Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits
Type
Conference Object
Change log
Authors
Choudary, MO
Kuhn, MG
Abstract
Template attacks and stochastic models are among the most powerful side-channel attacks. However, they can be computationally expensive when processing a large number of samples. Various compression techniques have been used very successfully to reduce the data dimensionality prior to applying template attacks, most notably Principal Component Analysis (PCA) and Fisher’s Linear Discriminant Analysis (LDA). These make the attacks more efficient computationally and help the profiling phase to converge faster. We show how these ideas can also be applied to implement stochastic models more efficiently, and we also show that they can be applied and evaluated even for more than eight unknown data bits at once.
Description
Keywords
Side-channel attacks, Template attack, Stochastic model, PCA, LDA
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name
Journal ISSN
0302-9743
1611-3349
1611-3349
Volume Title
8968
Publisher
Springer International Publishing