Efficient, Portable Template Attacks

Choudary, Omar; Kühn, Markus

doi:10.1109/tifs.2017.2757440

Cited by 62 publications

(49 citation statements)

References 31 publications

(89 reference statements)

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“…In [4], Choudary and Kuhn present a so-called portable template attack that is able to recover key bytes from a hardware implementation of the Advanced Encryption Standard on an ATMEL AVR XMEGA 8-bit microcontroller with 85% success rate. They present several methods to reduce numerical problems that happen during the statistical calculations related to a practical template attack.…”

Section: Previous Workmentioning

confidence: 99%

“…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%

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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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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…To the best of our knowledge, both data sets consist of traces captured from one single device, which are not suitable for current work. 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%

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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“…Side-channel analysis has attracted a great attention since timing attacks were introduced by Kocher in 1996 [2]. A variety of efficient approaches have proposed in the past 20 years, such as differential power analysis (DPA) [3], correlation power analysis (CPA) [4], template analysis (TA) [5,6], fault analysis (FA) [7], electromagnetic analysis (EMA) [8][9][10][11][12] and so on. For DPA, plenty of power traces are statistically computed to reveal the power consumption difference caused by different intermediate values and then the difference is used to recover the key.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm

Sun

Liang

Cui

et al. 2019

Sensors

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Correlation electromagnetic analysis (CEMA) is a method prevalent in side-channel analysis of cryptographic devices. Its success mostly depends on the quality of electromagnetic signals acquired from the devices. In the past, only one byte of the key was analyzed and other bytes were regarded as noise. Apparently, other bytes’ useful information was wasted, which may increase the difficulty of recovering the key. Multi-objective optimization is a good way to solve the problem of a single byte of the key. In this work, we applied multi-objective optimization to correlation electromagnetic analysis taking all bytes of the key into consideration. Combining the advantages of multi-objective optimization and genetic algorithm, we put forward a novel multi-objective electromagnetic analysis based on a genetic algorithm to take full advantage of information when recovering the key. Experiments with an Advanced Encryption Standard (AES) cryptographic algorithm on a Sakura-G board demonstrate the efficiency of our method in practice. The experimental results show that our method reduces the number of traces required in correlation electromagnetic analysis. It achieved approximately 42.72% improvement for the corresponding case compared with CEMA.

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Efficient, Portable Template Attacks

Abstract: Cambridge. His research interests include hardware and signal-processing aspects of computer security, in particular compromising emanations, side-channel attacks, distance bounding protocols, and the security of RFID and navigation systems.

Cited by 62 publications

References 31 publications

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

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

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

A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm

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