社交媒体能否塑造下一代联网车辆的安全性？
Can social media shape the security of next-generation connected vehicles?
AI总结：
本文提出了一种社交媒体汽车威胁情报（SOCMATI）框架，用于评估汽车网络安全中的潜在威胁，通过利用先进的情报技术和机器学习模型从社交媒体中提取有价值的信息，以提高威胁评估流程。
摘要：
随着车辆中连接性和电子组件的日益普及，这些系统成为攻击者的重要目标。尽管汽车厂商优先考虑安全性，但仍需要对网络安全风险进行全面评估和分析。在此背景下，本文提出了一种社交媒体汽车威胁情报（SOCMATI）框架，专门针对新兴的汽车网络安全领域。该框架利用先进的情报技术和机器学习模型，从社交媒体中提取有价值的信息。四个用例展示了该框架的潜力，通过展示其如何显著增强汽车行业中威胁评估流程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
