119 research outputs found
Smart Card Fault Injections with High Temperatures
Power and clock glitch attacks on smart cards can help an attacker to discover some internal
secrets or bypass certain security checks. Also, an attacker can manipulate the temperature and supply voltage
of the device, thus making the device glitch more easily. If these manipulations are within the device operating
conditions, it becomes harder to distinguish between an extreme condition from an attacker. To demonstrate
temperature and power supply effect on fault attacks, we perform several tests on an Atmega 163 microcontroller
in different conditions. Our results show that this kind of attacks are still a serious threat to small devices,
whilst maintaining the manufacturer recommendations
CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information
Machine learning has become mainstream across industries. Numerous examples
proved the validity of it for security applications. In this work, we
investigate how to reverse engineer a neural network by using only power
side-channel information. To this end, we consider a multilayer perceptron as
the machine learning architecture of choice and assume a non-invasive and
eavesdropping attacker capable of measuring only passive side-channel leakages
like power consumption, electromagnetic radiation, and reaction time.
We conduct all experiments on real data and common neural net architectures
in order to properly assess the applicability and extendability of those
attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our
experiments show that the side-channel attacker is capable of obtaining the
following information: the activation functions used in the architecture, the
number of layers and neurons in the layers, the number of output classes, and
weights in the neural network. Thus, the attacker can effectively reverse
engineer the network using side-channel information.
Next, we show that once the attacker has the knowledge about the neural
network architecture, he/she could also recover the inputs to the network with
only a single-shot measurement. Finally, we discuss several mitigations one
could use to thwart such attacks.Comment: 15 pages, 16 figure
Secure and Efficient RNS Approach for Elliptic Curve Cryptography
Scalar multiplication, the main operation in elliptic
curve cryptographic protocols, is vulnerable to side-channel
(SCA) and fault injection (FA) attacks. An efficient countermeasure
for scalar multiplication can be provided by using alternative
number systems like the Residue Number System (RNS). In RNS,
a number is represented as a set of smaller numbers, where each
one is the result of the modular reduction with a given moduli
basis. Under certain requirements, a number can be uniquely
transformed from the integers to the RNS domain (and vice
versa) and all arithmetic operations can be performed in RNS.
This representation provides an inherent SCA and FA resistance
to many attacks and can be further enhanced by RNS arithmetic
manipulation or more traditional algorithmic countermeasures.
In this paper, extending our previous work, we explore the
potentials of RNS as an SCA and FA countermeasure and provide
an description of RNS based SCA and FA resistance means. We
propose a secure and efficient Montgomery Power Ladder based
scalar multiplication algorithm on RNS and discuss its SCAFA
resistance. The proposed algorithm is implemented on an
ARM Cortex A7 processor and its SCA-FA resistance is evaluated
by collecting preliminary leakage trace results that validate our
initial assumptions
Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis
Despite considerable achievements of deep learning-based side-channel analysis, overfitting represents a significant obstacle in finding optimized neural network models. This issue is not unique to the side-channel domain. Regularization techniques are popular solutions to overfitting and have long been used in various domains.
At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these techniques\u27 effectiveness. In this paper, we aim to investigate the regularization effectiveness on a randomly selected model, by applying four powerful and easy-to-use regularization techniques to eight combinations of datasets, leakage models, and deep learning topologies.
The investigated techniques are , , dropout, and early stopping. Our results show that while all these techniques can improve performance in many cases, and are the most effective.
