Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

Smart meters are devices equipped with embedded software that are capable of processing complex digital data. These devices are now a reality in most areas of metrology. They enable a number of new applications, but they also introduce new challenges with regards to their validation. This paper describes initiatives developed by Inmetro, the national metrology institute of Brazil, and Eletrobrás, a Brazilian power utility company, that are designed to support the validation of smart electricity meters.

This chapter provides an overview of the evolving landscape of attacks in cyber-physical systems (CPS) and critical infrastructures, highlighting the possible use of Artificial Intelligence (AI) algorithms to develop intelligent cyberattacks. It describes various existing methods used to carry out intelligent attacks in Operational Technology (OT) environments and discusses AI-driven tools that automate penetration tests in Information Technology (IT) systems, which could potentially be used as attack tools. The chapter also discusses mitigation strategies to counter these emerging intelligent attacks by hindering the learning process of AI-based attacks and points to future research directions on the matter.

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Smart Grids are power grids that use Information and Communication Technologies to improve the power generation, transmission and distribution. The main characteristic on Smart Grids is the ability to process and transmit a huge data information in real-time. However, that rise a new security challenge on confidentiality and data integrity, due to Smart Grids can be part of hostile environments. For example, using a cloud computing to realize billing and load balance process. Thus, to ensure confidentiality and data integrity of Smart Grids in cloud environments, this paper proposes an application for the protection of billing and load balancing data using the Intel Intel Software Guard extension SGX platform. SGX allow to instantiate protected memory areas called enclaves, preventing subprocesses or other machines within the cloud from accessing the data stored in them.

Traceability of codes refers to the mapping between equivalent codes written in different languages – including high-level and low-level programming languages. In the field of Legal Metrology, it is critical to guarantee that the software binary code embedded in a meter corresponds to a program source code that was previously approved by the Legal Metrology Authority. In this paper, we propose a novel approach for correlating source and binary codes using artificial neural networks. Our approach correlates the source code with the binary code by feeding the neural network with logical flow characteristics of such codes. Any incidence of false positives is obviously a critical issue for software evaluation purposes. Our evaluation using real code examples shows a typical correspondence rate between 62% and 90% for the traceability of the binary codes with the very low rate of 4% false positives. Rastreabilidade de códigos refere-se ao mapeamento entre códigos equivalentes escritos em linguagens diferentes — inclusive linguagens de programação de alto nível e de baixo nível. No campo da Metrologia Legal, é fundamental garantir que o código binário de um software embarcado em um medidor corresponde a um código fonte do programa que foi previamente aprovado pela Autoridade Metrológica. Neste trabalho, é proposta uma nova abordagem para correlacionar códigos fonte e binário usando redes neurais artificiais. Nossa abordagem correlaciona o código fonte com o código binário utilizando-se de uma rede neural artificial alimentada pelas características do fluxo lógico do programa. Qualquer incidência de falsos positivos é um fator crítico para fins de avaliação de software. Nossa avaliação, usando exemplos de código real, mostra uma correspondência entre 62% e 90% para a rastreabilidade dos códigos binários com uma taxa de 4% de falsos positivos.