Enhancing Autonomous System Security: A Formal Framework for Assessing and Strengthening Autonomous Vehicle Defenses

In recent years, there has been growing concern among experts regarding the risks of hacking autonomous
vehicles. As these vehicles become increasingly complex, the number of potential vulnerabilities and
challenges associated with securing them also rises. This paper presents a model checking-based
framework that utilizes a predefined set of attacks and countermeasures, which are then used to assess the
security robustness of the model. First, we formalize a cyber-physical system using Unified Modeling
Language (UML) class and activity diagrams. Subsequently, we employ UML to develop a meta-language
for autonomous vehicle systems, cyberattacks, and cybersecurity countermeasures. The framework
instantiates domain-specific application diagrams for autonomous vehicles, identifies existing attack
surfaces, and generates potential attacks that could exploit detected vulnerabilities or weaknesses.
Furthermore, the proposed framework generates appropriate Java code for integrating countermeasures,
attacks, and smart vehicle models. To demonstrate the effectiveness of the proposed solution, we model,
analyze, harden, and evaluate our framework using a real-world use case. This research aims to contribute
to the ongoing efforts to improve the security of autonomous vehicles and mitigate the risks associated
with hacking and other cyber threats. By applying the framework presented in this paper, the goal is to
promote a more secure development and implementation of autonomous vehicle systems