Can We Develop an AI-Powered Automotive IDS That Meets AUTOSAR Standards?
Description
With the rise of connected and autonomous vehicles, cybersecurity threats targeting automotive systems have become a critical concern. Intrusion Detection Systems (IDS) play a crucial role in detecting and mitigating cyberattacks in modern vehicles. However, integrating AI-driven IDS within the automotive industry requires compliance with established frameworks like AUTOSAR, which standardizes software architectures to ensure interoperability, safety, and reliability. This paper explores the feasibility of developing an AI-powered automotive IDS that aligns with AUTOSAR standards. It examines the challenges of integrating machine learning-based anomaly detection within AUTOSAR-compliant Electronic Control Units (ECUs), addressing real-time constraints, resource limitations, and security requirements. Additionally, we discuss potential methodologies for implementing AI-based IDS while maintaining adherence to AUTOSAR guidelines. By evaluating current advancements and practical implementation strategies, this study aims to determine whether AI and AUTOSAR can effectively coexist to enhance automotive cybersecurity without compromising system integrity and performance.
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