The pervasive spread of disinformation across social media platforms has become a significant global challenge, disrupting democratic processes, undermining public trust, and fueling societal polarization. Existing approaches often neglect the dynamic and structural mechanisms that drive the spread and adoption of false narratives. This article leverages the well-established principles and methodologies of Statistical Mechanics and introduces a dynamic Mean-Field framework to model the evolution of disinformation within social networks. The framework introduces innovative elements, including heterogeneous coupling strengths to capture diverse social influences among network users, memory effects to account for cognitive inertia or belief re-evaluation and a three-state Potts model to represent polarization and neutrality in opinion dynamics. It employs the concept of effective fields to integrate external disinformation campaigns, facilitating a detailed analysis of critical thresholds and phase transitions. Monte Carlo simulations are performed to further illustrate the transient and equilibrium dynamics of belief adoption and rejection. Our findings provide actionable insights for the disinformation spread and offer a theoretical foundation for designing targeted interventions to mitigate its harmful effects on societies.

Modern technologies have enabled illicit firearms trafficking to partially merge with cybercrime, while also allowing its off-line aspects to become increasingly complex. The online trade of firearms, their components, 3D blueprints and illicit substances carried out by criminals on both the surface Web and dark Web is increasingly difficult to address as a consequence of the exponential growth in the amount of information disseminated on the Internet. On the other hand, law enforcement agencies are confronted with significant challenges that require the development of sophisticated technological solutions capable of processing large volumes of data, identifying relevant information in a timely manner and creating networks of connections between potential criminal groups. This article presents a real-world practical system, namely the CEASEFIRE one, powered by advanced artificial intelligence technologies that can assist law enforcement personnel in addressing the above described challenges.

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

As cybercrime is becoming increasingly sophisticated, effective cybersecurity is crucial to safeguard digital assets and protect critical infrastructures from emerging threats. Several security applications exploit recent advances in (Big) data analysis and Artificial Intelligence (AI) to prevent and respond to malicious activities. Towards this direction, supervised and unsupervised Machine Learning (ML) methods are used to detect anomalies or reveal patterns that may indicate potential threats. However, the successful implementation of these technologies requires security practitioners to undergo specialized training to fully understand and use AI-driven tools and data analytics. On the other hand, AI models themselves are vulnerable to a variety of cyber threats, which can compromise their training data and learning processes. To ensure the safe operation of these systems, especially when deployed in adversarial environments, it is crucial to create novel AI adversarial algorithms and models that are resilient against diverse security threats. This work presents a conceptual framework based on Large Language Models (LLMs) supported by a Multi-Agent layer for training of security practitioners in various advanced technologies and enhance ML models ability to detect and respond to emerging cyber threats effectively.

During the last decades, the dark web has become the ground for criminal activities, enabling for illegal content sharing, as well as marketplaces selling drugs and firearms. The 2023 Internet Organised Crime Threat Assessment (IOCTA) of Europol’s European Cybercrime Centre (EC3) presented the dark web as the one of the top crime ecosystems. Analyzing the .onion sites hosting marketplaces is of interest to law enforcement, security researchers, and big data analysts. The capability of automatically harvesting web content from web servers enables Law Enforcement Agencies (LEAs) to collect and preserve data prone to serve as potential clues or evidence in investigations. The use of sophisticated protocols and the inherent complexity of the Dark Web makes it difficult for security agencies to identify and investigate these activities through conventional methods. The sheer size, unpredictable ecosystem, and anonymity provided by the Dark Web are the essential confrontations to trace the criminals. Therefore, it is a crucial step to discover the potential solutions towards cyber-crimes evaluating the sailing Dark Web crime threats. In this paper, we devise Artificial Intelligence and Big Data processing to extract insights and investigate how the Dark Web facilitates crime and dynamically maintains marketplaces with illegal goods exchange. The scientific contribution of this paper entails novel textual and multimedia analytics over large collections of data to collect evidence for further investigations and support visual reporting and alerting mechanisms. The conclusions include practical implications of Dark Web content retrieval and archival, such as investigation clues and evidence, and related future research topics.

This paper presents a novel framework for the thorough analysis of fake news and disinformation campaigns, which have the potential to result in both offline and online criminal activities. Its primary focus relies on the spread analysis of disinformation across social media and online platforms, aiming to uncover the underlying dynamics and mechanisms driving the dissemination of false information. The framework integrates state-of-the-art Natural Language Processing (NLP) techniques for sentiment analysis, Deep Learning (DL) algorithms for prediction of criminal activties related to the disiformation spread and graph analysis to identify key actors and propagation pathways. To address the emerging challenges of disinformation that transcend the online realm and have tangible real-world consequences, the framework extends its analysis to potential offline actions incited by disinformation, such as acts of violence and public unrest or the disruption of public health efforts especially in case of pandemics. By exploring the complex interconnections between disinformation and crimes, our research aims to contribute to a deeper understanding of the societal implications of false information and provide actionable insights for policymakers, security practitioners and the broader public.