arXiv subjects

Francesco Zola

Publications and source records attributed to Francesco Zola.

At least 19 recordsLinked to original sources

arXiv2026

A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis

Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrialization of phishing through widely available phishing kits and reusable templates, which enable cybercriminals to rapidly generate and deploy large numbers of fraudulent webpages. Although surface-level attributes may differ across these websites, their underlying structures often exhibit significant similarities. However, most existing defenses rely on reactive blocklists or supervised classification models that focus on individual phishing instances, limiting their ability to identify structural reuse and detect coordinated phishing campaigns. To address this limitation, this study investigates whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse. We model webpages as Document Object Model (DOM) trees and extract structural features, optionally enriched with HTML tag-based content information. These representations are then clustered using unsupervised learning methods to group structurally similar webpages. Three clustering algorithms are evaluated and compared, while also analyzing how the depth of the extracted DOM-tree affects cluster formation and overall clustering performance. Finally, cluster quality is also evaluated both quantitatively and qualitatively, including a novel level-wise Jaccard Distance Score and manual inspection supported by visualization tools. Results demonstrate that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis of coordinated phishing threats

cs.LG↗

arXiv2026

Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

This study addresses the challenge of creating datasets for cybercrime analysis while complying with the requirements of regulations such as the General Data Protection Regulation (GDPR) and Organic Law 10/1995 of the Penal Code. To this end, a system is proposed for collecting information from the Telegram platform, including text, audio, and images; the implementation of speech-to-text transcription models incorporating signal enhancement techniques; and the evaluation of different Named Entity Recognition (NER) solutions, including Microsoft Presidio and AI models designed using a transformer-based architecture. Experimental results indicate that Parakeet achieves the best performance in audio transcription, while the proposed NER solutions achieve the highest f1-score values in detecting sensitive information. In addition, anonymization metrics are presented that allow evaluation of the preservation of structural coherence in the data, while simultaneously guaranteeing the protection of personal information and supporting cybersecurity research within the current legal framework.

cs.LG↗

arXiv2026

Synthetic Pattern Generation and Detection of Financial Activities using Graph Autoencoders

Illicit financial activities such as money laundering often manifest through recurrent topological patterns in transaction networks. Detecting these patterns automatically remains challenging due to the scarcity of labeled real-world data and strict privacy constraints. To address this, we investigate whether Graph Autoencoders (GAEs) can effectively learn and distinguish topological patterns that mimic money laundering operations when trained on synthetic data. The analysis consists of two phases: (i) data generation, where synthetic samples are created for seven well-known illicit activity patterns using parametrized generators that preserve structural consistency while introducing realistic variability; and (ii) model training and validation, where separate GAEs are trained on each pattern without explicit labels, relying solely on reconstruction error as an indicator of learned structure. We compare three GAE implementations based on three distinct convolutional layers: Graph Convolutional (GAE-GCN), GraphSAGE (GAE-SAGE), and Graph Attention Network (GAE-GAT). Experimental results show that GAE-GCN achieves the most consistent reconstruction performance across patterns, while GAE-SAGE and GAE-GAT exhibit competitive results only in few specific patterns. These findings suggest that graph-based representation learning on synthetic data provides a viable path toward developing AI-driven tools for detecting illicit behaviors, overcoming the limitations of financial datasets.

cs.LG↗

arXiv2025

ART: A Graph-based Framework for Investigating Illicit Activity in Monero via Address-Ring-Transaction Structures

As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems

cs.CR↗

arXiv2025

Inside LockBit: Technical, Behavioral, and Financial Anatomy of a Ransomware Empire

LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial apparatus. We recall the family's version timeline and map its tactics, techniques, and procedures to MITRE ATT&CK, highlighting the incremental hardening that distinguishes LockBit 3.0 from its predecessors. We then analyze 51 negotiation chat logs using natural-language embeddings and clustering to infer a canonical interaction playbook, revealing recurrent rhetorical stages that underpin the double-extortion strategy. Finally, we trace 19 Bitcoin addresses related to ransom payment chains, revealing two distinct patterns based on different laundering phases. In both cases, a small portion of the ransom is immediately split into long-lived addresses (presumably retained by the group as profit and to finance further operations) while the remainder is ultimately aggregated into two high-volume addresses before likely being sent to the affiliate. These two collector addresses appear to belong to distinct exchanges, each processing over 200k BTC. The combined evidence portrays LockBit as a tightly integrated criminal service whose resilience rests on rapid code iteration, script-driven social engineering, and industrial-scale cash-out pipelines.

