SCAUL: Power Side-Channel Analysis With Unsupervised Learning

Ramezanpour, Keyvan; Ampadu, Paul; Diehl, William

doi:10.1109/tc.2020.3013196

Cited by 42 publications

(15 citation statements)

References 33 publications

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“…Authors in [12] developed a multi-layerperceptron (MLP) NN-based sensitivity analysis to derive the leakage model. The observed traces (measured leaked information) were clustered, then encoded using long-short-term memory (LSTM) autoencoder to provide contextual features (secret keys patterns).…”

Section: Slice Isolationmentioning

confidence: 99%

“…However, machine-type-communication-generated traffic could be considered for robust interpretations of the dynamics in demands. Later, we discuss additional challenges that pertain to the introduction of ML for RAN-S. We also envisage the incorporation of MLbased information leakage models [11], [12] into RAN-S to tackle the challenge of slice isolation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges

Mazied,

Liu,

Midkiff

2021

Preprint

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To meet the diverse demands for wireless communication, fifth-generation (5G) networks and beyond (B5G) embrace the concept of network slicing by forging virtual instances (slices) of its physical infrastructure. While network slicing constitutes dynamic allocation of core network and radio access network (RAN) resources, this article emphasizes RAN slicing (RAN-S) design. Forming on-demand RAN-S that can be flexibly (re)-configured while ensuring slice isolation is challenging. A variety of machine learning (ML) techniques have been recently introduced for traffic forecasting and classification, resource usage prediction, admission control, scheduling, and dynamic resource allocation in RAN-S. Albeit these approaches grant opportunities towards intelligent RAN-S design, they raise critical challenges that need to be examined. This article underlines the opportunities and the challenges of incorporating ML into RAN-S by reviewing the cutting-edge ML-based techniques for RAN-S. It also draws few directions for future research towards intelligent RAN-S (iRAN-S).

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Section: Slice Isolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges

Mazied,

Liu,

Midkiff

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, the hardware security research has transferred attention to the machine learning (ML) based profiled attacks and ciphers classification. Support Vector Machine (SVM) [6], Random Forest (RF) [7], and other deep learning [8] based attacks not only perform valid attacks but reduce the concern on POIs.…”

Section: Introductionmentioning

confidence: 99%

“…2017, Eleonora Cagli proposed a point-to-point profiling attack method based on the application of Convolutional Neural Networks (CNN) [9][10][11][12][13][14][15]. It does not need a manually traces realignment nor accurate selection of POIs.…”

Section: Introductionmentioning

confidence: 99%

Side-channel Attack Using Word Embedding and Long Short Term Memories

Liu

Wang

Ling

2022

JWE

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Side-channel attack (SCA) based on machine learning has proved to be a valid technique in cybersecurity, especially subjecting to the symmetric-key crypto implementations in serial operation. At the same time, parallel-encryption computing based on Field Programmable Gate Arrays (FPGAs) grows into a new influencer, but the attack results using machine learning are exiguous. Research on the traditional SCA has been mostly restricted to pre-processing: Signal Noisy Ratio (SNR) and Principal Component Analysis (PCA), etc. In this work, firstly, we propose to replace Points of Interests (POIs) and dimensionality reduction by utilizing word embedding, which converts power traces into sensitive vectors. Secondly, we combined sensitive vectors with Long Short Term Memories (LSTM) to execute SCA based on FPGA crypto-implementations. In addition, compared with traditional Template Attack (TA), Multiple Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). The result shows that the proposed model can not only reduce the manual operation, such as parametric assumptions and dimensionality setting, which limits their range of application, but improve the effectiveness of side-channel attacks as well.

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“…Masure et al [16] investigate the theoretical soundness of Convolutional Neural Networks (CNNs) in the context of side-channel, References [17][18][19] demonstrated successful attacks on Virtex-5 FPGAs using CNNs. On a lightweight implementation of AES on Artix-7 FPGA [20], a non-profiled attack is able to recover the key with 3700 traces. Apart from FPGA, [21] shows the effectiveness of CNN-based side-channel attacks on ASICs.…”

Section: Introductionmentioning

confidence: 99%

Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

Wang

Dubrova

2021

SN COMPUT. SCI.

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Side-channel attacks have become a realistic threat to implementations of cryptographic algorithms, especially with the help of deep-learning techniques. The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to extract the secret from implementations of cryptographic algorithms. The potential benefits of combining multiple classifiers using the ensemble learning method have not been fully explored in the side-channel attack’s context. In this paper, we propose a tandem approach for the attack in which multiple models are trained on different attack points but are used in parallel to recover the key. Such an approach allows us to considerably reduce (33.5% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.

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SCAUL: Power Side-Channel Analysis With Unsupervised Learning

Cited by 42 publications

References 33 publications

Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges

Towards Intelligent RAN Slicing for B5G: Opportunities and Challenges

Side-channel Attack Using Word Embedding and Long Short Term Memories

Tandem Deep Learning Side-Channel Attack on FPGA Implementation of AES

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