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Digital Twin-Based Deception Solution for Autonomous Vehicles Security
(Tartu Ülikool, 2025) Khan, Md Al Amin; Muhammad, Naveed, juhendaja; Iqbal, Mubashar, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The autonomous vehicle industry is rapidly advancing toward fully autonomous vehicles, raising significant concerns about cybersecurity and physical safety. These vehicles depend heavily on interconnected digital components, which enhance convenience, but at the same time pose risks. Those interconnected components expose critical systems to malicious actors. Recent incidents have demonstrated that adversaries are employing sophisticated techniques to exploit these systems, posing a greater risk to human life, data security, and public safety. This reminds us that the security of autonomous vehicles needs to be addressed. This study addresses the problem of securing autonomous vehicles against cyber threats by employing a digital twin-based deception solution. Here we show that our proposed digital twin deception system can effectively mitigate unauthorized access attempts targeting autonomous vehicles, redirecting them to an isolated honeypot and collecting valuable intelligence while keeping the attacker busy on the honeypot. At the same time, the data is visualised on the Kibana dashboard. This method not only identifies intrusions but also generates actionable insights through integrated monitoring and incident response features. The proposed system can also be extended beyond autonomous vehicles; it can also be applied to other cyber-physical models where a digital twin offers significant security and operational advantages. These findings contribute to the larger effort of protecting next-generation transportation systems through intelligent, resilient, and adaptive security mechanisms.