Can social media shape the security of next-generation connected vehicles?

TL;DR

This paper introduces SOCMATI, a framework that uses social media data and machine learning to improve cybersecurity threat assessment for connected vehicles.

Contribution

It presents a novel framework leveraging social media and AI for automotive cyber risk analysis, filling a gap in existing security assessment methods.

Findings

SOCMATI enhances threat detection capabilities in automotive cybersecurity.

Use cases demonstrate significant improvements in threat assessment accuracy.

Framework shows potential for proactive security measures in connected vehicles.

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.