[continued from previous message]

This work introduces FESLA (Feature Enhanced Statistical Learning Attack), a hybrid statistical learning framework that integrates outputs from a suite of classical statistical tests with machine learning and deep learning classifiers to construct
ciphertext-only distinguishers for AES-128, AES-192, and AES-256. In contrast to existing approaches based on handcrafted or bitwise features, FESLA aggregates intermediate statistical metrics as features, enabling the capture of persistent structural
biases in ciphertext distributions.

Experimental evaluation across multiple datasets demonstrates consistent 100% classification accuracy using Support Vector Machines, Random Forests, Multi-Layer Perceptron, Logistic Regression, and Naïve Bayes classifiers. Generalization and robustness
are confirmed through k-fold cross-validation, including on previously unseen ciphertext samples.

These results establish the first ciphertext-only distinguishers for full-round AES-128, AES-192, and AES-256 under the secret-key model, and underscore the potential of machine learning–augmented cryptanalysis based on principled statistical feature
engineering.



## 2025/863

* Title: Fly Away: Lifting Fault Security through Canaries and the Uniform Random Fault Model
* Authors: Gaëtan Cassiers, Siemen Dhooghe, Thorben Moos, Sayandeep Saha, François-Xavier Standaert
* [Permalink](https://eprint.iacr.org/2025/863)
* [Download](https://eprint.iacr.org/2025/863.pdf)

### Abstract

Cryptographic implementations are vulnerable to active physical attacks where adversaries inject faults to extract sensitive information. Existing fault models, such as the threshold and random fault models, assume limitations on the amount or
probability of injecting faults. Such models, however, insufficiently address the case of practical fault injection methods capable of faulting a large proportion of the wires in a circuit with high probability. Prior works have shown that this
insufficiency can lead to concrete key recovery attacks against implementations proven secure in these models. We address this blind spot by introducing the uniform random fault model, which relaxes assumptions on the amount/probability of faults and
instead assumes a uniform probabilistic faulting of all wires in a circuit or region. We then show that security in this new model can be reduced to security in the random fault model by inserting canaries in the circuit to ensure secret-independent
fault detection. We prove that combining canaries with a more classical fault countermeasure such as redundancy can lead to exponential fault security in the uniform random fault model at a polynomial cost in circuit size in the security parameter.
Finally, we discuss the interactions between our work and the practical engineering challenges of fault security, shedding light on how the combination of state-of-the-art countermeasures may protect against injections of many high probability faults,
while opening a path to methodologies that formally analyze the guarantees provided by such countermeasures.

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