scholarly journals Machine learning in side-channel analysis: a first study

2011 ◽  
Vol 1 (4) ◽  
pp. 293-302 ◽  
Author(s):  
Gabriel Hospodar ◽  
Benedikt Gierlichs ◽  
Elke De Mulder ◽  
Ingrid Verbauwhede ◽  
Joos Vandewalle
Keyword(s):  
Machine Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

Side-channel analysis and machine learning: A practical perspective

2017 ◽  
Author(s):  
Stjepan Picek ◽  
Annelie Heuser ◽  
Alan Jovic ◽  
Simone A. Ludwig ◽  
Sylvain Guilley ◽  
...  
Keyword(s):  
Machine Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Systematic Side-Channel Analysis of Curve25519 with Machine Learning

2020 ◽  
Vol 4 (4) ◽  
pp. 314-328
Author(s):  
Léo Weissbart ◽  
Łukasz Chmielewski ◽  
Stjepan Picek ◽  
Lejla Batina
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Side Channel ◽  
Single Measurement ◽  
Excellent Performance ◽  
Side Channel Analysis ◽  
Learning Techniques ◽  
Attack Phase ◽  
Channel Analysis ◽  
Symmetric Key

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.

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

2018 ◽  
pp. 209-237 ◽  
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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