Genetic Algorithm-Based Electromagnetic Fault Injection

Maldini, Antun; Samwel, Niels; Picek, Stjepan; Batina, Lejla

doi:10.1109/fdtc.2018.00014

Cited by 15 publications

(15 citation statements)

References 21 publications

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“…2) We propose a memetic algorithm that combines genetic algorithm and Hooke-Jeeves local search. In our opinion, this improves over related works [15], [16] with memetic algorithms as Hooke-Jeeves is a well understood and efficient derivative-free optimization algorithm. 3) We are the first to explore the influence of the initial population on the performance of the population-based search algorithms for fault injection attacks.…”

Section: Introductionsupporting

confidence: 53%

“…The initial population, if welldistributed, could lead to better performance of the genetic algorithm [29], [30], [31]. Usually, the initialization is done using Monte Carlo simulation, taking random values for each gene of the solution [32], [33], [16]. We additionally explore the Latin Hypercube Sampling (LHS) and a Taguchi method.…”

Section: B Initialization Techniques For the Initial Populationmentioning

confidence: 99%

“…The fitness values for other fault classes are shown in Table III. The values are taken from [16] to present the preference of the fault classes and their relation. Since we are trying to maximize the fitness of the solutions, we set the fitness value for the fail class the highest followed by the mute and pass response.…”

Section: B Evaluation and Fitness Valuesmentioning

confidence: 99%

“…The memetic algorithm iterates the standard process of GA but adds a local search. The memetic algorithm is also used by Maldini et al for Electromagnetic Fault Injection (EMFI) [16]. Another interesting approach using artificial intelligence techniques, namely machine learning, was introduced in [17].…”

Section: Introductionmentioning

confidence: 99%

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On the Importance of Initial Solutions Selection in Fault Injection

Krček

Fronte

Picek

2021

2021 Workshop on Fault Detection and Tolerance in Cryptography (FDTC)

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Fault injection attacks require the adversary to select suitable parameters for the attack. In this work, we consider laser fault injection and parameters like the location of the laser shot (x, y), delay, pulse width, and intensity of the laser. The parameter selection process can be translated into an optimization problem. A very popular and successful method for various optimization problems is the genetic algorithm. To further improve the performance of a genetic algorithm, it is possible to combine it with local search to obtain a memetic algorithm. We conduct several experiments comparing the performance of the memetic algorithm and the random search algorithm for finding faults. We investigate the influence of different initialization techniques on the performance of the memetic algorithm. In our experiments, the memetic algorithm is significantly better at finding faults than the random search. While evaluating different initialization techniques, we did not observe significant differences when averaging results. However, when considering the stability of the results with a memetic algorithm based on different initialization techniques, we can distinguish preferable techniques, such as LHSMDU and the Taguchi method.

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Section: Introductionsupporting

confidence: 53%

Section: B Initialization Techniques For the Initial Populationmentioning

confidence: 99%

Section: B Evaluation and Fitness Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Importance of Initial Solutions Selection in Fault Injection

Krček

Fronte

Picek

2021

2021 Workshop on Fault Detection and Tolerance in Cryptography (FDTC)

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“…Next, Picek et al extended this work by using a combination of an evolutionary algorithm and a local search to characterize the search space for voltage glitching as efficient as possible [4]. Maldini et al used a genetic algorithm for finding fault injection parameters when considering electromagnetic fault injection (EMFI) [14]. There, the authors attacked the SHA-3 algorithm and reported 40 times more faulty measurements and 20 times more distinct fault measurements than by using a random search.…”

Section: Related Workmentioning

confidence: 99%

A Fast Characterization Method for Semi-invasive Fault Injection Attacks

Ribera²,

Beringuier-Boher³

et al. 2020

Topics in Cryptology – CT-RSA 2020

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Semi-invasive fault injection attacks are powerful techniques well-known by attackers and secure embedded system designers. When performing such attacks, the selection of the fault injection parameters is of utmost importance and usually based on the experience of the attacker. Surprisingly, there exists no formal and general approach to characterize the target behavior under attack. In this work, we present a novel methodology to perform a fast characterization of the fault injection impact on a target, depending on the possible attack parameters. We experimentally show our methodology to be a successful one when targeting different algorithms such as DES and AES encryption and then extend to the full characterization with the help of deep learning. Finally, we show how the characterization results are transferable between different targets.

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