Daniel De Pascale

Daniel De Pascale, Giuseppe Cascavilla, Mirella Sangiovanni et al.

Internet of things (IoT) technologies are becoming a more and more widespread part of civilian life in common urban spaces, which are rapidly turning into cyber-physical spaces. Simultaneously, the fear of terrorism and crime in such public spaces is ever-increasing. Due to the resulting increased demand for security, video-based IoT surveillance systems have become an important area for research. Considering the large number of devices involved in the illicit recognition task, we conducted a field study in a Dutch Easter music festival in a national interest project called VISOR to select the most appropriate device configuration in terms of performance and results. We iteratively architected solutions for the security of cyber-physical spaces using IoT devices. We tested the performance of multiple federated devices encompassing drones, closed-circuit television, smart phone cameras, and smart glasses to detect real-case scenarios of potentially malicious activities such as mosh-pits and pick-pocketing. Our results pave the way to select optimal IoT architecture configurations -- i.e., a mix of CCTV, drones, smart glasses, and camera phones in our case -- to make safer cyber-physical spaces' a reality.

Ingmar Bakermans, Daniel De Pascale, Gonçalo Marcelino et al.

Darknet markets (DNMs) facilitate the trade of illegal goods on a global scale. Gathering data on DNMs is critical to ensuring law enforcement agencies can effectively combat crime. Manually extracting data from DNMs is an error-prone and time-consuming task. Aiming to automate this process we develop a framework for extracting data from DNMs and evaluate the application of three state-of-the-art Named Entity Recognition (NER) models, ELMo-BiLSTM \citep{ShahEtAl2022}, UniversalNER \citep{ZhouEtAl2024}, and GLiNER \citep{ZaratianaEtAl2023}, at the task of extracting complex entities from DNM product listing pages. We propose a new annotated dataset, which we use to train, fine-tune, and evaluate the models. Our findings show that state-of-the-art NER models perform well in information extraction from DNMs, achieving 91% Precision, 96% Recall, and an F1 score of 94%. In addition, fine-tuning enhances model performance, with UniversalNER achieving the best performance.