arXiv subjects

Giacomo Zonneveld

Publications and source records attributed to Giacomo Zonneveld.

3 recordsLinked to original sources

arXiv2026

A forgery attack on the Block.co blockchain-based digital credential certification system

Certification of digital documents, such as academic credentials, seems a particularly suitable application for the use of blockchain and distributed ledger technologies. Indeed, these technologies enable decentralized certification systems that rely on the immutability and persistence of their distributed ledgers. However, in the absence of a central trusted authority, it is not easy to guarantee the authenticity of the connection between the real identity of an academic institution and the digital identity of the certificate issuer. In this paper, we demonstrate that one of such systems, known as Block.co, has a vulnerability that allows the production of forged certificates that are recognized as valid by the system. Since this is an inherent limitation of the approach used for blockchain-based certification, our attack is likely to be extendable to other systems adopting the same approach.

cs.CR↗

arXiv2025

Data Certification Strategies for Blockchain-based Traceability Systems

The use of blockchains for data certification and traceability is now well established in both the literature and practical applications. However, while blockchain-based certification of individual data is clear and straightforward, the use of blockchain to certify large amounts of data produced on a nearly continuous basis still poses some challenges. In such a case, in fact, it is first necessary to collect the data in an off-chain buffer, and then to organize it, e.g., via Merkle trees, in order to keep the size and quantity of certification data to be written to the blockchain small. In this paper, we consider a typical system for blockchain-based traceability of a production process, and propose and comparatively analyze some strategies for certifying the data of such a process on blockchain, while maintaining the possibility of verifying their certification in a decentralized way.

cs.CR↗

arXiv2024

Using Graph Theory for Improving Machine Learning-based Detection of Cyber Attacks

Early detection of network intrusions and cyber threats is one of the main pillars of cybersecurity. One of the most effective approaches for this purpose is to analyze network traffic with the help of artificial intelligence algorithms, with the aim of detecting the possible presence of an attacker by distinguishing it from a legitimate user. This is commonly done by collecting the traffic exchanged between terminals in a network and analyzing it on a per-packet or per-connection basis. In this paper, we propose instead to perform pre-processing of network traffic under analysis with the aim of extracting some new metrics on which we can perform more efficient detection and overcome some limitations of classical approaches. These new metrics are based on graph theory, and consider the network as a whole, rather than focusing on individual packets or connections. Our approach is validated through experiments performed on publicly available data sets, from which it results that it can not only overcome some of the limitations of classical approaches, but also achieve a better detection capability of cyber threats.

cs.CR↗