In recent years, a new generation of the Internet of Things (IoT 2.0) is emerging, based on artificial intelligence, the blockchain technology, machine learning, and the constant consolidation of pre-existing systems and subsystems into larger systems. In this work, we construct and examine a proof-of-concept prototype of such a system of systems, which consists of heterogeneous commercial off-the-shelf components, and utilises diverse communication protocols. We recognise the inherent need for lightweight security in this context, and address it by employing a low-cost state-of-the-art security solution. Our solution is based on a novel hardware and software co-engineering paradigm, utilising well-known software-based cryptographic algorithms, in order to maximise the security potential of the hardware security primitive (a Physical Unclonable Function) that is used as a security anchor. The performance of the proposed security solution is evaluated, proving its suitability even for real-time applications. Additionally, the Dolev-Yao attacker model is considered in order to assess the resilience of our solution towards attacks against the confidentiality, integrity, and availability of the examined system of systems. In this way, it is confirmed that the proposed solution is able to address the emerging security challenges of the oncoming era of systems of systems.

A Physical Unclonable Function (PUF) exploits de-vice manufacturing variations to extract a number of unique responses, each of which corresponds to a specific challenge, and use them for secret generation as well as authentication. However, these responses are often excessively noisy for authentication. In this study, in order to avoid using a complicated and costly Error Correction Code (ECC) in the context of a fuzzy extractor, we propose a Machine Learning (ML)-based classification technique that works accurately even in the presence of corrupted responses and we test it on an existing SRAM PUF dataset. In this approach, the SRAM power-up responses are transformed into 2D images in order to extract features from them for classification. With our proposed architecture, an accuracy of 97.34 % is achieved for “intact” noisy images. In addition, the presented ML model is successful in classifying responses even when a large part of them was corrupted. With our approach, the model can classify responses accurately with up to a 30 % loss in data. Finally, we also propose a proof-of-concept authentication technique using the relevant Convolutional Neural Network (CNN).

In this article, we introduce a novel approach to achieving lightweight device authentication through the use of a low-complexity Convolutional Neural Network (CNN). In our work, we improve the False Authentication Rate (FAR) by transforming the standard CNN into a Bayesian CNN (BCNN or BNN). This transformation enables the use of probabilistic modelling techniques, increasing the model’s robustness and its confidence in authentication decisions. Regardless of the model used, clients authenticate with a retention-based Dynamic Random Access Memory Physical Unclonable Function (DRAM PUF) response. Our approach integrates the low computational complexity of the CNN with the intrinsic security characteristics of the DRAM PUF, offering a robust solution for lightweight and secure device authentication.

In this work, we present a Physical Unclonable Function (PUF) implemented on a Commercial Off-The-Shelf (COTS) NAND Flash memory module using programming disturbances, and examine the robustness of its responses to environmental variations. In particular, we test a removable Flash memory module serving as a PUF, under nominal conditions, as well as under temperature and voltage variations. To determine its resilience to environmental variations, we utilise well-known PUF metrics, such as the Hamming weight and the intra-device Hamming distance. Our results prove that, in general, the tested Samsung K9F1G08U0E NAND Flash memory can be used to realise a lightweight, scalable, and flexible hardware security primitive, namely a PUF, that can be utilised in the context of smart homes, smart vehicles, and other smart applications, as well as to protect commercial devices and networks in general. However, voltage variations seem to pose a substantial threat to the adoption of this PUF in practice. This threat may be addressed by small-scale design improvements that should be implemented and tested in practice as part of future works.

This work concerns the demonstration of a security solution for a network of networks, which comprises heterogeneous devices and utilises diverse communication protocols. The security solution used in this work employs an architecture presented in a previous work, which is based upon the concept of hardware and software security co-engineering.