Integrating AI-driven threat intelligence and forecasting in the cyber security exercise content generation lifecycle
Authors: Zacharis, A., Katos, V. and Patsakis, C.
Journal: International Journal of Information Security
Volume: 23
Issue: 4
Pages: 2691-2710
eISSN: 1615-5270
ISSN: 1615-5262
DOI: 10.1007/s10207-024-00860-w
Abstract:The escalating complexity and impact of cyber threats require organisations to rehearse responses to cyber-attacks by routinely conducting cyber security exercises. However, the effectiveness of these exercises is limited by the exercise planners’ ability to replicate real-world scenarios in a timely manner that is, most importantly, tailored to the training audience and sector impacted. To address this issue, we propose the integration of AI-driven sectorial threat intelligence and forecasting to identify emerging and relevant threats and anticipate their impact in different industries. By incorporating such automated analysis and forecasting into the design of cyber security exercises, organisations can simulate real-world scenarios more accurately and assess their ability to respond to emerging threats. Fundamentally, our approach enhances the effectiveness of cyber security exercises by tailoring the scenarios to reflect the threats that are more relevant and imminent to the sector of the targeted organisation, thereby enhancing its preparedness for cyber attacks. To assess the efficacy of our forecasting methodology, we conducted a survey with domain experts and report their feedback and evaluation of the proposed methodology.
https://eprints.bournemouth.ac.uk/39831/
Source: Scopus
Integrating AI-driven threat intelligence and forecasting in the cyber security exercise content generation lifecycle
Authors: Zacharis, A., Katos, V. and Patsakis, C.
Journal: INTERNATIONAL JOURNAL OF INFORMATION SECURITY
Volume: 23
Issue: 4
Pages: 2691-2710
eISSN: 1615-5270
ISSN: 1615-5262
DOI: 10.1007/s10207-024-00860-w
https://eprints.bournemouth.ac.uk/39831/
Source: Web of Science (Lite)
Integrating AI-driven threat intelligence and forecasting in the cyber security exercise content generation lifecycle
Authors: Zacharis, A., Katos, V. and Patsakis, C.
Journal: International Journal of Information Security
Publisher: Springer Nature
ISSN: 1615-5262
DOI: 10.1007/s10207-024-00860-w
https://eprints.bournemouth.ac.uk/39831/
Source: Manual
Integrating AI-driven threat intelligence and forecasting in the cyber security exercise content generation lifecycle
Authors: Zacharis, A., Katos, V. and Patsakis, C.
Journal: International Journal of Information Security
Volume: 23
Pages: 2691-2710
Publisher: Springer Nature
ISSN: 1615-5262
Abstract:The escalating complexity and impact of cyber threats require organisations to rehearse responses to cyber-attacks by routinely conducting cyber security exercises. However, the effectiveness of these exercises is limited by the exercise planners’ ability to replicate real-world scenarios in a timely manner that is, most importantly, tailored to the training audience and sector impacted. To address this issue, we propose the integration of AI-driven sectorial threat intelligence and forecasting to identify emerging and relevant threats and anticipate their impact in different industries. By incorporating such automated analysis and forecasting into the design of cyber security exercises, organisations can simulate real-world scenarios more accurately and assess their ability to respond to emerging threats. Fundamentally, our approach enhances the effectiveness of cyber security exercises by tailoring the scenarios to reflect the threats that are more relevant and imminent to the sector of the targeted organisation, thereby enhancing its preparedness for cyber attacks. To assess the efficacy of our forecasting methodology, we conducted a survey with domain experts and report their feedback and evaluation of the proposed methodology.
https://eprints.bournemouth.ac.uk/39831/
Source: BURO EPrints