In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter datasets -- focusing on the 2016 U.S. presidential election and the COVID-19 pandemic -- HyperGraphDis outperforms existing methods in both accuracy and computational efficiency, underscoring its effectiveness and scalability for tackling the challenges posed by disinformation dissemination. HyperGraphDis displays exceptional performance on a COVID-19-related dataset, achieving an impressive F1 score (weighted) of approximately 89.5%. This result represents a notable improvement of around 4% compared to the other state-of-the-art methods. Additionally, significant enhancements in computation time are observed for both model training and inference. In terms of model training, completion times are accelerated by a factor ranging from 2.3 to 7.6 compared to the second-best method across the four datasets. Similarly, during inference, computation times are 1.3 to 6.8 times faster than the state-of-the-art.

During the last decade, the dark web has become the playground for criminal and underground activities, such as marketplaces of drugs and guns, as well as illegal content sharing. The dark web is one of the top crime environments presented in EUROPOL’s Internet Organised Crime Threat Assessment 2021. This paper provides a qualitative study on the darknet marketplaces of illegal arms trafficking. For this purpose, we implemented a crawler based on the ACHE Python library to collect hidden web pages (onion services) on the Tor network. We gathered data from ten marketplaces recommended by dark web search engines – Ahmia, Deep Search, and Onion Land Search. We provide a first report of the overall landscape of illicit arms trafficking, discussing the range of weapons such as military drones, explosives, and other related products, together with the payment and shipping methods provided by the vendors. The findings verify previous reports from reputable institutions (United Nations and RAND Europe). Most of these illicit marketplaces are easily accessible to the average user; they are well-organized with a large variety of firearms and also provide extensive customer support.

In an era where all the transactions, businesses and services are becoming digital and online, the data assets and the services protection are of utmost importance. Cyber-insurance companies are offering a wide range of coverages, but they also have exclusions. Customers of these companies need to be able to understand the terms and conditions of the related contracts and furthermore they need to be able to compare various offerings in order to determine the most appropriate solutions for their needs. The research in the area is very limited while at the same time the related market is growing, giving every potential solution a high value. In this paper, we propose a methodology and a prototype system that will help customers to compare contracts based on a pre-defined ontology that is describing cyber-insurance terms. After a first preliminary analysis and validation, our approach accuracy is averaging at almost 50%, giving a promising initial evaluation. Fine tuning, larger data set assessment and ontology refinement will be our next steps to improve the accuracy of our tool. Real user evaluation will follow, in order to evaluate the tool in real world cases.

The study of a large real world network in terms of graph sample representation constitutes a very powerful and useful tool in several domains of network analysis. This is the motivation that has led the work of this paper towards the development of a new graph sampling algorithm. Previous research in this area proposed simple processes such as the classic Random Walk algorithm, Random node and Random edge sampling and has evolved during the last decade to more advanced graph exploration approaches such as Forest Fire and Frontier sampling. In this paper, we propose a new graph sampling method based on edge selection. In addition, we crawled Facebook collecting a large dataset consisting of 10 million users and 80 million users' relations, which we have also used to evaluate our sampling algorithm. The experimental evaluation on several datasets proves that our approach preserves several properties of the initial graphs, leading to representative samples and outperforms all the other approaches.