Agent-based modeling (ABM) serves as a powerful and innovative tool to evaluate and address cybersecurity risks in the auto industry. With the increasing integration of advanced technologies in modern vehicles, the potential vulnerabilities and threats to cybersecurity have become a major concern. ABM allows cybersecurity experts to simulate complex interactions among autonomous agents representing various components of the automotive ecosystem, such as vehicles, manufacturers, suppliers, and hackers. ABM can effectively model real-world scenarios and assess how cyber threats propagate and spread throughout the system. It enables the identification of critical points of weakness and potential attack vectors, aiding in the development of proactive strategies to mitigate cybersecurity risks. Through iterative simulations and data-driven analysis, ABM empowers stakeholders in the auto industry to make informed decisions and implement robust security measures that safeguard against cyber-attacks, ensuring the safety and trustworthiness of connected and autonomous vehicles.
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Cybersecurity is an ever-growing challenge for industries worldwide, and the automotive industry is no exception (Bocetta, 2022). With the increasing use of electronic systems in cars, the possibility of cyberattacks has also increased (Burkacky et al., 2021). Modern cars have numerous sensors, processors, and communication interfaces designed to work together seamlessly (Security Compass, 2019; Ritchell, 2021). Furthermore, the rise of connected cars and autonomous vehicles has increased the attack surface for hackers (Bocetta, 2022; Hamadneh & Esztergar-Kiss, 2022; Richell, 2021). These vehicles rely on wireless communication and are connected to the internet, which makes them vulnerable to cyber-attacks from anywhere in the world (Ritchell, 2021; Burkacky et al., 2021; Hamadneh & Esztergar-Kiss, 2022). However, this complexity also makes identifying vulnerabilities and protecting against cyber-attacks challenging (Lonzetta et al., 2018; Bocetta, 2022; Burkacky et al., 2021; Richell, 2021). Agent-based modeling (ABM) is a technique that can be used to simulate complex systems such as cybersecurity in the automotive industry (Bouard et al., 2013). Cybersecurity has become a critical concern in the modern world as technology advances rapidly (Security Compass, 2019). The rise of connected and autonomous vehicles has created new opportunities for hackers to exploit vulnerabilities and gain unauthorized access to systems (Lonzetta et al., 2018; Hamadneh & Esztergar-Kiss, 2022; Wan & Wang, 2022).
Agent-based modeling (ABM) has emerged as a valuable computational tool for addressing complex data and cybersecurity challenges faced by organizations collaborating with a variety of production vendors. In today's interconnected and data-driven business landscape, managing data and ensuring cybersecurity across multiple vendors can be highly intricate and critical. ABM provides a novel approach to simulate and analyze the interactions, dependencies, and vulnerabilities within the multi-vendor system, allowing stakeholders to gain deeper insights and develop effective strategies to safeguard data and bolster cybersecurity.
In a data-driven world where organizations engage with multiple production vendors, data management and cybersecurity have become pressing concerns. Ensuring data integrity, privacy, and security across a diverse vendor network is challenging, requiring advanced methodologies to analyze the complex interactions between vendors and the organization. ABM offers a powerful tool to simulate the behavior of individual agents, identify potential data vulnerabilities, and assess cybersecurity risks in a multi-vendor environment.