Chapter 8
It Started with Templates: The Future
of Profiling in Side-Channel Analysis
Lejla Batina, Milena Djukanovic, Annelie Heuser, and Stjepan Picek
Abstract Side-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling
side-channel attacks has received a lot of attention in recent years due to the fact that
this type of attack defines the worst-case security assumptions. The SCA community
realized that the same approach is actually used in other domains in the form of
supervised machine learning. Consequently, some researchers started experimenting
with different machine learning techniques and evaluating their effectiveness in
the SCA context. More recently, we are witnessing an increase in the use of deep
learning techniques in the SCA community with strong first results in side-channel
analyses, even in the presence of countermeasures. In this chapter, we consider
the evolution of profiling attacks, and subsequently we discuss the impacts they
have made in the data preprocessing, feature engineering, and classification phases.
We also speculate on the future directions and the best-case consequences for the
security of small devices.
8.1 Introduction
In 1996, Kocher demonstrated the possibility to recover secret data by introducing
a method for exploiting leakages from the device under attack [338]. In other
words, implementations of cryptographic algorithms leak relevant information
L. Batina
Radboud University, Nijmegen, The Netherlands
M. Djukanovic
University of Montenegro, Podgorica, Montenegro
A. Heuser
Univ Rennes, Inria, CNRS, IRISA, Rennes, France
S. Picek ()
Delft University of Technology, Delft, The Netherlands
© The Author(s) 2021
G. Avoine, J. Hernandez-Castro (eds.), Security of Ubiquitous Computing Systems,
https://doi.org/10.1007/978-3-030-10591-4_8
133
It Started with Templates: The Future
of Profiling in Side-Channel Analysis
Lejla Batina, Milena Djukanovic, Annelie Heuser, and Stjepan Picek
Abstract Side-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling
side-channel attacks has received a lot of attention in recent years due to the fact that
this type of attack defines the worst-case security assumptions. The SCA community
realized that the same approach is actually used in other domains in the form of
supervised machine learning. Consequently, some researchers started experimenting
with different machine learning techniques and evaluating their effectiveness in
the SCA context. More recently, we are witnessing an increase in the use of deep
learning techniques in the SCA community with strong first results in side-channel
analyses, even in the presence of countermeasures. In this chapter, we consider
the evolution of profiling attacks, and subsequently we discuss the impacts they
have made in the data preprocessing, feature engineering, and classification phases.
We also speculate on the future directions and the best-case consequences for the
security of small devices.
8.1 Introduction
In 1996, Kocher demonstrated the possibility to recover secret data by introducing
a method for exploiting leakages from the device under attack [338]. In other
words, implementations of cryptographic algorithms leak relevant information
L. Batina
Radboud University, Nijmegen, The Netherlands
M. Djukanovic
University of Montenegro, Podgorica, Montenegro
A. Heuser
Univ Rennes, Inria, CNRS, IRISA, Rennes, France
S. Picek ()
Delft University of Technology, Delft, The Netherlands
© The Author(s) 2021
G. Avoine, J. Hernandez-Castro (eds.), Security of Ubiquitous Computing Systems,
https://doi.org/10.1007/978-3-030-10591-4_8
133