"AI-Driven Cybersecurity Risks and Defense in Avionics Systems"

Abstract - Advancements in the digitization and automation of critical infrastructures, particularly in aeronautical Communication, Navigation, and Surveillance (CNS) technologies, have led to the merging of complex physical and information networks with artificial intelligence (AI) systems. This integration has significantly enhanced the capabilities of avionics and Air Traffic Management (ATM) systems, improving data processing, information sharing, and geographic coverage. However, these advancements also make these systems more vulnerable to cybersecurity threats, physical weaknesses, and risks to data integrity. The interconnected nature of CNS infrastructure, together with ATM systems, increases the likelihood of widespread attacks, as threats can rapidly spread across connected components. Consequently, both ATM and UAS Traffic Management (UTM) systems are facing growing security challenges. Although the concept of cybersecurity in aviation is not new, integrating it seamlessly into aviation systems continues to pose significant difficulties. As AI technology improves operational efficiency and reliability within aviation systems, it has also become a key area for emerging cybersecurity threats. AI-driven intrusions and thefts are progressively replacing traditional attack methods, prompting researchers to propose defensive strategies based on AI technology. This paper critically examines the cybersecurity vulnerabilities and threats that could impact ATM and UTM systems. It categorizes potential threat actors based on their goals, motivations, and capabilities, and explores various attack methodologies based on AI technology, alongside their defensive countermeasures. Key Words: Cybersecurity, Avionics, Autonomous Systems, Cyber Threats, Air Traffic Management (ATM), UAS Traffic Management (UTM), CNS+A