Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms

2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei
Keyword(s):  
Deep Learning ◽  
Elliptic Curve ◽  
Smart Cities ◽  
Information Leakage ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Public Key Cryptosystems ◽  
Elliptic Curve Cryptosystems ◽  
Hardware Implementations ◽  
Substantial Progress

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.

scholarly journals Automatic Generation of HCCA Resistant Scalar Multiplication Algorithm by Proper Sequencing of Field Multiplier Operands

10.29007/qszz ◽  
2018 ◽  
Author(s):  
Poulami Das ◽  
Debapriya Basu Roy ◽  
Debdeep Mukhopadhyay
Keyword(s):  
Correlation Analysis ◽  
Elliptic Curve ◽  
Power Analysis ◽  
Experimental Validation ◽  
Information Leakage ◽  
Automatic Generation ◽  
Side Channel ◽  
Multiplication Algorithm ◽  
Elliptic Curve Cryptosystems ◽  
First Time

Horizontal collision correlation analysis (HCCA) imposes a serious threat tosimple power analysis resistant elliptic curve cryptosystems involving unified algorithms, for e.g. Edward curve unified formula. This attack can be mounted even in presence of differential power analysis resistant randomization schemes. In this paper we have designed an effective countermeasure for HCCA protection, where the dependency of side-channel leakage from a school-book multiplication with the underling multiplier operands is investigated. We have shown how changing the sequence in which the operands are passed to the multiplication algorithm introduces dissimilarity in the information leakage. This disparity has been utilized in constructing a zero-cost countermeasure against HCCA. This countermeasure has been shown to help in HCCA resistivity. Additionally we provide experimental validation for our proposed countermeasure technique on a SASEBO platform. To the best of our knowledge, this is the first time that asymmetry in information leakage has been utilized in designing a side channel countermeasure and successfully applied in an ECC-based crypto-module.

scholarly journals Keep it Unsupervised: Horizontal Attacks Meet Deep Learning

2020 ◽  
pp. 343-372
Author(s):  
Guilherme Perin ◽  
Łukasz Chmielewski ◽  
Lejla Batina ◽  
Stjepan Picek
Keyword(s):  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Public Key Cryptosystems ◽  
Attack Model ◽  
Private Key ◽  
Single Trace ◽  
Iterative Framework

To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a “scalar blinding” is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a single trace still pose serious threats to protected ECC or RSA implementations. If the secrets learned through a single-trace attack contain too many wrong (or noisy) bits, the cryptanalysis methods for recovering remaining bits become impractical due to time and computational constraints. This paper proposes a deep learning-based framework to iteratively correct partially correct private keys resulting from a clustering-based horizontal attack. By testing the trained network on scalar multiplication (or exponentiation) traces, we demonstrate that a deep neural network can significantly reduce the number of wrong bits from randomized scalars (or exponents).When a simple horizontal attack can recover around 52% of attacked multiple private key bits, the proposed iterative framework improves the private key accuracy to above 90% on average and to 100% for at least one of the attacked keys. Our attack model remains fully unsupervised and excludes the need to know where the error or noisy bits are located in each separate randomized private key.

Parallel crypto-devices for GF(p) elliptic curve multiplication resistant against side channel attacks

2009 ◽  
Vol 35 (2) ◽  
pp. 329-338 ◽  
Author(s):  
Santosh Ghosh ◽  
Monjur Alam ◽  
Dipanwita Roy Chowdhury ◽  
Indranil Sen Gupta
Keyword(s):  
Elliptic Curve ◽  
Side Channel ◽  
Side Channel Attacks

scholarly journals Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis

2019 ◽  
pp. 107-131 ◽  
Author(s):  
Benjamin Timon
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Deep Learning ◽  
New Method ◽  
Invariance Property ◽  
Side Channel ◽  
Translation Invariance ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Learning Techniques

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis [MPP16]. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [CDP17] against de-synchronized traces also during Non-Profiled side-channel attacks. In this case, we show that this method can outperform classic Non-Profiled attacks such as Correlation Power Analysis. We also highlight that it is possible to break masked implementations in black-box, without leakages combination pre-preprocessing and with no assumptions nor knowledge about the masking implementation. To carry the attack, we introduce metrics based on Sensitivity Analysis that can reveal both the secret key value as well as points of interest, such as leakages and masks locations in the traces. The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.

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