cs.CR↗

arXiv2025

A Graph Machine Learning Approach for Detecting Topological Patterns in Transactional Graphs

The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics that share operational knowledge and techniques both within and across different environments (fiat-based, crypto-assets, etc.). Traditional rule-based systems lack the adaptability needed to detect sophisticated or coordinated criminal behaviors (patterns), highlighting the need for strategies that analyze actors' interactions to uncover suspicious activities and extract their modus operandi. For this reason, in this work, we propose an approach that integrates graph machine learning and network analysis to improve the detection of well-known topological patterns within transactional graphs. However, a key challenge lies in the limitations of traditional financial datasets, which often provide sparse, unlabeled information that is difficult to use for graph-based pattern analysis. Therefore, we firstly propose a four-step preprocessing framework that involves (i) extracting graph structures, (ii) considering data temporality to manage large node sets, (iii) detecting communities within, and (iv) applying automatic labeling strategies to generate weak ground-truth labels. Then, once the data is processed, Graph Autoencoders are implemented to distinguish among the well-known topological patterns. Specifically, three different GAE variants are implemented and compared in this analysis. Preliminary results show that this pattern-focused, topology-driven method is effective for detecting complex financial crime schemes, offering a promising alternative to conventional rule-based detection systems.

cs.LG↗

arXiv2025

PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction

Phishing attacks pose a significant cybersecurity threat, evolving rapidly to bypass detection mechanisms and exploit human vulnerabilities. This paper introduces PhishKey to address the challenges of adaptability, robustness, and efficiency. PhishKey is a novel phishing detection method using automatic feature extraction from hybrid sources. PhishKey combines character-level processing with Convolutional Neural Networks (CNN) for URL classification, and a Centroid-Based Key Component Phishing Extractor (CAPE) for HTML content at the word level. CAPE reduces noise and ensures complete sample processing avoiding crop operations on the input data. The predictions from both modules are integrated using a soft-voting ensemble to achieve more accurate and reliable classifications. Experimental evaluations on four state-of-the-art datasets demonstrate the effectiveness of PhishKey. It achieves up to 98.70% F1 Score and shows strong resistance to adversarial manipulations such as injection attacks with minimal performance degradation.

cs.CR↗

arXiv2025

Topological Analysis of Mixer Activities in the Bitcoin Network

Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.

cs.CR↗

arXiv2024

Assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?

Regulatory authorities aim to tackle illegal activities by targeting the economic incentives that drive such behaviour. This is typically achieved through the implementation of financial sanctions against the entities involved in the crimes. However, the rise of cryptocurrencies has presented new challenges, allowing entities to evade these sanctions and continue criminal operations. Consequently, enforcement measures have been expanded to include crypto assets information of sanctioned entities. Yet, due to the nature of the crypto ecosystem, blocking or freezing these digital assets is harder and, in some cases, such as with Bitcoin, unfeasible. Therefore, sanctions serve merely as deterrents. For this reason, in this study, we aim to assess the impact of these sanctions on entities' crypto activities, particularly those related to the Bitcoin ecosystem. Our objective is to shed light on the validity and effectiveness (or lack thereof) of such countermeasures. Specifically, we analyse the transactions and the amount of USD moved by punished entities that possess crypto addresses after being sanctioned by the authority agency. Results indicate that while sanctions have been effective for half of the examined entities, the others continue to move funds through sanctioned addresses. Furthermore, punished entities demonstrate a preference for utilising rapid exchange services to convert their funds, rather than employing dedicated money laundering services. To the best of our knowledge, this study offers valuable insights into how entities use crypto assets to circumvent sanctions.

cs.CR↗

arXiv2024

Unveiling Dynamics and Patterns: A Comprehensive Analysis of Spreading Patterns and Similarities in Low-Labelled Ransomware Families

