Percorrer por autor "Maia, Eva"
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- Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness DetectionPublication . Vitorino, João; Rodrigues, Lourenço; Maia, Eva; Praça, Isabel; Lourenço, AndréDrowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), and an adversarial robustness analysis to assess their reliability when processing faulty input data and perturbed HRV signals. The most reliable model was Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and 150 s. Furthermore, the 18 most impactful features were selected and new smaller models were trained, achieving a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, so it can be a valuable approach to provide a more robust driver drowsiness detection.
- A Comparative Analysis of Machine Learning Techniques for IoT Intrusion DetectionPublication . Vitorino, João; Andrade, Rui; Praça, Isabel; Sousa, Orlando Jorge Coelho Moura; Maia, EvaThe digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.
- Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic reviewPublication . Vitorino, João; Dias, Tiago; Fonseca, Tiago; Maia, Eva; Praça, IsabelEvery novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in automated testing tools. Therefore, this systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods.
- Consumo de fármacos, suplementos e fitoterápicos, e risco de interações: revisão sistemática do impacto das crenças e do conhecimentoPublication . Dores, Artemisa Rocha; Peixoto, Miguel; Castro, Maria; Sã, Catarina; Martins, Andreia; Maia, Eva; Praça, Isabel; ForPharmacy team; Marques, AntónioO aumento do consumo de diversos produtos naturais e em particular de suplementos para fins diversos, como melhoria do desempenho físico e/ou intelectual, tem aumentando nos últimos anos, com consequências negativas para a saúde, algumas fatais. A falta de conhecimento sobre estes produtos, crenças erradas, a aquisição sem aconselhamento e em locais pouco seguros parecem contribuir para esta realidade que precisa de investigação adicional (Dores et al., 2021)
- Herb-Drug Interactions: A Holistic Decision Support System in HealthcarePublication . Martins, Andreia; Maia, Eva; Praça, IsabelComplementary and alternative medicine are commonly used concomitantly with conventional medications leading to adverse drug reactions and even fatality in some cases. Furthermore, the vast possibility of herb-drug interactions prevents health professionals from remembering or manually searching them in a database. Decision support systems are a powerful tool that can be used to assist clinicians in making diagnostic and therapeutic decisions in patient care. Therefore, an original and hybrid decision support system was designed to identify herb-drug interactions, applying artificial intelligence techniques to identify new possible interactions. Different machine learning models will be used to strengthen the typical rules engine used in these cases. Thus, using the proposed system, the pharmacy community, people's first line of contact within the Healthcare System, will be able to make better and more accurate therapeutic decisions and mitigate possible adverse events.
- Holistic Security and Safety for Factories of the FuturePublication . Maia, Eva; Wannous, Sinan; Dias, Tiago; Praça, Isabel; Faria, AnaThe accelerating transition of traditional industrial processes towards fully automated and intelligent manufacturing is being witnessed in almost all segments. This major adoption of enhanced technology and digitization processes has been originally embraced by the Factories of the Future and Industry 4.0 initiatives. The overall aim is to create smarter, more sustainable, and more resilient future-oriented factories. Unsurprisingly, introducing new production paradigms based on technologies such as machine learning (ML), the Internet of Things (IoT), and robotics does not come at no cost as each newly incorporated technique poses various safety and security challenges. Similarly, the integration required between these techniques to establish a unified and fully interconnected environment contributes to additional threats and risks in the Factories of the Future. Accumulating and analyzing seemingly unrelated activities, occurring simultaneously in different parts of the factory, is essential to establish cyber situational awareness of the investigated environment. Our work contributes to these efforts, in essence by envisioning and implementing the SMS-DT, an integrated platform to simulate and monitor industrial conditions in a digital twin-based architecture. SMS-DT is represented in a three-tier architecture comprising the involved data and control flows: edge, platform, and enterprise tiers. The goal of our platform is to capture, analyze, and correlate a wide range of events being tracked by sensors and systems in various domains of the factory. For this aim, multiple components have been developed on the basis of artificial intelligence to simulate dominant aspects in industries, including network analysis, energy optimization, and worker behavior. A data lake was also used to store collected information, and a set of intelligent services was delivered on the basis of innovative analysis and learning approaches. Finally, the platform was tested in a textile industry environment and integrated with its ERP system. Two misuse cases were simulated to track the factory machines, systems, and people and to assess the role of SMS-DT correlation mechanisms in preventing intentional and unintentional actions. The results of these misuse case simulations showed how the SMS-DT platform can intervene in two domains in the first scenario and three in the second one, resulting in correlating the alerts and reporting them to security operators in the multi-domain intelligent correlation dashboard.
- Knowledge and beliefs about herb/supplement consumption and herb/supplement–drug Interactions among the general population, including healthcare professionals and pharmacists: a systematic review and guidelines for a smart decision systemPublication . Dores, Artemisa R.; Peixoto, Miguel; Castro, Maria; Sá, Catarina; Carvalho, Irene P.; Martins, Andreia; Maia, Eva; Praça, Isabel; Marques, AntónioThe increased consumption of a variety of herbs/supplements has been raising serious health concerns. Owing to an inadequate understanding of herb/supplement–drug interactions, the simultaneous consumption of these products may result in deleterious effects and, in extreme cases, even fatal outcomes. This systematic review is aimed at understanding the knowledge and beliefs about the consumption of herbs/supplements and herb/drug–supplement interactions (HDIs). The study follows the PRISMA guidelines. Four online databases (Web of Science; PubMed; Cochrane; and EBSCOhost) were searched, and a total of 44 studies were included, encompassing 16,929 participants. Herb and supplement consumption is explained mostly by the reported benefits across multiple conditions and ease of use. Regarding HDIs, most people take both herbs/supplements and prescription drugs simultaneously. Only a small percentage of participants have knowledge about their interaction effects, and many reported adverse interactions or side effects. Nevertheless, the main reason for stopping the prescribed drug intake is the perceived lack of its effect, and not due to interactions. Therefore, it is important to increase the knowledge about supplement use so that further strategies can be elaborated to better detect or be alert for whenever a potentially dangerous reaction and/or interaction may occur. This paper raises awareness regarding the need for developing a decision support system and ends with some considerations about the development of a technological solution capable of detecting HDIs and, thereby, aiding in the improvement of pharmacy services.
- LEMMAS: a secured and trusted Local Energy Market simulation systemPublication . Andrade, Rui; Vitorino, João; Wannous, Sinan; Maia, Eva; Praça, IsabelThe ever changing nature of the energy grid and the addition of novel systems such as the Local Energy Market (LEM) drastically increase its complexity, thus making the management harder and with increased importance at local level. Providing innovative and advanced management solutions is fundamental for the success of this new distributed energy grid paradigm. In this paper we extend Multi-Agent System (MAS) based simulation tool for LEMs called LEMMAS. A cyberattack detection model is developed and integrated in LEMMAS with the objective of preventing cyber-attacks from affecting the negotiations. This model is compared with the previous version which only analysed the trustworthiness of participants. The results show that the cyber-attack detection model drastically increases the security capabilities of LEMMAS.
- A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the FuturePublication . Bécue, Adrien; Maia, Eva; Feeken, Linda; Borchers, Philipp; Praça, IsabelIn the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.
- SMS-I: Intelligent Security for Cyber–Physical SystemsPublication . Maia, Eva; Sousa, Norberto; Oliveira, Nuno; Wannous, Sinan; Sousa, Orlando; Praça, IsabelCritical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.