leaking information about them enables efficient key-recovery attacks [7, 19].”
On the Power of Power Analysis in the Real World: A Complete Break of the KeeLoqCode Hopping Scheme (2008, methodology): “in [3].”