Improving Side-Channel Analysis Through Semi-supervised Learning

Picek, Stjepan; Heuser, Annelie; Jović, Alan; Kneźević, Karlo; Richmond, Tania

doi:10.1007/978-3-030-15462-2_3

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Hence, we collected new traces from 30 different 8-bit AVR microcontrollers running [10], [30], [19], [18], [20], [31] Cross-device Attack Template Attack [14], [13], [32], [15] Neural Networks [21], [22], This Work the AES-128 algorithm using the ChipWhisperer platform [24] (Figure 2). Although 8-bit microcontrollers are becoming less preferred for encryption engines nowadays, recent body of work ( [13], [18], [37], [38], [39]) investigated performance of Profiled SCA attack using datasets gathered from 8-bit microcontrollers.…”

Section: A Related Workmentioning

confidence: 99%

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Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Golder

Das

Danial

et al. 2019

IEEE Trans. VLSI Syst.

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Power side-channel analysis (SCA) has been of immense interest to most embedded designers to evaluate the physical security of the system. This work presents profiling-based cross-device power SCA attacks using deep learning techniques on 8-bit AVR microcontroller devices running AES-128. Firstly, we show the practical issues that arise in these profiling-based cross-device attacks due to significant device-to-device variations. Secondly, we show that utilizing Principal Component Analysis (PCA) based pre-processing and multi-device training, a Multi-Layer Perceptron (MLP) based 256-class classifier can achieve an average accuracy of 99.43% in recovering the first key byte from all the 30 devices in our data set, even in the presence of significant inter-device variations. Results show that the designed MLP with PCA-based pre-processing outperforms a Convolutional Neural Network (CNN) with 4-device training by ∼ 20% in terms of the average test accuracy of cross-device attack for the aligned traces captured using the ChipWhisperer hardware. Finally, to extend the practicality of these cross-device attacks, another preprocessing step, namely, Dynamic Time Warping (DTW) has been utilized to remove any misalignment among the traces, before performing PCA. DTW along with PCA followed by the 256-class MLP classifier provides ≥10.97% higher accuracy than the CNN based approach for cross-device attack even in the presence of up to 50 time-sample misalignments between the traces.

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Section: A Related Workmentioning

confidence: 99%

“…the AES-128 algorithm using the ChipWhisperer platform [24] (Figure 2). Although 8-bit microcontrollers are becoming less preferred for encryption engines nowadays, recent body of work ( [13], [18], [37], [38], [39]) investigated performance of Profiled SCA attack using datasets gathered from 8-bit microcontrollers.…”

Section: A Related Workmentioning

confidence: 99%

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Golder

Das

Danial

et al. 2019

IEEE Trans. VLSI Syst.

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“…Semi-supervised learning is proven to be more powerful than supervised learning when labeled data is scarce [13]. Research by Picek S et al suggests that exploring SCA with semi-supervised learning can improve the performance of SCA models [14]. These studies demonstrate that semi-supervised learning can play a significant role in SCA.…”

Section: Introductionmentioning

confidence: 99%

“…SCA with semi-supervised learning is based on a hypothesis that might occur in real situations. Picek S et al first proposed this hypothesis and described the scenario in which it exists [14]. Under this hypothesis, the attacker has many constraints during the profiling phase, but only a few constraints during the attack phase.…”

Section: Introductionmentioning

confidence: 99%

Improve the Performance of Semi-Supervised Side-channel Analysis Using HWFilter Method

2024

KSII TIIS

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Side-channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi-supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi-supervised learning generates many pseudo-labels. The performance of the SCA model can be reduced by some of these pseudo-labels. To solve this issue, we propose the HWFilter method to improve semisupervised SCA. This method uses a Hamming Weight Pseudo-label Filter (HWPF) to filter the pseudo-labels generated by the semi-supervised SCA model, which enhances the model's performance. Furthermore, we introduce a normal distribution method for constructing the HWPF. In the normal distribution method, the Hamming weights (HWs) of power traces can be obtained from the normal distribution of power points. These HWs are filtered and combined into a HWPF. The HWFilter was tested using the ASCADv1 database and the AES_HD dataset. The experimental results demonstrate that the HWFilter method can significantly enhance the performance of semi-supervised SCA models. In the ASCADv1 database, the model with HWFilter requires only 33 power traces to recover the key. In the AES_HD dataset, the model with HWFilter outperforms the current best semi-supervised SCA model by 12%.

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“…Usually, they rely on a large amount of accurate and high quality profiling traces, which makes them almost impractical in many cases, specifically when there are only a limited number of power traces with low signal-to-noise ratio. Machine learning (ML) based methods can prevent these problems, and are found to perform better than previously known techniques when faced with complexity in numerical and statistical calculations or some of the restrictive assumptions on noise distribution [4][5][6][7][8][9][10][11][12][13][14][15]. Power analysis attacks compromise the security of cryptographic devices through analyzing their power consumption.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Profiling Attack to Real Codes of PIC16F690 and ARM Cortex-M3

2020

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This paper presents a new and efficient method based on power analysis, hierarchical recognition of instructions, and machine learning for reverse engineering of the instructions of PIC16F690 as an 8-bit microcontroller and LPC1768, which includes an ARM Cortex-M3 core as a 32-bit platform. Both dynamic and static power consumption were considered and analyzed. The instructions were classified in different Hamming weight groups using ensemble classification algorithms along with the Kullback-Leibler feature selection method to improve the recognition rate of opcodes and operands of real instructions. Results demonstrated 99.5% and 93.3% average success rate in recovering test instructions and real codes of PIC16F690, respectively. This work also presents promising results in reverse engineering of the instructions of LPC1768 with an overall recognition rate of 98% for test codes and 80.2% for real codes. To the best of our knowledge, this is the first serious report about profiling attack to a 32-bit platform without the need for any sophisticated laboratory tools. INDEX TERMSTemplate Attacks, Reverse Engineering of Instructions, Ensemble Classification. Nomenclature AES Advanced encryption standard KL Kullback-Leibler KLD Kullback-Leibler Divergence ALU Arithmetic Logic Unit KNN k-Nearest Neighbours CWT Continuous wavelet transform LDA Linear discriminant analysis DPA Dynamic power analysis ML Machine learning ECDSA Elliptic curve digital signature algorithm PCA

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Improving Side-Channel Analysis Through Semi-supervised Learning

Cited by 6 publications

References 20 publications

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Practical Approaches Toward Deep-Learning-Based Cross-Device Power Side-Channel Attack

Improve the Performance of Semi-Supervised Side-channel Analysis Using HWFilter Method

An Efficient Profiling Attack to Real Codes of PIC16F690 and ARM Cortex-M3

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