| 1. |
- Bellini, Emanuele, et al.
(författare)
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Resilience learning through self adaptation in digital twins of human-cyber-physical systems
- 2021
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Ingår i: Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience (CSR). - : IEEE. - 9781665402859 - 9781665402866 ; , s. 168-173
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Konferensbidrag (refereegranskat)abstract
- Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and implications, which in turn require the adoption of advanced monitoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms, data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation to time-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.
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| 2. |
- Perrone, P., et al.
(författare)
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Machine Learning for Threat Recognition in Critical Cyber-Physical Systems
- 2021
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Ingår i: 2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021. - 9781665402859 ; , s. 298-303
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Konferensbidrag (refereegranskat)abstract
- Cybersecurity has become an emerging challenge for business information management and critical infrastructure protection in recent years. Artificial Intelligence (AI) has been widely used in different fields, but it is still relatively new in the area of Cyber-Physical Systems (CPS) security. In this paper, we provide an approach based on Machine Learning (ML) to intelligent threat recognition to enable run-time risk assessment for superior situation awareness in CPS security monitoring. With the aim of classifying malicious activity, several machine learning methods, such as k-nearest neighbours (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF), have been applied and compared using two different publicly available real-world testbeds. The results show that RF allowed for the best classification performance. When used in reference industrial applications, the approach allows security control room operators to get notified of threats only when classification confidence will be above a threshold, hence reducing the stress of security managers and effectively supporting their decisions.
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