Profiling side-channel analysis (SCA) is widely used to evaluate the security of cryptographic implementations under worst-case attack scenarios. This method assumes a strong adversary with a fully controlled device clone, known as a profiling device, with full access to the internal state of the target algorithm, including the mask shares. However, acquiring such a profiling device in the real world is challenging, as secure products enforce strong life cycle protection, particularly on devices that allow the user partial (e.g., debug mode) or full (e.g., test mode) control. This enforcement restricts access to profiling devices, significantly reducing the effectiveness of profiling SCA. To address this limitation, this paper introduces a novel framework that allows an attacker to create and learn from their own white-box reference design without needing privileged access on the profiling device. Specifically, the attacker first implements the target algorithm on a different type of device with full control. Since this device is a white box to the attacker, they can access all internal states and mask shares. A novel conditional generative adversarial network (CGAN) framework is then introduced to mimic the feature extraction procedure from the reference device and transfer this experience to extract high-order leakages from the target device. These extracted features then serve as inputs for profiled SCA. Experiments show that our approach significantly enhances the efficacy of black-box profiling SCA, matching or potentially exceeding the results of worst-case security evaluations. Compared with conventional profiling SCA, which has strict requirements on the profiling device, our framework relaxes this threat model and, thus, can be better adapted to real-world attacks.

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In an era of increasing reliance on digital technology, securing embedded and interconnected devices, such as smart cards or Internet of Things (IoT) devices, against emerging threats becomes crucial, highlighting the need for advanced security measures. Cryptographic algorithms, essential for secure communication, data storage, and transaction integrity, are often employed to develop secure systems. However, the practical implementation of these algorithms in software and hardware introduces vulnerabilities, exposing sensitive information to risks. Implementation attacks, such as fault injection (FI) and side-channel analysis (SCA), belong to a category of security threats that exploit these vulnerabilities occurring during the cryptographic algorithms’ execution.

Security evaluation and certification assess the product’s security features against industry best practices and regulatory standards. These processes aim to independently verify the claims made about the product’s security, fostering and maintaining trust among users. Given the evolving landscape of security threats and increasing security concerns, the need for more efficient and resource-effective security evaluations has become evident. Fault injection and side-channel analysis are commonly conducted as part of this assessment, and recent studies have demonstrated that integrating artificial intelligence (AI) methods can significantly enhance their performance. Moreover, this integration can provide more automated and optimized attacks for security evaluation.

This thesis aims to advance AI-based implementation attacks by investigating current AI frameworks, with the objective of improving the efficiency and effectiveness of these attacks across various scenarios. We target specific challenges within AI-based fault injection (AIFI) and deep learning-based SCA (DLSCA), addressing gaps in the current methodologies and proposing solutions that significantly impact their performance and efficiency. We focus on hyperparameter tuning of the utilized AI methods, portability of the attacks, and alternative evaluation metrics within the AI frameworks.

Hyperparameter tuning is critical but can be a time-intensive process. By investigating specific hyperparameters, we can identify those crucial for the performance, guiding a more efficient tuning process. This thesis focuses on initialization methods, revealing no universally optimal initialization method. Instead, we offer a strategic approach to selecting initialization methods that can lead to improved and more reliable performance in specific scenarios. Next, we provide practical AI-based solutions to enhance the portability of FI parameter search results across different samples of the same target and SCA profiling models across different public datasets (targets). This approach makes security evaluation more efficient by leveraging data and findings to expedite evaluations on other targets. Furthermore, this enables future efforts to develop universal methods to help standardize the AI-based implementation attacks for security evaluation. Lastly, we revisit and refine evaluation metrics within the AI-based implementation attacks, proposing new metrics better aligned with the considered objectives. We present new XIX XX SUMMARY metrics for evaluating the performance of AI-based FI parameter search to find distant vulnerable regions of the target alongside algorithms for this objective. On the other hand, we improve the training process of DLSCA by introducing a training scheme involving the redefinition of the labels and a metric that can evaluate the generality of the profiling model, enabling better assessment for early stopping and model tuning.