Ransomware has become one of the most widespread threats, primarily due to its easy deployment and the accessibility to services that enable attackers to raise and obfuscate funds. This latter aspect has been significantly enhanced with the advent of cryptocurrencies, which, by fostering decentralisation and anonymity, have transformed this threat into a large-scale outbreak. However, recent reports indicate that a small group of individuals dominate the ransomware ecosystem and try to obfuscate their activity using multiple strains characterised by a short time to live. This scenario suggests that different strains could share mechanisms in ransom collection, fund movement, and money laundering operations. For this reason, this study aims to analyse the address-transaction graphs generated in the Bitcoin network by low-labelled ransomware families. Our goals are to identify payment spreading patterns for evaluating the evolution of ransomware families and to detect similarities among different strains that potentially can be controlled by the same attacker. Specifically, this latter task assigns an address behaviour to each node in the address-transaction graphs according to its dynamics. The distribution of the behaviours in each strain is finally used to evaluate the closeness among different ransomware families. Our findings show that although ransomware families can quickly establish connections with millions of addresses, numerous families require multiple-step analysis. Furthermore, the study demonstrates that the introduced behaviours can effectively be used to highlight similarities among different ransomware strains. The outcome shows that families are similar primarily due to behaviours usually associated with ransom collection and money laundering operations.+

cs.CR↗

arXiv2024

Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing

Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiring deep domain and technical knowledge. These characteristics often limit the engagement of law enforcement and end-users in these technologies that, despite their potential, remain misunderstood. For this reason, in this study, we describe our experience in combining learning and training methods and the potential benefits of gamification to enhance technology transfer and increase adult learning. In fact, in this case, participants are experienced practitioners in professions/industries that are exposed to terrorism financing (such as Law Enforcement Officers, Financial Investigation Officers, private investigators, etc.) We define training activities on different levels for increasing the exchange of information about new trends and criminal modus operandi among and within law enforcement agencies, intensifying cross-border cooperation and supporting efforts to combat and prevent terrorism funding activities. On the other hand, a game (hackathon) is designed to address realistic challenges related to the dark net, crypto assets, new payment systems and dark web marketplaces that could be used for terrorist activities. The entire methodology was evaluated using quizzes, contest results, and engagement metrics. In particular, training events show about 60% of participants complete the 11-week training course, while the Hackathon results, gathered in two pilot studies (Madrid and The Hague), show increasing expertise among the participants (progression in the achieved points on average). At the same time, more than 70% of participants positively evaluate the use of the gamification approach, and more than 85% of them consider the implemented Use Cases suitable for their investigations.

cs.LG↗

arXiv2024

NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness

The usage of Artificial Intelligence (AI) systems has increased exponentially, thanks to their ability to reduce the amount of data to be analyzed, the user efforts and preserving a high rate of accuracy. However, introducing this new element in the loop has converted them into attacked points that can compromise the reliability of the systems. This new scenario has raised crucial challenges regarding the reliability and trustworthiness of the AI models, as well as about the uncertainties in their response decisions, becoming even more crucial when applied in critical domains such as healthcare, chemical, electrical plants, etc. To contain these issues, in this paper, we present NeuralSentinel (NS), a tool able to validate the reliability and trustworthiness of AI models. This tool combines attack and defence strategies and explainability concepts to stress an AI model and help non-expert staff increase their confidence in this new system by understanding the model decisions. NS provide a simple and easy-to-use interface for helping humans in the loop dealing with all the needed information. This tool was deployed and used in a Hackathon event to evaluate the reliability of a skin cancer image detector. During the event, experts and non-experts attacked and defended the detector, learning which factors were the most important for model misclassification and which techniques were the most efficient. The event was also used to detect NS's limitations and gather feedback for further improvements.

cs.LG↗

arXiv2022

Conditional Generative Adversarial Network for keystroke presentation attack

Cybersecurity is a crucial step in data protection to ensure user security and personal data privacy. In this sense, many companies have started to control and restrict access to their data using authentication systems. However, these traditional authentication methods, are not enough for ensuring data protection, and for this reason, behavioral biometrics have gained importance. Despite their promising results and the wide range of applications, biometric systems have shown to be vulnerable to malicious attacks, such as Presentation Attacks. For this reason, in this work, we propose to study a new approach aiming to deploy a presentation attack towards a keystroke authentication system. Our idea is to use Conditional Generative Adversarial Networks (cGAN) for generating synthetic keystroke data that can be used for impersonating an authorized user. These synthetic data are generated following two different real use cases, one in which the order of the typed words is known (ordered dynamic) and the other in which this order is unknown (no-ordered dynamic). Finally, both keystroke dynamics (ordered and no-ordered) are validated using an external keystroke authentication system. Results indicate that the cGAN can effectively generate keystroke dynamics patterns that can be used for deceiving keystroke authentication systems.

