Network Intrusion Detection for Intelligent Driving
DOI:
https://doi.org/10.54097/3k6snv62Keywords:
Intelligent Driving, Network Intrusion Detection, Machine Learning, Deep LearningAbstract
The rapid advancement of intelligent driving technology has increased communication between vehicles, external networks, cloud platforms, and other vehicles, significantly elevating cybersecurity risks. Since intelligent driving systems rely heavily on in-vehicle networks and wireless communications, they are vulnerable to various network attacks. Traditional security measures often fail to meet the stringent real-time and accuracy requirements of such systems, making it urgent to develop efficient and intelligent intrusion detection systems to ensure safe and stable operation. This paper begins by analyzing the network architecture and security requirements of intelligent driving systems, and reviews mainstream network intrusion detection technologies—including feature matching, anomaly detection, and methods based on machine learning and deep learning. By comparing the strengths and weaknesses of different approaches, it highlights the challenges related to real-time performance and accuracy that conventional detection techniques face in intelligent driving environments. The study also discusses future trends in intelligent driving cybersecurity, emphasizing the importance of integrating AI technology with cybersecurity to enhance intrusion detection capabilities. Finally, the paper proposes research directions such as multi-level defense systems and cross-domain collaborative defense, offering both theoretical and practical support for securing intelligent driving systems.
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