TY - JOUR AU - Park, Seunghyun AU - Choi, Jin-Young PY - 2020 DA - 2020/01/17 TI - Malware Detection in Self-Driving Vehicles Using Machine Learning Algorithms SP - 3035741 VL - 2020 AB - The recent trend for vehicles to be connected to unspecified devices, vehicles, and infrastructure increases the potential for external threats to vehicle cybersecurity. Thus, intrusion detection is a key network security function in vehicles with open connectivity, such as self-driving and connected cars. Specifically, when a vehicle is connected to an external device through a smartphone inside the vehicle or when a vehicle communicates with external infrastructure, security technology is required to protect the software network inside the vehicle. Existing technology with this function includes vehicle gateways and intrusion detection systems. However, it is difficult to block malicious code based on application behaviors. In this study, we propose a machine learning-based data analysis method to accurately detect abnormal behaviors due to malware in large-scale network traffic in real time. First, we define a detection architecture, which is required by the intrusion detection module to detect and block malware attempting to affect the vehicle via a smartphone. Then, we propose an efficient algorithm for detecting malicious behaviors in a network environment and conduct experiments to verify algorithm accuracy and cost through comparisons with other algorithms. SN - 0197-6729 UR - https://doi.org/10.1155/2020/3035741 DO - 10.1155/2020/3035741 JF - Journal of Advanced Transportation PB - Hindawi KW - ER -