Finally, if training time matters, early stopping is the best technique
The uncertainty of Side-Channel Analysis: A way to leverage from heuristics
Performing a comprehensive side-channel analysis evaluation of small embedded
devices is a process known for its variability and complexity. In real-world
experimental setups, the results are largely influenced by a huge amount of
parameters that are not easily adjusted without trial and error and are heavily
relying on the experience of professional security analysts. In this paper, we
advocate the use of an existing statistical methodology called Six Sigma
(6{\sigma}) for side-channel analysis optimization for this purpose. This
well-known methodology is commonly used in other industrial fields, such as
production and quality engineering, to reduce the variability of industrial
processes. We propose a customized Six Sigma methodology, which enables even a
less-experienced security analysis to select optimal values for the different
variables that are critical for the side-channel analysis procedure. Moreover,
we show how our methodology helps in improving different phases in the
side-channel analysis process.Comment: 30 pages, 8 figure
Auto-tune POIs: Estimation of distribution algorithms for efficient side-channel analysis
Due to the constant increase and versatility of IoT devices that should keep
sensitive information private, Side-Channel Analysis (SCA) attacks on embedded
devices are gaining visibility in the industrial field. The integration and
validation of countermeasures against SCA can be an expensive and cumbersome
process, especially for the less experienced ones, and current certification
procedures require to attack the devices under test using multiple SCA
techniques and attack vectors, often implying a high degree of complexity. The
goal of this paper is to ease one of the most crucial and tedious steps of
profiling attacks i.e. the points of interest (POI) selection and hence assist
the SCA evaluation process. To this end, we introduce the usage of Estimation
of Distribution Algorithms (EDAs) in the SCA field in order to automatically
tune the point of interest selection. We showcase our approach on several
experimental use cases, including attacks on unprotected and protected AES
implementations over distinct copies of the same device, dismissing in this way
the portability issue
Toward Practical Autoencoder-based Side-Channel Analysis Evaluations
This paper introduces a practical evaluation procedure based on autoencoders for profiled side-channel analysis evaluations. An autoencoder is a learning model able to pre-process leakage traces improving in this way the guessing entropy. Nevertheless, this learning model\u27s design should aim to code the leakage distribution to avoid relevant information being removed. For this reason, we propose an autoencoder built upon dilated convolutions. When using these learning models, the evaluation produces new assets, e.g., new versions of the dataset and new models based on learning algorithms. Our procedure comprises meaningful metrics and visualization techniques, namely signal-to-noise ratio and weight visualization, to evaluate those assets\u27 effectiveness. After applying our procedure and our new autoencoder architecture to the ASCAD random key database, our results outperform state-of-the-art
CNN architecture extraction on edge GPU
Neural networks have become popular due to their versatility and
state-of-the-art results in many applications, such as image classification,
natural language processing, speech recognition, forecasting, etc. These
applications are also used in resource-constrained environments such as
embedded devices. In this work, the susceptibility of neural network
implementations to reverse engineering is explored on the NVIDIA Jetson Nano
microcomputer via side-channel analysis. To this end, an architecture
extraction attack is presented. In the attack, 15 popular convolutional neural
network architectures (EfficientNets, MobileNets, NasNet, etc.) are implemented
on the GPU of Jetson Nano and the electromagnetic radiation of the GPU is
analyzed during the inference operation of the neural networks. The results of
the analysis show that neural network architectures are easily distinguishable
using deep learning-based side-channel analysis.Comment: Will appear at the AIHWS 2024 workshop at ACNS 202
Near Collision Side Channel Attacks
Side channel collision attacks are a powerful method to exploit side channel leakage. Otherwise than a few exceptions, collision attacks usually combine leakage from distinct points in time, making them inherently bivariate. This work introduces the notion of near collisions to exploit the fact that values depending on the same sub-key can have similar while not identical leakage. We show how such knowledge can be exploited to mount a key recovery attack. The presented approach has several desirable features when compared to other state-of-the-art collision attacks:
Near collision attacks are truly univariate. They have low requirements on the leakage functions, since they work well for leakages that are linear in the bits of the targeted intermediate state. They are applicable in the presence of masking countermeasures if there exist distinguishable leakages, as in the case of leakage squeezing.
Results are backed up by a broad range of simulations for unprotected and masked implementations, as well as an analysis of the measurement set provided by DPA Contest v4
Deep neural networks aiding cryptanalysis: A case study of the Speck distinguisher
At CRYPTO\u2719, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear, leading us to investigate whether a smaller and/or better performing neural distinguisher exists. This paper studies the depth-10 and depth-1 neural distinguishers proposed by Gohr with the aim of finding out whether smaller or better-performing distinguishers for Speck32/64 exist.
We first evaluate whether we can find smaller neural networks that match the accuracy of the proposed distinguishers. We answer this question in affirmative with the depth-1 distinguisher successfully pruned, resulting in a network that remained within one percentage point of the unpruned network\u27s performance. Having found a smaller network that achieves the same performance, we examine if its performance can be improved as well. We also study whether processing the input before giving it to the pruned depth-1 network would improve its performance. To this end, convolutional autoencoders were found that managed to reconstruct the ciphertext pairs successfully, and their trained encoders were used as a preprocessor before training the pruned depth-1 network. We found that, even though the autoencoders achieve a perfect reconstruction, the pruned network did not have the necessary complexity anymore to extract useful information from the preprocessed input, motivating us to look at the feature importance to get more insights. To achieve this, we used LIME, with results showing that a stronger explainer is needed to assess it correctly
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