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- Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 TechnologiesPublication . Gaspar, Francisco; Guerreiro, Vitor; Loureiro, Paulo; Costa, Paulo; Mendes, Sílvio; Rabadão, CarlosHuman errors are probably the main cause of car accidents, and this type of vehicle is one of the most dangerous forms of transport for people. The danger comes from the fact that on public roads there are simultaneously different types of actors (drivers, pedestrians or cyclists) and many objects that change their position over time, making difficult to predict their immediate movements. The intelligent transport system (ITS-G5) standard specifies the European communication technologies and protocols to assist public road users, providing them with relevant information. The scientific community is developing ITS-G5 applications for various purposes, among which is the increasing of pedestrian safety. This paper describes the developed work to implement an ITS-G5 prototype that aims at the increasing of pedestrian and driver safety in the vicinity of a pedestrian crosswalk by sending ITS-G5 decentralized environmental notification messages (DENM) to the vehicles. These messages are analyzed, and if they are relevant, they are presented to the driver through a car’s onboard infotainment system. This alert allows the driver to take safety precautions to prevent accidents. The implemented prototype was tested in a controlled environment pedestrian crosswalk. The results showed the capacity of the prototype for detecting pedestrians, suitable message sending, the reception and processing on a vehicle onboard unit (OBU) module and its presentation on the car onboard infotainment system.
- Engineering the application of machine learning in an IDS based on IoT traffic flowPublication . Prazeres, Nuno; Costa, Rogério Luís de C.; Santos, Leonel; Rabadão, CarlosInternet of Things (IoT) devices are now widely used, enabling intelligent services that, in association with new communication technologies like the 5G and broadband internet, boost smart-city environments. Despite their limited resources, IoT devices collect and share large amounts of data and are connected to the internet, becoming an attractive target for malicious actors. This work uses machine learning combined with an Intrusion Detection System (IDS) to detect possible attacks. Due to the limitations of IoT devices and low latency services, the IDS must have a specialized architecture. Furthermore, although machine learning-based solutions have high potential, there are still challenges related to training and generalization, which may impose constraints on the architecture. Our proposal is an IDS with a distributed architecture that relies on Fog computing to run specialized modules and use deep neural networks to identify malicious traffic inside IoT data flows. We compare our IoT-Flow IDS with three other architectures. We assess model generalization using test data from different datasets and evaluate their performance in terms of Recall, Precision, and F1-Score. Results confirm the feasibility of flowbased anomaly detection and the importance of network traffic segmentation and specialized models in the AI-based IDS for IoT.
- Plano para a Igualdade de Género, Não Discriminação e Inclusão 2024/2028Publication . Oliveira, Ana Zita; Rabadão, Carlos Manuel da Silva; Henriques, Carolina Miguel da Graça; Frade, José Manuel Couceiro Barosa Correia; Ferreira, Tânia Isabel MartinsNo âmbito da implementação do Plano para a Igualdade de Género, Não Discriminação e Inclusão do Instituto Politécnico de Leiria, foi elaborado um plano para o horizonte temporal de 2024-2028 tendo em consideração seis dimensões estratégicas para a comunidade académica, valorizando as ações a implementar junto dos colaboradores e dos estudantes.
- GPT and Interpolation-Based Data Augmentation for Multiclass Intrusion Detection in IIoTPublication . Melicias, Francisco S.; Ribeiro, Tiago F. R.; Rabadão, Carlos; Santos, Leonel; Costa, Rogério Luís de C.The absence of essential security protocols in Industrial Internet of Things (IIoT) networks introduces cybersecurity vulnerabilities and turns them into potential targets for various attack types. Although machine learning has been used for intrusion detection in the IIoT, datasets with representative data of common attacks of IIoT network traffic are limited and often imbalanced. Data augmentation techniques address these problems by creating artificial data in classes with fewer samples. In this work, we evaluate the use of data augmentation when training intrusion detection models based on IIoT traffic data. We compare Generative Pre-trained Transformers (GPT) and variations on the Synthetic Minority Over-sampling TEchnique (SMOTE) and evaluate their capability to enhance intrusion detection performance. We examine the performance of five intrusion detection algorithms when trained with augmented datasets to models trained with the original non-augmented dataset. To ensure a fair comparison, we evaluated the algorithms’ performance in the different scenarios using the same test dataset, which does not contain synthetic data. The results show the need for a systematic evaluation before employing data augmentation, as its impact on classification performance depends on the algorithm, data, and used technique. While deep neural networks benefit from data augmentation, the eXtreme Gradient Boosting (XGBoost), which achieved superior performance in intrusion detection between all evaluated classifiers (with F1-Score over 91%), didn’t have any performance improvement when trained with augmented data. The evaluation of data generated by GPT-based methods shows such methods (especially GReaT) generate invalid data for both numerical and categorical features in a way that leads to performance degradation in multiclass classification.
- Cybersecurity and Digital Forensics – Course Development in a Higher Education InstitutionPublication . Antunes, Mário; Rabadão, CarlosIndividuals and companies have a feeling of insecurity in the Internet, as every day a reasonable amount of attacks take place against users’ privacy and confidentiality. The use of digital equipment in illicit and unlawful activities has increasing. Attorneys, criminal polices, layers and courts staff have to deal with crimes committed with digital “weapons”, whose evidences have to be examined and reported by applying digital forensics methods. Digital forensics is a recent and fast-growing area of study which needs more graduated professionals. This fact has leveraged higher education institutions to develop courses and curricula to accommodate digital forensics topics and skills in their curricular offers. This paper aims to present the development of a cybersecurity and digital forensics master course in Polytechnic of Leiria, a public higher education institution in Portugal. The authors depict the roadmap and the general milestones that lead to the development of the course. The strengths and opportunities are identified and the major students’ outcomes are pointed out. The way taken and the decisions made are also approached, with a view to understanding the performance obtained so far.
