| Title | Innovative Predictive Model for Smart City Security Risk Assessment |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Franchina, L., Socal, A. |
| Conference Name | 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) |
| Keywords | Analytical models, Big Data analytics, consequences calculation, Data models, domino effect, Metrics, Prediction algorithms, predictive model, Predictive models, predictive security metrics, pubcrawl, risk assessment, risk management, security, smart cities, smart city, what “if” methodology |
| Abstract | In a Smart City, new technologies such as big data analytics, data fusion and artificial intelligence will increase awareness by measuring many phenomena and storing a huge amount of data. 5G will allow communication of these data among different infrastructures instantaneously. In a Smart City, security aspects are going to be a major concern. Some drawbacks, such as vulnerabilities of a highly integrated system and information overload, must be considered. To overcome these downsides, an innovative predictive model for Smart City security risk assessment has been developed. Risk metrics and indicators are defined by considering data coming from a wide range of sensors. An innovative ``what if'' algorithm is introduced to identify critical infrastructures functional relationship. Therefore, it is possible to evaluate the effects of an incident that involves one infrastructure over the others. |
| DOI | 10.23919/MIPRO48935.2020.9245358 |
| Citation Key | franchina_innovative_2020 |
Innovative Predictive Model for Smart City Security Risk Assessment