Systematic Side-Channel Analysis of Curve25519 with Machine Learning

AbstractProfiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning techniques to mount side-channel attacks on two implementations of scalar multiplication on the elliptic curve Curve25519. The first implementation follows the baseline implementation with complete formulae as used for EdDSA in WolfSSl, where we exploit power consumption as a side-channel. The second implementation features several countermeasures, and in this case, we analyze electromagnetic emanations to find side-channel leakage. Most techniques considered in this work result in potent attacks, and especially the method of choice appears to be convolutional neural networks (CNNs), which can break the first implementation with only a single measurement in the attack phase. The same convolutional neural network demonstrated excellent performance for attacking AES cipher implementations. Our results show that some common grounds can be established when using deep learning for profiling attacks on very different cryptographic algorithms and their corresponding implementations.

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Reverse engineering smart card malware using side channel analysis with machine learning techniques

2016 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2016.7841039 ◽

2016 ◽

Cited By ~ 2

Author(s):

Hippolyte Djonon Tsague ◽

Bheki Twala

Keyword(s):

Machine Learning ◽

Reverse Engineering ◽

Smart Card ◽

Machine Learning Techniques ◽

Side Channel ◽

Side Channel Analysis ◽

Learning Techniques ◽

Channel Analysis

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The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations

IACR Transactions on Cryptographic Hardware and Embedded Systems ◽

10.46586/tches.v2019.i1.209-237 ◽

2018 ◽

pp. 209-237 ◽

Cited By ~ 1

Author(s):

Stjepan Picek ◽

Annelie Heuser ◽

Alan Jovic ◽

Shivam Bhasin ◽

Francesco Regazzoni

Keyword(s):

Machine Learning ◽

Hamming Distance ◽

Class Imbalance ◽

Imbalanced Data ◽

Machine Learning Techniques ◽

Side Channel ◽

Side Channel Analysis ◽

Learning Techniques ◽

Channel Analysis ◽

Learning Metrics

We concentrate on machine learning techniques used for profiled sidechannel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.

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It Started with Templates: The Future of Profiling in Side-Channel Analysis

Security of Ubiquitous Computing Systems ◽

10.1007/978-3-030-10591-4_8 ◽

2021 ◽

pp. 133-145

Author(s):

Lejla Batina ◽

Milena Djukanovic ◽

Annelie Heuser ◽

Stjepan Picek

Keyword(s):

Machine Learning ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Side Channel ◽

Side Channel Attacks ◽

Worst Case ◽

Learning Techniques ◽

The Future ◽

First Results ◽

Channel Analysis

AbstractSide-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.

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