Research

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The cybersecurity research conducted by the lab is geared toward the development of innovative cyber solutions. Techniques for effective detection of malware and attacks, as well as their respective defense methods, are investigated. Therefore, devices such as (Next Generation) Firewalls, Intrusion Detection Systems and Intrusion Prevention Systems (IDS/IPS), SIEMs, and network probes are being investigated and innovated. Also included in the area of interest is the analysis of security in software, with its application methodologies, as well as Indicators of Compromise (IoC) analysis and filtering, and advanced cryptography techniques.

The biometry research focuses on biometric authentication and biometric data security through cancelable biometrics approaches and biometric cryptosystems. Another core area of interest is integrating advanced behavioral biometrics and cybersecurity techniques, addressing critical topics such as Human Activity Recognition (HAR), the same way, Continuous Authentication through Mobile Devices, with the development of innovative solutions that combine touch events with human activity to provide dynamic and personalized security measures are investigated.

Environmental sustainability represents a fundamental pillar of the research activities conducted by the laboratory, with a particular focus on the application of artificial intelligence (AI) and machine learning to optimize the management of natural resources. The research efforts are dedicated to the development of advanced predictive models capable of improving the forecasting and spatial mapping of meteorological and climatic parameters, with specific attention to variables such as solar radiation, precipitation, and temperature. These models, applied in the agricultural sector, support more accurate and sustainable decision-making, contributing to the efficient management of natural resources. Among the technologies employed, neural networks stand out as innovative solutions that optimize agricultural irrigation management, reducing water consumption and improving crop yields. The adoption of these techniques enables highly precise weather forecasting, which is essential for long-term agricultural planning. The laboratory’s approach emphasizes the importance of sustainable agricultural practices aimed at reducing the waste of natural resources, particularly water, in alignment with global objectives for responsible resource management. The solutions developed are based on real-time data analysis, enhancing resilience to the impacts of climate change and allowing farmers to quickly adapt to environmental variations.

The laboratory is dedicated to the development of advanced solutions for smart cities, with the goal of improving urban quality of life through the strategic and integrated use of data. Research focuses on the creation and implementation of dynamic models, referred to as “Dynamic Layers,” which enable real-time representation of events relevant to social safety and urban dynamics. These informational layers, powered by open data and sourced from heterogeneous origins, provide critical insights into aspects such as incidents, crime, air pollution, and other significant phenomena. The integration of technological tools, including geospatial APIs, environmental data, and local information sources, enables the collection, analysis, and visualization of a wide range of data. This process supports the development of interactive and intuitive systems that allow real-time monitoring of critical factors affecting the safety, sustainability, and livability of cities. The analyses include key aspects such as traffic, air quality, public safety, and social activities. By processing this data, the laboratory identifies critical areas and potential risks, facilitating targeted and proactive interventions. This approach not only enhances urban management but also promotes transparency and active citizen participation. Citizens can access this information to make informed decisions and contribute to improving their urban environment. The interactive platform developed by the laboratory serves as a tangible example of how advanced technology can be used to address the challenges of contemporary cities.

The laboratory’s research in e-health primarily focuses on developing advanced artificial intelligence methodologies applied, with a particular emphasis on the early detection and analysis of neurodegenerative diseases. Leveraging state-of-the-art machine learning and computer vision techniques, the research aims to analyze human gait to identify and classify pathological conditions at an early stage. These efforts contribute in developing innovative tools for improving diagnosis, monitoring disease progression, and enhancing patient care. Expanding its scope beyond medical applications, the laboratory amploys large language models (LLMs) to further its research goals. These models are fine-tuned for specific healthcare-related tasks, such as medical data interpretation, patient record analysis, and the generation of clinical insights, providing customized solutions that address challenges in the medical domain. Research also places significant emphasis on xAI, intending to create AI models that are not only highly accurate, but also understandable.