Connectivity has become essential to modern manufacturing, but it also introduces new security challenges. Industrial IoT ecosystems rely on public-key cryptography to protect communications, firmware, and operational data. However, the emergence of quantum computing threatens to undermine these cryptographic foundations, exposing long-lived manufacturing systems to future attacks. This paper presents the Quantum-Secure IoT-based Digital Manufacturing Pilot, developed within the EU-funded QUBIP project, which investigates the integration of post-quantum cryptography (PQC) into IoT environments. The pilot aims to demonstrate how quantum resistant algorithms can be efficiently deployed across heterogeneous devices with limited resources to ensure data exchange and authentication. By combining software and hardware-based approaches, the proposed pilot provides a replicable model for PQC migration in digital manufacturing, ensuring long-term data integrity and resilience in the quantum era.

Side-channel resilience is a critical requirement for cryptographic accelerators. However, current validation approaches rely heavily on costly, measurement-based testing, which is typically applicable only at the final stages of the design flow. This reliance on physical prototypes is aggravated by the lack of integrated security analysis in fragmented toolchains. To address these challenges, we introduce the HWSEC-OSS Framework, a comprehensive open-source platform designed to streamline the security validation of hardware designs. The framework integrates a complete digital design flow with a pre-silicon Side-Channel Analysis (SCA) module based on Hamming-distance power modeling. We demonstrate the effectiveness of the framework by identifying leakage sources in an EdDSA25519 implementation, exhibiting a strong correlation between simulation-based results and measurements from a physical FPGA prototype. Furthermore, we apply the flow to a hardware implementation of ML-KEM, demonstrating scalability to Post-Quantum Cryptography (PQC). By providing an integrated environment for early security feedback, this work constitutes a fast, cost-effective solution for hardware security validation.

The Keccak core is a fundamental cryptographic primitive widely used in post-quantum cryptography to ensure security against emerging quantum-computing attacks. Secure software implementations rely on masking schemes to mitigate side-channel leakage, but these protections introduce significant overhead, which is particularly impactful on resource-constrained microcontrollers and further exacerbated by micro-architectural leakages. To date, publicly available masked Keccak implementations do not achieve both efficiency and robustness against such leakages. This work presents an optimized software implementation providing 1st-order side-channel protection, as well as the first efficiency-oriented 2nd-order implementation, surpassing previous solutions in performance. Both implementations target the ARM Cortex-M4 microcontroller and satisfy constraints to prevent micro-architectural leakages. A public repository provides the source code and a labeled dataset of 500K+500K traces, enabling reproducibility. Benchmarks show a 44.1% improvement over previously reported first-order implementations.

The adoption of Post-Quantum Cryptography over the security network layer, such as Transport Layer Security, poses significant challenges, particularly in the context of Internet of Things (IoT) devices, where power and area constraints frequently limit the adoption of hardened security features. In addressing this challenge, hardware-based Secure Elements become a crucial component in facilitating cryptographic operations and storing secret keys within these frameworks. However, the design and validation of such hardware modules require substantial time investment. Consequently, this can result in the allocation of time that could be utilized for the development of other essential components of the Post-Quantum Transition. In this context, the present work proposes a methodology for the integration of Secure Elements on IoT devices, with the objective of reducing the time required for design and validation. To this end, two distinct categories of IoT devices (microcontrollers and embedded processors) have been employed to encompass the broadest possible range of IoT devices. The outcome of this study is the delivery of an open-source cryptographic library that can be deployed in any type of IoT devices. This library facilitates the implementation of Post-Quantum Cryptography in Secure Elements enabling crypto-agility and ensuring a smooth PostQuantum transition for IoT.

PUFs have emerged as an alternative to traditional Non-Volatile Memories in the field of hardware security. In this paper, a novel PUF is proposed that uses the Random Telegraph Noise phenomenon as the underlying source of entropy. This phenomenon manifests as discrete and random shifts in the drain current of transistors and it is characterized by several parameters like the number of the defects in the device, as well as the emission and capture time constants and current shifts of these defects. Using the recently reported Maximum Current Fluctuation metric, it is possible to condense all this information and use it for the PUF design. By forming pairs of transistors, measuring, and comparing their Maximum Current Fluctuation over a given time interval, we demonstrate using numerical experiments that it is possible to obtain a PUF. Furthermore, the results reported here show that this RTN-based PUF matches, and even out-performs, other silicon PUFs in terms of uniqueness, unpredictability, and reliability with an evident advantage in silicon area.

