arXiv subjects

Jessica De Pascale

Publications and source records attributed to Jessica De Pascale.

4 recordsLinked to original sources

arXiv2026

Internet-of-Things Architectures for Secure Cyber-Physical Spaces: the VISOR Experience Report

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.

cs.CR↗

arXiv2026

Real-world K-Anonymity Applications: the \textsc{KGen} approach and its evaluation in Fraudulent Transactions

K-Anonymity is a property for the measurement, management, and governance of the data anonymization. Many implementations of k-anonymity have been described in state of the art, but most of them are not able to work with a large number of attributes in a "Big" dataset, i.e., a dataset drawn from Big Data. To address this significant shortcoming, we introduce and evaluate \textsc{KGen} an approach to K-anonymity featuring Genetic Algorithms. \textsc{KGen} promotes such a meta-heuristic approach since it can solve the problem by finding a pseudo-optimal solution in a reasonable time over a considerable load of input. \textsc{KGen} allows the data manager to guarantee a high anonymity level while preserving the usability and preventing loss of information entropy over the data. Differently from other approaches that provide optimal global solutions catered for small datasets, \textsc{KGen} works properly also over Big datasets while still providing a good-enough solution. Evaluation results show how our approach can still work efficiently on a real world dataset, provided by Dutch Tax Authority, with 47 attributes (i.e., the columns of the dataset to be anonymized) and over 1.5K+ observations (i.e., the rows of that dataset), as well as on a dataset with 97 attributes and over 3942 observations.

cs.CR↗

arXiv2026

CRATOR: a Dark Web Crawler

Dark web crawling is a complex process that involves specific methodologies and techniques to navigate the Tor network and extract data from hidden services. This study proposes a general dark web crawler designed to extract pages handling security protocols, such as captchas, efficiently. Our approach uses a combination of seed URL lists, link analysis, and scanning to discover new content. We also incorporate methods for user-agent rotation and proxy usage to maintain anonymity and avoid detection. We evaluate the effectiveness of our crawler using metrics such as coverage, performance and robustness. Our results demonstrate that our crawler effectively extracts pages handling security protocols while maintaining anonymity and avoiding detection. Our proposed dark web crawler can be used for various applications, including threat intelligence, cybersecurity, and online investigations.

cs.CR↗

arXiv2026

Breadcrumbs in the Digital Forest: Tracing Criminals through Torrent Metadata with OSINT

This work investigates the potential of torrent metadata as a source for open-source intelligence (OSINT), with a focus on user profiling and behavioral analysis. While peer-to-peer (P2P) networks such as BitTorrent are well studied with respect to privacy and performance, their metadata is rarely used for investigative purposes. This work presents a proof of concept demonstrating how tracker responses, torrent index data, and enriched IP metadata can reveal patterns associated with high-risk behavior. The research follows a five-step OSINT process: source identification, data collection, enrichment, behavioral analysis, and presentation of the results. Data were collected from The Pirate Bay and UDP trackers, yielding a dataset of more than 60,000 unique IP addresses across 206 popular torrents. The data were enriched with geolocation, anonymization status, and flags of involvement in child exploitation material (CEM). A case study on sensitive e-books shows how such data can help detect possible interest in illicit content. Network analysis highlights peer clustering, co-download patterns, and the use of privacy tools by suspicious users. The study shows that publicly available torrent metadata can support scalable and automated OSINT profiling. This work adds to digital forensics by proposing a new method to extract useful signals from noisy data, with applications in law enforcement, cybersecurity, and threat analysis.

cs.IR↗