City Research Online

The THREAT-ARREST Cyber-Security Training Platform

Soultatos, O., Fysarakis, K., Spanoudakis, G. ORCID: 0000-0002-0037-2600, Koshutanski, H., Damiani, E., Beckers, K., Wortmann, D., Bravos, G. and Ioannidis, M. (2020). The THREAT-ARREST Cyber-Security Training Platform. In: Fournaris, A. P., Athanatos, M., Lampropoulos, K., Ioannidis, S., Hatzivasilis, G., Damiani, E., Abie, H., Ranise, S., Verderame, L., Siena, A. and Garcia-Alfaro, J. (Eds.), IOSEC 2019, MSTEC 2019, FINSEC 2019: Computer Security. (pp. 199-214). Cham, Switzerland: Springer. ISBN 9783030420505

Abstract

Cyber security is always a main concern for critical infrastructures and nation-wide safety and sustainability. Thus, advanced cyber ranges and security training is becoming imperative for the involved organizations. This paper presets a cyber security training platform, called THREAT-ARREST. The various platform modules can analyze an organization’s system, identify the most critical threats, and tailor a training program to its personnel needs. Then, different training programmes are created based on the trainee types (i.e. administrator, simple operator, etc.), providing several teaching procedures and accomplishing diverse learning goals. One of the main novelties of THREAT-ARREST is the modelling of these programmes along with the runtime monitoring, management, and evaluation operations. The platform is generic. Nevertheless, its applicability in a smart energy case study is detailed.

Publication Type: Book Section
Additional Information: This is a post-peer-review, pre-copyedit version of a chapter published in LNCS. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-42051-2_14
Publisher Keywords: Security training, Cyber range, Training programmes, Training exercises, Dynamic adaptation, CTTP, Smart grid Smart energy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: https://openaccess.city.ac.uk/id/eprint/24126
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