The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there remains a critical need for comprehensive assessment and analysis of cyber risks. In this context, this paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed for the emerging field of automotive cybersecurity. The framework leverages advanced intelligence techniques and machine learning models to extract valuable insights from social media. Four use cases illustrate The framework’s potential by demonstrating how it can significantly enhance threat assessment procedures within the automotive industry.
Can social media shape the security of next-generation connected vehicles? / Scarano, Nicola; Mannella, Luca; Savino, Alessandro; DI CARLO, Stefano. - (2024), pp. 1-4. (Intervento presentato al convegno 2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS) tenutosi a Rennes (FRA) nel 3-5 July 2024) [10.1109/IOLTS60994.2024.10616053].
Can social media shape the security of next-generation connected vehicles?
Scarano Nicola;Mannella Luca;Savino Alessandro;Di Carlo Stefano
2024
Abstract
The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there remains a critical need for comprehensive assessment and analysis of cyber risks. In this context, this paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed for the emerging field of automotive cybersecurity. The framework leverages advanced intelligence techniques and machine learning models to extract valuable insights from social media. Four use cases illustrate The framework’s potential by demonstrating how it can significantly enhance threat assessment procedures within the automotive industry.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2993131