Network models for cyber attacks evaluation

The significant recent growth in digitization has been accompanied by a rapid increase in cyber attacks\r\naffecting all sectors. Thus, it is fundamental to make a correct assessment of the risk to suffer a cyber attack and\r\nof the resulting damage. Quantitative loss data are rarely available, while it is possible to obtain a qualitative\r\nevaluation on an ordinal scale of the gravity of an attack from experts of the sector. In this paper, we discuss\r\nhow network models can be useful instruments for the evaluation of the risk associated to a cyber attack. In\r\nparticular, we consider Bayesian Networks, Random Forests and Social Networks to study different aspects of\r\nthe examined problem. Along with the description of the methodology, we examine a real set of data regarding\r\nserious cyber attacks occurred worldwide before and during the pandemic due to Covid-19. In the analysis,\r\nwe also investigate how the Covid-19 period had an impact on the cyber risk landscape in terms of frequency\r\nand gravity of the observed attacks.