Analysis of Random Body Bias Application in FDSOI Cryptosystems as a Countermeasure to Leakage-Based Power Analysis Attacks

Palma, Kenneth; Moll, Francesc

doi:10.1109/access.2021.3105635

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The provided energy consumption identified numerous nuclei, with several major changes, like the version described in this study suggested by Palma et al [16]. First and foremost, the methodology is applied to diverse multi-core devices and assesses energy at command instead of resource use.…”

Section: Background To the Energy Prediction Modelsmentioning

confidence: 99%

Power Prediction of VLSI Circuits Using Machine Learning

Poovannan¹,

Karthik²

2023

Computers, Materials &Amp; Continua

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The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective power assessment is only possible in a limited sample of pre-selected experiments. Thus, a key objective is to find the experiments that might give the worst situations again for testing power. It offers a machine-based circuit power estimation (ML-CPE) system for the selection of exams. Two distinct techniques of predicting are utilized. Firstly, to find testings with power dissipation, it forecasts the behavior of testing. Secondly, the change movement and energy data are linked to the semiconductor design, identifying small problem areas. Several types of algorithms are utilized. In particular, the methods compared. The findings show great accuracy and efficiency in forecasting. That enables such methods suitable for selecting the worst scenario.

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Section: Background To the Energy Prediction Modelsmentioning

confidence: 99%

Power Prediction of VLSI Circuits Using Machine Learning

Poovannan¹,

Karthik²

2023

Computers, Materials &Amp; Continua

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Current Balancing Random Body Bias in FDSOI Cryptosystems as a Countermeasure to Leakage Power Analysis Attacks

Palma

Moll

2022

IEEE Access

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This paper identifies vulnerabilities to recently proposed countermeasures to leakage power analysis attacks in FDSOI systems based on the application of a random body bias. The vulnerabilities are analyzed and the relative difficulty to obtain the secret key, once the vulnerabilities are taken into account, are compared to the original proposals. A new countermeasure, based on a new body bias scheme, is then proposed. The new countermeasure is based on the equalization of asymmetries in static power consumption dependent on data being stored in registers implemented in FDSOI technology. The countermeasure's effectiveness is theoretically established through the development of a power model based on technological parameters, and further reinforced through numerical simulations of a dummy cryptosystem implementing part of an AES encrypting round.INDEX TERMS Body bias, correlation power analysis, countermeasures, cryptography, FDSOI, leakage power analysis, side-channel. I. INTRODUCTIONThe exploitation of power consumption of cryptographic circuits as a source of information and a means to retrieve the secret key has been extensively studied in the last two decades [1]. These so called Power Analysis Attacks (PAA) rely on asymmetries in power consumption that arise from differing circuit states subjected to the data being processed in intermediate stages of encrypting algorithms.Traditionally, PAA have mainly focused on the dynamic power consumption of cryptographic circuits to derive statistical models of power consumption based on the data being processed. These models of power consumption allow the testing of secret key hypothesis with a minimum setting and quick computation.PAA traditionally rely on statistical metrics, namely, the Difference of Means or the Pearson Correlation Coefficient (PCC) [2], to test secret key hypothesis given a correct power model. Since power consumption is dependent on processed data which is, in turn, dependent on the secret key, the power consumed by a cryptographic circuit is highly correlated withThe associate editor coordinating the review of this manuscript and approving it for publication was Junggab Son .

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