Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis

Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure is commonly investigated in related works - desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We propose to use data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show the proposed techniques work very well and improve the attack significantly, even for an order of magnitude

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces

Deep Learning based Side Channel Attacks in Practice

A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The advantage of this new profiling approach is twofold: $(1)$ the approximation of the information leakage by a multivariate Gaussian distribution is relaxed (leading to a more generic approach) and $(2)$ the pre-processing phases such as the traces realignment or the selection of the Points of Interest (PoI) are no longer mandatory, in some cases, to succeed the key recovery (leading to a less complex security evaluation roadmap). The related published works have demonstrated that Deep Learning based Side-Channel Attacks (DL-SCA) are very efficient when targeting cryptographic implementations protected with the common side-channel countermeasures such as masking, jitter and random delays insertion. In this paper, we assess the efficiency of this new profiling attack under different realistic and practical scenarios. First, we study the impact of the intrinsic characteristics of the manipulated data-set (\emph{i.e.} distance in time samples between the PoI, the dimensionality of the area of interest and the pre-processing of the data) on the robustness of the attack. We demonstrate that the deep learning techniques are sensitive to these parameters and we suggest some practical recommendations that can be followed to enhance the profiling and the key recovery phases. Second, we discuss the tolerance of DL-SCA with respect to a deviation from the idealized leakage models and provide a comparison with the well-known stochastic attack. Our results show that DL-SCA are still efficient in such a context. Then, we target a more complex masking scheme based on Shamir\u27s secret sharing and prove that this new profiling approach is still performing well. Finally, we conduct a security evaluation of a batch of several combinations of side-channel protections using simulations and real traces captured on the ChipWhisperer board. The experimental results obtained confirm that DL-SCA are very efficient even when a cryptographic implementation combines several side-channel countermeasures

Deep Learning-based Side Channel Attack on HMAC SM3

SM3 is a Chinese hash standard. HMAC SM3 uses a secret key to encrypt the input text and gives an output as the HMAC of the input text. If the key is recovered, adversaries can easily forge a valid HMAC. We can choose different methods, such as traditional side channel analysis, template attack-based side channel analysis to recover the secret key. Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis. In this paper, we try to recover the secret key with deep learning-based side channel analysis. We should train the network recursively for different parameters by using the same dataset and attack the target dataset with the trained network to recover different parameters. The experiment results show that the secret key can be recovered with deep learning-based side channel analysis. This work demonstrates the interests of this new method and show that this attack can be performed in practice

Hiding in Plain Sight: Non-profiling Deep Learning-based Side-channel Analysis with Plaintext/Ciphertext

Deep learning-based profiling side-channel analysis is widely adopted in academia and industry thanks to the ability to reveal secrets protected with countermeasures. To leverage its capability, the adversary needs to have access to a clone of an attack device to obtain the profiling measurements. Moreover, the adversary needs to know secret information to label these measurements. Non-profiling attacks avoid those constraints by not relying on secret information to label data but rather by trying all key guesses and taking the most successful one. Deep learning approaches also form the basis of several non-profiling attacks. Unfortunately, such approaches suffer from high computational complexity and low generality when applied in practice. This paper proposes a novel non-profiling deep learning-based side-channel analysis technique. Our approach relies on the fact that there is (commonly) a bijective relationship between known information, such as plaintext and ciphertext, and secret information. We use this fact to label the leakage measurement with the known information and then mount attacks. Our results show that we reach at least $3\times$ better attack performance with negligible computational effort than existing non-profiling methods. Moreover, our non-profiling approach rivals the performance of state-of-the-art deep learning-based profiling attacks