22,760 research outputs found
NASCTY: Neuroevolution to Attack Side-channel Leakages Yielding Convolutional Neural Networks
Side-channel analysis (SCA) can obtain information related to the secret key
by exploiting leakages produced by the device. Researchers recently found that
neural networks (NNs) can execute a powerful profiling SCA, even on targets
protected with countermeasures. This paper explores the effectiveness of
Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural
Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic
operators on architectures' hyperparameters to produce CNNs for side-channel
analysis automatically. The results indicate that we can achieve performance
close to state-of-the-art approaches on desynchronized leakages with mask
protection, demonstrating that similar neuroevolution methods provide a solid
venue for further research. Finally, the commonalities among the constructed
NNs provide information on how NASCTY builds effective architectures and deals
with the applied countermeasures.Comment: 19 pages, 6 figures, 4 table
Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis
Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered
Improved Study of Side-Channel Attacks Using Recurrent Neural Networks
Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information.
In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs):
RQ1: Is it possible to predict the unknown cryptographic algorithm using neural network models from different datasets?
RQ2: Is it possible to map the key value for the specific plaintext-ciphertext pair with or without side-band information?
RQ3: Using similar hyper-parameters, can we evaluate the performance of two neural network models (CNN vs. RNN)?
In answering the questions, we have worked with two different datasets: one is a physical dataset (DPA contest v1 dataset), and the other one is simulated dataset (toggle count quantity) from Verilog HDL. We have evaluated the efficiency of CNN and RNN models in predicting the unknown cryptographic algorithms of the device under attack. We have mapped to 56 bits key for a specific plaintext-ciphertext pair with and without using side-band information. Finally, we have evaluated vi our neural network models using different metrics such as accuracy, loss, baselines, epochs, speed of operation, memory space consumed, and so on. We have shown the performance comparison between RNN and CNN on different datasets. We have done three experiments and shown our results on these three experiments. The first two experiments have shown the advantages of choosing CNN over RNN while working with side-channel datasets. In the third experiment, we have compared two RNN models on the same datasets but different dimensions of the datasets
Resolving the Doubts: On the Construction and Use of ResNets for Side-channel Analysis
The deep learning-based side-channel analysis gave some of the most prominent side-channel attacks against protected targets in the past few years.
To this end, the research community\u27s focus has been on creating 1) powerful and 2) (if possible) minimal multilayer perceptron or convolutional neural network architectures. Currently, we see that computationally intensive hyperparameter tuning methods (e.g., Bayesian optimization or reinforcement learning) provide the best results. However, as targets with more complex countermeasures become available, these minimal architectures may be insufficient, and we will require novel deep learning approaches.
This work explores how residual neural networks (ResNets) perform in side-channel analysis and how to construct deeper ResNets capable of working with larger input sizes and requiring minimal tuning.
The resulting architectures obtained by following our guidelines are significantly deeper than commonly seen in side-channel analysis, require minimal hyperparameter tuning for specific datasets, and offer competitive performance with state-of-the-art methods across several datasets. Additionally, the results indicate that ResNets work especially well when the number of profiling traces and features in a trace is large
What Can Help Pedestrian Detection?
Aggregating extra features has been considered as an effective approach to
boost traditional pedestrian detection methods. However, there is still a lack
of studies on whether and how CNN-based pedestrian detectors can benefit from
these extra features. The first contribution of this paper is exploring this
issue by aggregating extra features into CNN-based pedestrian detection
framework. Through extensive experiments, we evaluate the effects of different
kinds of extra features quantitatively. Moreover, we propose a novel network
architecture, namely HyperLearner, to jointly learn pedestrian detection as
well as the given extra feature. By multi-task training, HyperLearner is able
to utilize the information of given features and improve detection performance
without extra inputs in inference. The experimental results on multiple
pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
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