- Gender Equality, Non-Discrimination, and Inclusion Plan 2024/2028Publication . Oliveira, Ana Zita; Rabadão, Carlos Manuel da Silva; Henriques, Carolina Miguel da Graça; Frade, José; Ferreira, Tânia Isabel MartinsWithin the scope of the implementation of the Gender Equality, Non-Discrimination, and Inclusion Plan of the Polytechnic University of Leiria, a plan was developed for the 2024-2028 period. This plan encompasses six strategic dimensions for the academic community, focusing on the actions to be implemented with staff and students.
- Evaluation of AI-based Malware Detection in IoT Network TrafficPublication . Prazeres, Nuno; Costa, Rogério Luís de C.; Santos, Leonel; Rabadão, CarlosInternet of Things (IoT) devices have become day-to-day technologies. They collect and share a large amount of data, including private data, and are an attractive target of potential attackers. On the other hand, machine learning has been used in several contexts to analyze and classify large volumes of data. Hence, using machine learning to classify network traffic data and identify anomalous traffic and potential attacks promises. In this work, we use deep and traditional machine learning to identify anomalous traffic in the IoT-23 dataset, which contains network traffic from real-world equipment. We apply feature selection and encoding techniques and expand the types of networks evaluated to improve existing results from the literature. We compare the performance of algorithms in binary classification, which separates normal from anomalous traffic, and in multiclass classification, which aims to identify the type of attack.
- Towards Industry 4.0 | A case study of BIM Deployment in Ornamental Stones SectorPublication . Rabadão, Carlos; Capela, Carlos; Silva, AgostinhoThe transition witnessed from the Third to the Fourth Industrial Age leads to the emergence of paradigms such as BIM, seeking efficiency in Architecture, Engineering and Construction (AEC) through a global approach and procurement oriented towards standardized products and I4.0, where production comes to be supported by Cyber-Physical Systems (CPS). Integrated in the AEC supply chain, the ornamental stone sector shows Portugal to be the eighth country in OS trade worldwide, and the second per capita, with its competitiveness coming from customization BIM represents threats for its business sustainability. Supported by the Service Science, the main objective of this research is to conceptualize an empirical framework, which, when applied to a sample of Ornamental Stone companies, allows a conclusive answer how to keep their competitive advantage of products customization in BIM-standardized procurement environment. By monitoring the entire sequence of events, it was found that the Ornamental Stone companies can retain their main current competitive advantage of customizing their products, which leads us to conclude that Industry 4.0 technologies, appears to respond positively to threat raised from BIM procurement.
- Impacto das Tecnologias Digitais na Produtividade e Exportações do Setor das Rochas Ornamentais PortuguesasPublication . Dionísio, Andreia; Rabadão, Carlos; Capela, Carlos; Agostinho da SilvaO presente estudo visa dar resposta a uma questão atual do Setor das Rochas Ornamentais (RO) Português: “Qual o Impacto das Tecnologias Digitais na Produtividade e Exportações do Setor?”. Partindo do conhecimento concreto deste impacto (ou impactos), pretende-se contribuir para a definição de uma estratégia que incentive o desenvolvimento concertado e integrado de um Setor que em 2019 cresceu mais de 15% em exportações, identificando alguns dos fatores críticos que estão por detrás desta tendência de crescimento e, acima de tudo, projetar cenários futuros que possam ajudar a orientação das políticas públicas nos próximos anos. Por via de um modelo de análise da evolução da Produtividade e recorrendo a uma metodologia quantitativa, foi possível concluir que o investimento de 1€, num cenário moderado (ou médio), poderá aportar em termos médios, nos primeiros 5 anos após o investimento, um retorno de 5,26€, em Volume de Negócios e 7,29€, em Exportações. Analisando as projeções, a partir de um cenário pessimista, verifica-se que 1€ de investimento poderá gerar um retorno de 5€ nos primeiros 5 anos e, num cenário otimista, 12,5 € por cada Euro investido. Conclui-se com o presente estudo que, (i) com políticas publicas que incentivem a digitalização do Setor, (ii) com abertura das empresas “ao digital” e à (re)qualificação dos seus recursos humanos e (iii) com a monitorização e apoio à transição digital por parte das organizações setoriais, resultarão ganhos muito significativos para o Setor e, naturalmente, para a economia Portuguesa.
- National cyber-security policies oriented to BYOD (bring your own device): Systematic reviewPublication . Herrera, Andrea Vaca; Ron, Mario; Rabadão, CarlosThere are some corporate policies in most of companies around the world, focus on mobile devices to be used as BYOD (Bring Your Own Device), but in Ecuador, these policies are not being established yet. In spite of that, this technology has been used frequently, even do some employers don’t allow to use employees’ mobile devices because of the inherent security risks, without being aware that BYOD carries a lot of advantages such as increase the company’s economy, improve its communication skills and help to work at home, which now is a good alternative. Business policies should follow national policy guidelines as already happens in several countries, but in the case of Ecuador, such guidelines are not present in a formal way, the regulatory framework of this human activity that must come from the state has not been developed, not only to regulate the activity, but also to guide its use in a safe and proper manner providing privacy to users in all social and economic activities. This research begins focusing on a systematic review about BYOD’s actual situation, its trend and impact, it will continue with the local situation and a proposal of recommendations oriented to have a National Policy in Ecuador.