Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing new Network instance reach high performance number of considered datasets. We compare our neural network the one designed particular dataset masking...

Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering deep commonly have plethora hyperparameters tune, clear such top can come high price preparing attack. This especially problematic as side-channel community...

We concentrate on machine learning techniques used for profiled sidechannel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, instance Hamming weight or distance leakage models. In order to deal with data, we use various balancing show that most them help mounting successful attacks when data is highly imbalanced. Especially, results SMOTE technique encouraging, since observe some where it reduces number necessary measurements more than 8 times....

The field of side-channel analysis has made significant progress over time. Side-channel is now used in practice design companies as well test laboratories, and the security products against attacks significantly improved. However, there are still some remaining issues to be solved for become more effective. consists two steps, commonly referred identification exploitation. understanding leakage building suitable models. exploitation using identified models extract secret key. In scenarios...

Profiled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing new Network instance reach high performance number of considered datasets. We compare our neural network the one designed particular dataset masking...

Abstract The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range direct solvers optimization techniques. Motivated enormous advances in the field machine learning, there has recently growing interest developing complementary data-driven methods for photonics. Here, we demonstrate several predictive generative approaches characterization inverse crystals. Concretely, built data set 20,000 two-dimensional...

Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating advantages of such an approach. Indeed, breaking protected implementations while not requiring complex feature selection made learning preferred option for profiling analysis. Still, this does mean it is trivial to mount successful One biggest challenges find optimal hyperparameters neural networks resulting in powerful attacks. This work proposes automated way...

One of the main promoted advantages deep learning in profiling sidechannel analysis is possibility skipping feature engineering process. Despite that, most recent publications consider selection as attacked interval from side-channel measurements pre-selected. This similar to worst-case security assumptions evaluations when random secret shares (e.g., mask shares) are known during phase: an evaluator can identify points ofinterest locations and efficiently trim trace interval. To broadly...

Profiled side-channel attacks represent a practical threat to digital devices, thereby having the potential disrupt foundation of e-commerce, Internet Things (IoT), and smart cities.In profiled attack, adversary gains knowledge about target device by getting access cloned device.Though these two devices are different in realworld scenarios, yet, unfortunately, large part research works simplifies setting using only single for both profiling attacking.There, portability issue is conveniently...

Substitution Boxes (S-Boxes) play an important role in many modern-day cryptographic algorithms, more commonly known as ciphers. Without carefully chosen S-Boxes, such ciphers would be easier to break. Therefore, it is not surprising that the design of suitable S-Boxes attracts a lot attention cryptography community. The evolutionary computation (EC) community also had several attempts using paradigms evolve with good properties. This article focuses on fitness function one should use when...

The adoption of deep neural networks for profiled side-channel attacks provides powerful options leakage detection and key retrieval secure products. When training a network analysis, it is expected that the trained model can implement an approximation function detect leaking samples and, at same time, be insensible to noisy (or non-leaking) samples. This outlines generalization situation where identify main representations learned from set in separate test set.This paper discusses how...

Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while performing normally on other benign inputs. There are already numerous works backdoor networks, but only few consider graph networks (GNNs). As such, there is no intensive research explaining impact of trigger injecting position performance GNNs.

In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling attacks. At same time, previous state-of-the-art technique, template attack, started losing its importance and was more considered baseline to compare against. As such, most of results reported that learning (and especially deep learning) could significantly outperform attack. Nevertheless, attack still has certain advantages even compared learning. The significant one...

This work explores stylistic triggers for backdoor attacks in the audio domain: dynamic transformations of malicious samples through guitar effects. We first formalize – currently missing literature. Second, we explore how to develop domain by proposing JingleBack. Our experiments confirm effectiveness attack, achieving a 96% attack success rate. code is available https://github.com/skoffas/going-in-style.

Large Language Models (LLMS) have increasingly become central to generating content with potential societal impacts. Notably, these models demonstrated capabilities for that could be deemed harmful. To mitigate risks, researchers adopted safety training techniques align model outputs values curb the generation of malicious content. However, phenomenon "jailbreaking", where carefully crafted prompts elicit harmful responses from models, persists as a significant challenge. This research...

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that learning attacks will always succeed. Various countermeasures make significantly more complex, and such can further combined challenging. An intuitive solution improve performance of would reduce effect countermeasures.This paper investigates whether we consider certain types hiding as noise then use a...

This work explores backdoor attacks for automatic speech recognition systems where we inject inaudible triggers. By doing so, make the attack challenging to detect legitimate users, and thus, potentially more dangerous. We conduct experiments on two versions of a dataset three neural networks explore performance our concerning duration, position, type trigger. Our results indicate that less than 1% poisoned data is sufficient deploy reach 100% success rate. observed short, non-continuous...

Abstract Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even relatively small number of attack traces. Still, necessary to conduct hyperparameter tuning reach strong performance, which be far from trivial. Besides many options stemming the machine domain, recent years also brought neural network elements specially designed analysis. The loss function, calculates error or between actual and desired output, one...