cs.CR↗

arXiv2022

Verification system based on long-range iris and Graph Siamese Neural Networks

Biometric systems represent valid solutions in tasks like user authentication and verification, since they are able to analyze physical and behavioural features with high precision. However, especially when physical biometrics are used, as is the case of iris recognition, they require specific hardware such as retina scanners, sensors, or HD cameras to achieve relevant results. At the same time, they require the users to be very close to the camera to extract high-resolution information. For this reason, in this work, we propose a novel approach that uses long-range (LR) distance images for implementing an iris verification system. More specifically, we present a novel methodology for converting LR iris images into graphs and then use Graph Siamese Neural Networks (GSNN) to predict whether two graphs belong to the same person. In this study, we not only describe this methodology but also evaluate how the spectral components of these images can be used for improving the graph extraction and the final classification task. Results demonstrate the suitability of this approach, encouraging the community to explore graph application in biometric systems.

cs.CV↗

arXiv2021

NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices

In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, or network, can cause huge damage for the companies. However, when this attack detection encounters the Artificial Intelligence paradigm, it can be addressed using high-quality classifiers which often need high resource demands in terms of computation or memory usage. This situation has a high impact when the attack classifiers need to be used with limited resourced devices or without overloading the performance of the devices, as it happens for example in IoT devices, or in industrial systems. For overcoming this issue, NBcoded, a novel light attack classification tool is proposed in this work. NBcoded works in a pipeline combining the removal of noisy data properties of the encoders with the low resources and timing consuming obtained by the Naive Bayes classifier. This work compares three different NBcoded implementations based on three different Naive Bayes likelihood distribution assumptions (Gaussian, Complement and Bernoulli). Then, the best NBcoded is compared with state of the art classifiers like Multilayer Perceptron and Random Forest. Our implementation shows to be the best model reducing the impact of training time and disk usage, even if it is outperformed by the other two in terms of Accuracy and F1-score (~ 2%).

cs.LG↗

arXiv2021

Temporal graph-based approach for behavioural entity classification

Graph-based analyses have gained a lot of relevance in the past years due to their high potential in describing complex systems by detailing the actors involved, their relations and their behaviours. Nevertheless, in scenarios where these aspects are evolving over time, it is not easy to extract valuable information or to characterize correctly all the actors. In this study, a two phased approach for exploiting the potential of graph structures in the cybersecurity domain is presented. The main idea is to convert a network classification problem into a graph-based behavioural one. We extract these graph structures that can represent the evolution of both normal and attack entities and apply a temporal dissection approach in order to highlight their micro-dynamics. Further, three clustering techniques are applied to the normal entities in order to aggregate similar behaviours, mitigate the imbalance problem and reduce noisy data. Our approach suggests the implementation of two promising deep learning paradigms for entity classification based on Graph Convolutional Networks.

cs.LG↗

arXiv2020

Generative Adversarial Networks for Bitcoin Data Augmentation

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes. In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no "one-fits-all" GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a "good" GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.

cs.LG↗

arXiv2019

Kriptosare.gen, a dockerized Bitcoin testbed: analysis of server performance

Bitcoin is a peer-to-peer distributed cryptocurrency system, that keeps all transaction history in a public ledger known as blockchain. The Bitcoin network is implicitly pseudoanonymous and its nodes are controlled by independent entities making network analysis difficult. This calls for the development of a fully controlled testing environment. This paper presents Kriptosare.gen, a dockerized automatized Bitcoin testbed, for deploying full-scale custom Bitcoin networks. The testbed is deployed in a single machine executing four different experiments, each one with different network configuration. We perform a cost analysis to investigate how the resources are related with network parameters and provide experimental data quantifying the amount of computational resources needed to run the different types of simulations. Obtained results demonstrate that it is possible to run the testbed with a configuration similar to a real Bitcoin system.

cs.PF↗