Suchergebnisse

14 páginas; 11 tablas; 1 figura ; Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method to detect security incidents from structured text regardless its nature. We use the Normalized Compression Distance to obtain a set of features that can be used by a Support Vector Machine to classify events from a heterogeneous cybersecurity environment. In particular, we explore and validate the application of our method in four different cybersecurity domains: HTTP anomaly identification, spam detection, Domain Generation Algorithms tracking and sentiment analysis. The results obtained show the validity and flexibility of our approach in different security scenarios with a low configuration burden. ; This research has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 872855 (TRESCA project), from the Comunidad de Madrid (Spain) under the projects CYNAMON (P2018/TCS-4566) and S2017/BMD-3688, co-financed with FSE and FEDER EU funds, by the Consejo Superior de Investigaciones Científicas (CSIC) under the project LINKA20216 ("Advancing in cybersecurity technologies", i-LINK+ program), and by Spanish project MINECO/FEDER TIN2017-84452-R.

Amongst all types of fabricated information travelling on open social networks (OSN), scientific misinformation, or cloaked science, is particularly dangerous. Here we present the design of the TRESCA misinformation widget (Ms.W), which is both a methodology and a toolbox for investigating disinformation operations leveraging scientific communications. Ms.W follows a man-in-the-loop approach: the methodology takes into consideration ideological and psychological biases, while the toolbox integrates open source intelligence solutions for verifying the accuracy of claims and the credibility of sources. Overall, Ms.W. is a flexible investigative tool offering a REST API for advanced users, who can create and label datasets and add new functionalities to the toolbox. ; "CYNAMON – Cybersecurity, Network Anal- ysis and Monitoring for the Next Generation Internet", funded by the Madrid Region under "Programas de Actividades de I+D entre grupos de investigación de la Comunidad de Madrid en tecnologı́as 2018" (P2018/TCS-4566; BOCM. No. 304; 21/12/2018) ; European Union's Horizon 2020 Research and Innovation Pro- gramme under Grant Agreement No 872855 (TRESCA) ; Peer reviewed