Through its exploration of AI-based implementation attacks, this thesis offers valuable insights and practical solutions that significantly enhance the field. By improving the efficiency and effectiveness of AI-based implementation attacks, this research not only aids security analysts but also offers a foundation for future standardization efforts of these attacks for security evaluation.
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Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different underlying leakage distributions. Consequently, the same profiling model hyperparameters are usually not equally efficient for other targets. This paper considers autoencoders for dimensionality reduction to verify if encoded datasets from different targets enable the portability of profiling models and architectures. Successful portability reduces the hyperparameter tuning efforts as profiling model tuning is eliminated for the new dataset, and tuning autoencoders is simpler. We first search for the best autoencoder for each dataset and the best profiling model when the encoded dataset becomes the training set. Our results show no significant difference in tuning efforts using original and encoded traces, meaning that encoded data reliably represents the original data. Next, we verify how portable is the best profiling model among different datasets. Our results show that tuning autoencoders enables and improves portability while reducing the effort in hyperparameter search for profiling models. Lastly, we present a transfer learning case where dimensionality reduction might be necessary if the model is tuned for a dataset with fewer features than the new dataset. In this case, tuning of the profiling model is eliminated and training time reduced.

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Evolutionary algorithms have been successfully applied to attack Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. In this paper, we take a step back and systematically evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs. Our results confirm that CMA-ES has the best performance, but we note several other algorithms with similar performance while having smaller computational costs.

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The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.@en

We consider finding as many faults as possible on the target device in the laser fault injection security evaluation. Since the search space is large, we require efficient search methods. Recently, an evolutionary approach using a memetic algorithm was proposed and shown to find more interesting parameter combinations than random search, which is commonly used. Unfortunately, once a variation on the bench or target is introduced, the process must be repeated to find suitable parameter combinations anew.To negate the effect of variation, we propose a novel method combining a memetic algorithm with a machine learning approach called a decision tree. Our approach improves the memetic algorithm by using prior knowledge of the target introduced in the initial phase of the memetic algorithm. In our experiments, the decision tree rules enhance the performance of the memetic algorithm by finding more interesting faults in different samples of the same target. Our approach shows more than two orders of magnitude better performance than random search and up to 60% better performance than previous state-of-the-art results with a memetic algorithm. Another advantage of our approach is human-readable rules, allowing the first insights into the explainability of target characterization for laser fault injection.@en

In fault injection attacks, the first step is to evaluate the target behavior for various fault injection parameters. Showing the results of such a characterization (commonly known as target cartography) is informative and allows researchers to assess the target’s behavior better. Additionally, it helps understand the performance of new search methods or attacks. Thus, publishing obtained results is essential to provide relevant information for reproducibility and benchmarking, improving state-of-the-art results and general security. Unfortunately, publishing the results also allows malicious parties to reverse engineer the information and potentially mount an attack easier. This work discusses how various transformations can be used to occlude sensitive information but, at the same time, still be useful for interested researchers. Our results show that even simple 2D transformations, such as rotation, scaling, and shifting, significantly increase the effort required to reverse engineer the transformed data but maintain the interesting data distribution. Consequently, this work provides a method to allow publishers to share more data in a confidential setting.

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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.@en

Recently, deep learning has emerged as a powerful technique for side-channel attacks, capable of even breaking common countermeasures. Still, trained models are generally large, and thus, performing evaluation becomes resource-intensive. The resource requirements increase in realistic settings where traces can be noisy, and countermeasures are active. In this work, we exploit mimicking to compress the learned models. We demonstrate up to 300 times compression of a state-of-the-art CNN. The mimic shallow network can also achieve much better accuracy as compared to when trained on original data and even reach the performance of a deeper network.@en

The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers’ choice influences deep neural networks’ performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions.

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