Post-quantum cryptographic algorithms have emerged to secure communication channels between electronic devices faced with the advent of quantum computers. The performance of post-quantum cryptographic algorithms on embedded systems has to be evaluated to achieve a good trade-off between required resources (area) and timing. This work presents two optimized implementations to speed up the NTRUEncrypt algorithm on a system-on-chip. The strategy is based on accelerating the most time-consuming operation that is the truncated polynomial multiplication. Hardware dedicated modules for multiplication are designed by exploiting the presence of consecutive zeros in the coefficients of the blinding polynomial. The results are validated on a PYNQ-Z2 platform that includes a Zynq-7000 SoC from Xilinx and supports a Python-based programming environment. The optimized version that exploits the presence of double, triple, and quadruple consecutive zeros offers the best performance in timing, in addition to considerably reducing the possibility of an information leakage against an eventual attack on the device, making it practically negligible.

This paper describes two computer-aided design (CAD) tools for automatic synthesis of fuzzy logic-based inference systems. The tools share a common architecture for efficient hardware implementation of fuzzy modules, but are based on two different design strategies. One of them is focused on the generation of standard VHDL code, which can be later implemented on a reconfigurable device [field-programmable gate array (FPGA)] or as an application-specific integrated circuit (ASIC). The other one uses the Matlab/Simulink environment and tools for development of digital signal processing (DSP) systems on Xilinx's FPGAs. Both tools are included in the last version of Xfuzzy, which is a specific environment for designing complex fuzzy systems, and they provide interfaces to commercial VHDL synthesis and verification tools, as well as to conventional FPGA development environments. As demonstrated by the included design example, the proposed development strategies speed up the stages of description, synthesis, and functional verification of embedded fuzzy inference systems.

The complexity reached by current applications of industrial control systems has motivated the development of new computational paradigms, as well as the employment of hybrid implementation techniques that combine hardware and software components to fulfill system requirements. On the other hand, continuous improvements in field-programmable devices today make possible the implementation of complex control systems on reconfigurable hardware, although they are limited by the lack of specific design tools and methodologies to facilitate the development of new products. This paper describes a model-based design approach for the synthesis of embedded fuzzy controllers on field-programmable gate arrays (FPGAs). Its main contributions are the proposal of a novel implementation technique, which allows accelerating the exploration of the design space of fuzzy inference modules, and the use of a design flow that eases their integration into complex control systems and the joint development of hardware and software components. This design flow is supported by specific tools for fuzzy systems development and standard FPGA synthesis and implementation tools, which use the modeling and simulation facilities provided by the Matlab environment. The development of a complex control system for parking an autonomous vehicle demonstrates the capabilities of the proposed procedure to dramatically speed up the stages of description, synthesis, and functional verification of embedded fuzzy controllers for industrial applications.

This paper describes a design methodology to implement on FPGAs piecewise-affine (PWA) functions based on representation methods from the lattice theory. An off-line automatic processing starts at the algorithmic formulation of the problem, obtains the parameters required by a parameterized digital architecture, and ends with the bitstream to program an FPGA. The methodology has been proven to implement PWA functions on Xilinx FPGAs. The results are compared with other approaches for FPGA implementations of PWA functions.

The tuning of hierarchical fuzzy systems are not supported by the majority of CAD tools available at the market currently. The xfsl tool integrated into Xfuzzy 3 allows the tuning of complex fuzzy systems, for instance, hierarchical systems with modules in cascade. The authors propose the use of this tool for tuning a complex fuzzy system for video de-interlacing in this paper. The parameters obtained after tuning are proven by de-interlacing a wide battery of sequences. The use of tuning techniques improves the quality of de-interlacing and provides an algorithm simplification that facilitates its hardware implementation.

Since 1992, Xfuzzy environment has been improving to ease the design of fuzzy systems. The current version, Xfuzzy 3, which is entirely programmed in Java, includes a wide set of new featured tools that allow automating the whole design process of a fuzzy logic based system: from its description (in the XFL3 language) to its synthesis in C, C++ or Java (to be included in software projects) or in VHDL (for hardware projects). The new features of the current version have been exploited in different application areas such as autonomous robot navigation and image processing.