• How can ML enhance traditional cybersecurity measures in AVs, and what are its limitations?
• What are the biggest challenges in deploying ML-based intrusion detection and prevention systems?
• How can we ensure that ML-driven security mechanisms remain robust against adversarial attacks?
• What role should regulators and industry standards play in shaping AI-driven AV cybersecurity?
• How can automakers and technology providers collaborate to build a more resilient and adaptive security ecosystem?

As AVs become more connected and reliant on complex software systems, cybersecurity threats pose a significant challenge to their safe deployment. Traditional security measures struggle to keep pace with the evolving attack landscape, necessitating a shift towards intelligent, adaptive defense mechanisms. This presentation explores how ML enhances AV security by detecting anomalies, predicting cyber threats, and enabling real-time threat mitigation. We will discuss ML-based intrusion detection systems, adversarial attack defenses, and the role of continuous learning in safeguarding AVs against emerging risks. Attendees will gain insights into the practical applications of ML in AV security, industry challenges, and the regulatory implications of AI-driven cybersecurity.

In this session, you will learn more about:

• How ML models identify and respond to evolving cyber threats in AV systems
• Strategies to enhance AV security by defending against adversarial ML exploits
• The role of AI-driven cybersecurity in meeting AV safety and security standards