Cyber security incidents can have dramatic economic, social and institutional impact. The task of providing an adequate cyber-security posture to companies and organisations is far far from trivial and need the collection of information about threats from a wide range of sources. One such a source is history in the form of datasets containing information about past cyber-security incidents including date, size, type of attacks, and industry sector. Unfortunately, there are few publicly available datasets of this kind that are of good quality. The paper reports our initial efforts in building a large datasets of cyber-security incidents that contains around 14,000 entries by merging a collection of four publicly available datasets of different size and provenance. We also perform an analysis of the combined dataset, discuss our findings, and discuss the limitations of the proposed approach.
Learning from others' mistakes: An analysis of cyber-security incidents / Abbiati, G.; Ranise, S.; Schizzerotto, A.; Siena, A.. - (2019), pp. 299-306. (Intervento presentato al convegno 4th International Conference on Internet of Things, Big Data and Security, IoTBDS 2019 tenutosi a grc nel 2019).
Learning from others' mistakes: An analysis of cyber-security incidents
Abbiati G.;Ranise S.;Schizzerotto A.;
2019-01-01
Abstract
Cyber security incidents can have dramatic economic, social and institutional impact. The task of providing an adequate cyber-security posture to companies and organisations is far far from trivial and need the collection of information about threats from a wide range of sources. One such a source is history in the form of datasets containing information about past cyber-security incidents including date, size, type of attacks, and industry sector. Unfortunately, there are few publicly available datasets of this kind that are of good quality. The paper reports our initial efforts in building a large datasets of cyber-security incidents that contains around 14,000 entries by merging a collection of four publicly available datasets of different size and provenance. We also perform an analysis of the combined dataset, discuss our findings, and discuss the limitations of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione