Abstract - This paper presents an in-depth description of a multi-modal security solution based on a blockchain architecture developed within the CHRISS (Critical infrastructure High accuracy and Robustness increase Integrated Synchronization Solutions) project. Specifically, the focus in the paper is on the description of the proposed security architecture, functionalities and security measures based on modern blockchain solutions developed for the CHRISS project.

Entity recognition, also known as named entity recognition (NER), is a fundamental task in natural language processing (NLP) that involves identifying and categorizing entities within text. These entities, such as names of people, organizations, locations, dates, and numerical values, provide structured information from unstructured text data. NER models, ranging from rule-based to machine learning-based approaches, decode linguistic patterns and contextual information to extract entities effectively. This article explores the roles of entities, tokens, and NER models in NLP, detailing their significance in various applications like information retrieval and border security. It delves into the practices of implementing NER in legal document analysis, travel history analysis, and document verification, showcasing its transformative impact in streamlining processes and enhancing security measures. Despite challenges such as ambiguity and data scarcity, ongoing research and emerging trends in multilingual NER and ethical considerations promise to drive innovation in the field. By addressing these challenges and embracing new developments, entity recognition is poised to continue advancing NLP capabilities and powering diverse real-world applications.

The paper presents an overview of Call Detail Records (CDRs), important datasets within the telecommunications industry that capture detailed information about telephonic activities. CDRs encompass a vast array of metadata, including the date, time, duration, source, destination of calls, and more specific details like service type, call status, and location data. This paper highlights the significant potential of CDRs in various applications, ranging from telecom fraud detection, urban planning, disaster readiness, to novel methodologies for predicting socio-economic metrics like poverty. We discuss specific use cases, demonstrating the critical role of CDRs in identifying SIM box fraud, analyzing urban mobility, and enhancing emergency response strategies through sophisticated data analysis techniques. Furthermore, we introduce a novel CDR Generator solution developed within the RITHMS project (G.A. 101073932) aimed at detecting suspicious activities around archaeological sites. The tool leverages modern technologies such as Python, Streamlit, and Folium, to generate synthetic CDRs based on selected geographic areas, demonstrating the applicability of CDRs in safeguarding cultural heritage. We find that the generated dataset largely preserves the properties of the real dataset, thus being of real use for the research community suffering from the limited availability of datasets from either practical simulations or experimental testbeds that can be considered as reference or standard datasets

The increase in the number of Internet users has also led to an increase in activities leading to a cyber threat and fraud intelligence. These activities include the use of the Dark Web for coordination and virtual currencies for funding. This article will present the main methods of cyber-attacks and crimes used nowadays, and how they can be prevented by using tools specialized in monitoring transactions with virtual currencies and detecting web pages that pose a threat to users. The tools that will be described in this article are Graphsense used to analyze virtual currency activities and SpiderFoot used to identify Cyber Threat, Attack Surfaces, Security Assessments and Asset Discovery.

In the current industrial century, smart grid is one of the technologies that has been proposed for efficient and quality distribution of electricity. However, this technology is exposed to many security threats and vulnerabilities. These challenges have led to the development of advanced technologies and sustainable solutions to make smart grids more secure and reliable. Blockchain is one of the recent technologies that has attracted a lot of attention in various applications, including smart grids. SealedGRID is a project designed, analyzed and implemented with the aim of providing a scalable and reliable Smart Grid security platform based on blockchain. In this paper, we present a scalable and secure solution for smart grids using Hyperledger Fabric and MQTT.

Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.