Can social media shape the security of next-generation connected vehicles?
It addresses the need for improved cyber threat assessment in the automotive industry, but the results are qualitative and lack concrete performance numbers.
The paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework that uses machine learning to extract threat insights from social media for automotive cybersecurity, demonstrating its potential through four use cases.
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.