scholarly journals Practical Aspects on Non-profiled Deep-learning Side-channel Attacks against AES Software Implementation with Two Types of Masking Countermeasures including RSM

2021 ◽  
Author(s):  
Kunihiro Kuroda ◽  
Yuta Fukuda ◽  
Kota Yoshida ◽  
Takeshi Fujino
Keyword(s):  
Deep Learning ◽  
Software Implementation ◽  
Side Channel ◽  
Side Channel Attacks

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 Power-Balancing Software Implementation to Mitigate Side-Channel Attacks without Using Look-Up Tables

2020 ◽  
Vol 10 (7) ◽  
pp. 2454
Author(s):  
HanBit Kim ◽  
HeeSeok Kim ◽  
Seokhie Hong
Keyword(s):  
Software Implementation ◽  
Side Channel ◽  
Random Numbers ◽  
Hamming Weight ◽  
Side Channel Attacks ◽  
Power Balancing

With the increasing number of side-channel attacks, countermeasure designers continue to develop various implementations to address such threats. Power-balancing (PB) methods hold the number of 1s and/or transitions (i.e., Hamming weight/distance) of internal processes constant to ensure side-channel safety in an environment in which it is difficult to use random numbers. Most existing studies employed look-up tables (LUTs) to compute those operations, except for XOR and NOT operations. However, LUT-based schemes exhibit some side-channel issues in the address bits of LUTs. In this paper, we propose the application of AND and ADD operations to PB methods based on a rule that encodes 8-bit data into a 32-bit codeword without using LUTs. Unlike previous studies that employed LUTs, our proposals overcome side-channel vulnerabilities associated with the address bits and memory wastage. In addition, we evaluate the side-channel security ensured by the proposed method in comparison with that ensured by other methods. Finally, we apply our methods to SIMON/SPECK ciphers and analyze their performance by comparing them with older schemes.

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.

scholarly journals Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks

2021 ◽  
pp. 60-96
Author(s):  
Gabriel Zaid ◽  
Lilian Bossuet ◽  
Amaury Habrard ◽  
Alexandre Venelli
Keyword(s):  
Deep Learning ◽  
Model Performance ◽  
Public Key ◽  
Side Channel ◽  
Ensemble Model ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Ensemble Models ◽  
Remaining Time ◽  
Multiple Scenarios

Deep Learning based Side-Channel Attacks (DL-SCA) are considered as fundamental threats against secure cryptographic implementations. Side-channel attacks aim to recover a secret key using the least number of leakage traces. In DL-SCA, this often translates in having a model with the highest possible accuracy. Increasing an attack’s accuracy is particularly important when an attacker targets public-key cryptographic implementations where the recovery of each secret key bits is directly related to the model’s accuracy. Commonly used in the deep learning field, ensemble models are a well suited method that combine the predictions of multiple models to increase the ensemble accuracy by reducing the correlation between their errors. Linked to this correlation, the diversity is considered as an indicator of the ensemble model performance. In this paper, we propose a new loss, namely Ensembling Loss (EL), that generates an ensemble model which increases the diversity between the members. Based on the mutual information between the ensemble model and its related label, we theoretically demonstrate how the ensemble members interact during the training process. We also study how an attack’s accuracy gain translates to a drastic reduction of the remaining time complexity of a side-channel attacks through multiple scenarios on public-key implementations. Finally, we experimentally evaluate the benefits of our new learning metric on RSA and ECC secure implementations. The Ensembling Loss increases by up to 6.8% the performance of the ensemble model while the remaining brute-force is reduced by up to 222 operations depending on the attack scenario.

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.

Sign in / Sign up

Export Citation Format

Share Document