Understanding the decision-making process of a machine/deep learning model is crucial, particularly in security-sensitive applications. In this study, we introduce a neural network framework that combines the global and exact interpretability properties of rule-based models with the high performance of deep neural networks. Our proposed framework, called Truth Table rules (TT-rules), is built upon Truth Table nets (TTnets), a family of deep neural networks initially developed for formal verification. By extracting the set of necessary and sufficient rules R from the trained TTnet model (global interpretability), yielding the same output as the TTnet (exact interpretability), TT-rules effectively transforms the neural network into a rule-based model. This rule-based model supports binary classification, multi-label classification, and regression tasks for tabular datasets. Furthermore, our TT-rules framework optimizes the rule set R into Ropt by reducing the number and size of the rules. To enhance model interpretation, we leverage Reduced Ordered Binary Decision Diagrams (ROBDDs) to visualize these rules effectively. After outlining the framework, we evaluate the performance of TT-rules on seven tabular datasets from finance, healthcare, and justice domains. We also compare the TT-rules framework to state-of-the-art rule-based methods. Our results demonstrate that TT-rules achieves equal or higher performance compared to other interpretable methods while maintaining a balance between performance and complexity. Notably, TT-rules presents the first accurate rule-based model capable of fitting large tabular datasets, including two real-life DNA datasets with over 20K features. Finally, we extensively investigate a rule-based model derived from TT-rules using the Adult dataset.

Recently, the NIST launched a competition for lightweight cryptography and a large number of ciphers are expected to be studied and analyzed under this competition. Apart from the classical security, the candidates are desired to be analyzed against physical attacks. Differential Fault Analysis (DFA) is an invasive physical attack method for recovering key information from cipher implementations. Up to date, almost all the block ciphers have been shown to be vulnerable against DFA, while following similar attack patterns. However, so far researchers mostly focused on particular ciphers rather than cipher families, resulting in works that reuse the same idea for different ciphers. In this article, we aim at bridging this gap, by providing a generic DFA attack method targeting Substitution-Permutation Network (SPN) based families of symmetric block ciphers. We provide the overview of the state-of-the-art of the fault attacks on SPNs, followed by generalized conditions that hold on all the ciphers of this design family. We show that for any SPN, as long as the fault mask injected before a non-linear layer in the last round follows a non-uniform distribution, the key search space can always be reduced. This shows that it is not possible to design an SPN-based cipher that is completely secure against DFA, without randomization. Furthermore, we propose a novel approach to find good fault masks that can leak the key with a small number of instances. We then developed a tool, called Joint Difference Distribution Table (JDDT) for pre-computing the solutions for the fault equations, which allows us to recover the last round key with a very small number of pairs of faulty and non-faulty ciphertexts. We evaluate our methodology on various block ciphers, including PRESENT-80, PRESENT-128, GIFT-64, GIFT-128, AES-128, LED-64, LED-128, Skinny, Pride and Prince. The developed technique would allow automated DFA analysis of several candidates in the NIST competitio

We propose the TWEAKEY framework with goal to unify the design of tweakable block ciphers and of block ciphers resistant to related-key attacks. Our framework is simple, extends the key-alternating construction, and allows to build a primitive with arbitrary tweak and key sizes, given the public round permutation (for instance, the AES round). Increasing the sizes renders the security analysis very difficult and thus we identify a subclass of TWEAKEY , that we name STK , which solves the size issue by the use of finite field multiplications on low hamming weight constants. Overall, this construction allows a significant increase of security of well-known authenticated encryptions mode like Θ CB3 from birthday-bound security to full security, where a regular block cipher was used as a black box to build a tweakable block cipher. Our work can also be seen as advances on the topic of secure key schedule design.

This paper is an introduction to security challenges for the design of automotive hardware/software architectures. State-of-the-art automotive architectures are highly heterogeneous and complex systems that rely on distributed functions based on electronics and software. As cars are getting more connected with their environment, the vulnerability to attacks is rapidly growing. Examples for such wireless communication are keyless entry systems, WiFi, or Bluetooth. Despite this increasing vulnerability, the design of automotive architectures is still mainly driven by safety and cost issues rather than security. In this paper, we present potential threats and vulnerabilities, and outline upcoming security challenges in automotive architectures. In particular, we discuss the challenges arising in electric vehicles, like the vulnerability to attacks involving tampering with the battery safety. Finally, we discuss future automotive architectures based on Ethernet/IP and how formal verification methods might be used to increase their security.

In this article, we propose a new improvement of the rebound techniques, used for cryptanalyzing AES -like permutations during the past years. Our improvement, that allows to reduce the complexity of the attacks, increases the probability of the outbound part by considering a new type of differential paths. Moreover, we propose a new type of distinguisher, the multiple limited-birthday problem, based on the limited-birthday one, but where differences on the input and on the output might have randomized positions. We also discuss the generic complexity for solving this problem and provide a lower bound of it as well as we propose an efficient and generic algorithm for solving it. Our advances lead to improved distinguishing or collision results for many AES -based functions such as AES , ECHO , Grøstl , LED , PHOTON and Whirlpool .