An Improved Side-Channel Attack Based on Support Vector Machine

Zhong, Zhang; Gu, Dawu; Liu, Junrong; Guo, Zheng

doi:10.1109/cis.2014.80

Cited by 11 publications

(5 citation statements)

References 15 publications

(20 reference statements)

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“…Support Vector Machine (SVM). Support Vector Machines are one of the most popular algorithms used for classification problems in different application domains, including side-channel analysis [19,33,57]. In SVM, n-dimensional data is separated using a hyperplane, by computing and adjusting the coefficients to find the maximum-margin hyperlane, which best separates the target classes.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine-Learning Assisted Side-Channel Attacks on RNS ECC Implementations Using Hybrid Feature Engineering

Mukhtar

Papachristodoulou

Fournaris

et al. 2022

Lecture Notes in Computer Science

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Machine learning-based side-channel attacks have recently been introduced to recover the secret information from protected software and hardware implementations. Limited research exists for publickey algorithms, especially on non-traditional implementations like those using Residue Number System (RNS). Template attacks were proven successful on RNS-based Elliptic Curve Cryptography (ECC), only if the aligned portion is used for templates. In this study, we present a systematic methodology for the evaluation of ECC cryptosystems with and without countermeasures (both RNS-based and traditional ones) against MLbased side-channel attacks using two attack models on full length aligned and unaligned leakages. RNS-based ECC datasets are evaluated using four machine learning classifiers and comparison is provided with existing state-of-the-art template attacks. Moreover, we analyze the impact of raw features and advanced hybrid feature engineering techniques. We discuss the metrics and procedures that can be used for accurate classification on the imbalanced datasets. The experimental results demonstrate that, for ECC RNS datasets, the efficiency of simple machine learning algorithms is better than the complex deep learning techniques when such datasets are limited in size. This is the first study presenting a complete methodology for ML side-channel attacks on public key algorithms.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Machine-Learning Assisted Side-Channel Attacks on RNS ECC Implementations Using Hybrid Feature Engineering

Mukhtar

Papachristodoulou

Fournaris

et al. 2022

Lecture Notes in Computer Science

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“…They used a support vector machine as the classifier of their model to find the secret mask. After that, they attacked each SBox with computing and use the hamming weight of the output byte of the mask SBox as the label of the model to retrieve the secret key [16]. However, the success rate in this model completely depends on the number of sample points.…”

Section: ) Deep Learning Based Profiling Scasmentioning

confidence: 99%

“…In 2019, Hettwer et al [5] directly used the secret key as a label of the model; they also claimed that this would give more ability to the network to learn the most meaningful features of the leakage which is needed for classification. Additionally, there have been studies in supervised machine learning-based profiling side-channel attacks such as [14]- [16], [22] which use support vector machine, LS-SVM, KNN, and RF for the classification problem. In most cases still, a large number of traces is required to reach a stable key rank.…”

Section: ) Performance Metricsmentioning

confidence: 99%

Deep K-TSVM: A Novel Profiled Power Side-Channel Attack on AES-128

Ghandali

Tehranipoor

2021

IEEE Access

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The appearance of deep neural networks for Side-Channel leads to strong power analysis techniques for detecting secret information of physical cryptography implementations. Generally, deep learning techniques do not suffer the difficulties of template attacks such as trace misalignment. However, the generalization of a trained deep neural network that can accurately predict Side-Channel leakages largely depends on its adjustable variables (parameters of a neural network). Although pre-training is no longer mandatory, it is needed for parameter selection of a deep neural network to improve the success rate and provide a better insight into the network's inner functionality. In this paper, we propose a novel model via Twin support vector with a deep kernel approach when targeting a hardware implementation of AES-128. The proposed model is pre-trained with the Restricted Boltzmann Machine method in a layerwise manner and then fine-tuned via gradient descent. Further, we used the grid search technique for the selection of each hyperparameter which is used to compute class probabilities of every test trace in our deep model based side-channel attack. Based on our analysis and experiments, this model empirically shows its effectiveness by outperforming some of its competitors in profiling attack methods such as convolutional neural networks and multilayer perceptron models. We also evaluate our model on both masked and unmasked AES implementation. The results indicate that the proposed approach has achieved a success rate of greater than 99% even with a single trace using Keras library with Tensorflow. We investigate the correct "key rank" according to the number of traces; our model reaches the key rank ≤ 10 when attacking the third AES SBox.

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“…We have performed comparative analysis of six machine/deep learning models, including multi-layer perceptron (MLP), support vector machines (SVM), random forest (RF), Naive Bayes (NB), decision tree (Tree), and K-nearest neighbors (kNN). [49][50][51] However, in this section the details of the best performing machine learning model, MLP, are provided. For analysis in this research, neural network-based SCA model has shown tremendous performance improvement for secret key recovery.…”

Section: Training Model Characteristicsmentioning

confidence: 99%

Edge enhanced deep learning system for IoT edge device security analytics

Mukhtar

Mehrabi

Kong

et al. 2021

Concurrency and Computation

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The processing of locally harvested data at the physically accessible edge devices opens a new avenue of security threats for edge enhanced analytics. Cryptographic algorithms are used to secure the data being processed on the edge device. However, the implementation weakness of the algorithms on the edge devices can lead to side‐channel attack vulnerability, which is exacerbated with the application of machine‐learning techniques. This research proposes a deep learning‐based system integrated at the edge device to identify the side‐channel leakages. To design such a deep learning‐based system, one of the challenges is formulating the suitable attack model for the underlying target algorithm. Based on the previous findings, three machine learning‐based side‐channel attack models are curated and investigated for the edge device security evaluations. As a test case, the standard elliptic‐curve cryptographic algorithm is selected. Moreover, quantitative analysis is provided for the best attack model selection using standard machine‐learning evaluation metrics. A comparative analysis is performed on the raw unaligned data samples and reduced feature‐engineered samples using edge enhanced security analytics. The investigation concludes that the vulnerable algorithm implementation can lead to the secret key recovery from the edge device, with 96% accuracy, using a neural‐network‐based algorithm to analyse side‐channel attacks.

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An Improved Side-Channel Attack Based on Support Vector Machine

Cited by 11 publications

References 15 publications

Machine-Learning Assisted Side-Channel Attacks on RNS ECC Implementations Using Hybrid Feature Engineering

Machine-Learning Assisted Side-Channel Attacks on RNS ECC Implementations Using Hybrid Feature Engineering

Deep K-TSVM: A Novel Profiled Power Side-Channel Attack on AES-128

Edge enhanced deep learning system for IoT edge device security analytics

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