Journal Article

Integrating AI-driven threat intelligence and forecasting in the cyber security exercise content generation lifecycle

Zacharis, Alexandros, Katos, Vasilios, Patsakis, Constantinos

2024

Overview

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.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Artificial intelligence / Coding and Information Theory / Communications Engineering / Computer Communication Networks / Computer Science / Cryptology / Cybersecurity / Effectiveness / Efficacy / Feedback / Forecasting / Impact analysis / Information gathering / Intelligence / Intelligence gathering / Internet / Management of Computing and Information Systems / Networks / Operating Systems / Regular Contribution / Security / Threat evaluation / Threats