安全增强的车载嵌入式系统中任务映射和调度算法

Task Mapping and Scheduling Algorithms for Security-enhanced Automotive Embedded Systems

摘要

随着现代车载嵌入式系统应用的不断集成与电子控制单元(ECU)数量的增长,系统实时性保障面临更大挑战.同时,汽车与外部环境交互频繁,具有灵活数据速率的控制器局域网(CAN FD)虽提升了传输性能,但仍缺乏内置安全机制,易受伪装等攻击威胁.而添加安全机制往往会占用实时性资源,危害车辆安全,因此必须在保证系统实时性的同时提升安全性.为此,本文提出了一种基于强化学习的任务映射和调度算法(Reinforcement Learning-based Task Mapping and Scheduling Algorithm,RLMS),将任务映射建模为马尔科夫决策过程,结合资源感知机制以ECU利用率为约束优化方案,在满足实时性约束的前提下减少CAN FD总线消息数量,并为消息提供4字节MAC的基础安全保护.为进一步提升系统安全性,设计安全增强机制(Security Enhancement with Balanced Rounds,SEBR),利用系统空闲时间逐轮扩展消息的MAC字节.最终,通过真实案例和模拟实验验证了所提方法的有效性.

Abstract

As modern in-vehicle embedded systems continue to integrate more applications and the number of Electronic Control Units(ECUs)increases,ensuring system real-time performance becomes increasingly challenging.Meanwhile,frequent interactions between vehicles and the external environment raise security concerns.Although the Controller Area Network with Flexible Data-Rate(CAN FD)improves transmission performance through flexible data rates,it lacks built-in security mechanisms and remains vulnerable to threats such as spoofing.Introducing security mechanisms often consumes real-time resources and may compromise vehicle safety.Therefore,enhancing security while ensuring real-time guarantees is essential.This paper proposes a Reinforcement Learning-based Task Mapping and Scheduling Algorithm(RLMS),which formulates the task mapping process as a Markov Decision Process.By incorporating a resource-aware mechanism that constrains ECU utilization,the proposed method reduces the number of CAN FD bus messages while satisfying real-time constraints.Each message is provided with basic security protection through a 4-byte Message Authentication Code(MAC).To further strengthen system security,a mechanism called Security Enhancement with Balanced Rounds(SEBR)is introduced,which gradually increases the MAC length by leveraging system idle time in a round-by-round manner.The effectiveness of the proposed approach is validated through real-world case studies and simulation experiments.

关键词

嵌入式系统 / CAN FD / 任务调度 / 安全性增强

Key words

embedded systems / CAN FD / task scheduling / security enhancement

[1] Zou A,Li J,Gill C D,et al.RTGPU:real-time GPU scheduling of hard deadline parallel tasks with fine-grain utilization [J].IEEE Transactions on Parallel and Distributed Systems,2023,34(5):1450-1465.
[2] Sonko S,Daudu C D,Osasona F,et al.The evolution of embedded systems in automotive industry:a global review[J].World Journal of Advanced Research and Reviews,2024,21(2):96-104.
[3] Sun H,Huang W,Weng J,et al.CCID-CAN:cross-chain intrusion detection on CAN bus for autonomous vehicles[J].IEEE Internet of Things Journal,2024,11(15):26146-26159.
[4] de Andrade R,Santos M M D,Justo J F,et al.Security architecture for automotive communication networks with CAN FD[J].Computers & Security,2023,129:103203,doi:10.1016/j.cose.2023.103203.
[5] Lodge N,Tambe N,Saqib F.Addressing vulnerabilities in CAN-FD:an exploration and security enhancement approach[J].Internet of Things,2024,5(2):290-310.
[6] Shang C,Cao J,Liu J,et al.CEAMP:a cross-domain entity authentication and message protection framework for intra-vehicle network[J].IEEE Transactions on Intelligent Transportation Systems,2023,25(7):6780-6795.
[7] Zhao R,Luo C,Gao F,et al.Application-layer anomaly detection leveraging time-series physical semantics in can-fd vehicle networks[J].Electronics,2024,13(2):377,doi:10.3390/electronics13020377.
[8] Xie Y,Zeng G,Kurachi R,et al.Security/timing-aware design space exploration of CAN FD for automotive cyber-physical systems[J].IEEE Transactions on Industrial Informatics,2019,15(2):1094-1104.
[9] Roy S K,Devaraj R,Sarkar A.Contention cognizant scheduling of task graphs on shared bus-based heterogeneous platforms[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2021,41(2):281-293.
[10] Minaeva A,Roy D,Akesson B,et al.Control performance optimization for application integration on automotive architectures[J].IEEE Transactions on Computers,2020,70(7):1059-1073.
[11] McLean S D,Juul Hansen E A,Pop P,et al.Configuring ADAS platforms for automotive applications using metaheuristics[J].Frontiers in Robotics and AI,2022,8:762227,doi:10.3389/frobt.2021.762227.
[12] Xie G,Yang L T,Liu Y,et al.Security enhancement for real-time independent in-vehicle CAN-FD messages in vehicular networks[J].IEEE Transactions on Vehicular Technology,2021,70(6):5244-5253.
[13] Ma W,Liu Y,Xie G,et al.Security-aware CAN-FD message packing in intelligent automotive cyber-physical systems[J].IEEE Internet of Things Journal,2021,9(22):22343-22356.
[14] Stumpf F,Pohl C,Hoettges D,et al.Introducing HSM-based secure on-board communication in vehicles[C]//Proceedings of Escar Europe,2019:104-107.
[15] Xie Y,Guo Y,Yang S,et al.Security-related hardware cost optimization for CAN FD-based automotive cyber-physical systems[J].Sensors,2021,21(20):6807,doi:10.3390/s21206807.
[16] Reusch N,Craciunas S S,Pop P.Dependability-aware routing and scheduling for time-sensitive networking[J].IET Cyber-Physical Systems:Theory & Applications,2022,7(3):124-146.
[17] Dabbaghjamanesh M,Moeini A,Kavousi Fard A.Reinforcement learning-based load forecasting of electric vehicle charging station using Q-learning technique[J].IEEE Transactions on Industrial Informatics,2021,17(6):4229-4237.
[18] Xie G,Yang L T,Wu W,et al.Security enhancement for real-time parallel in-vehicle applications by CAN FD message authentication[J].IEEE Transactions on Intelligent Transportation Systems,2020,22(8):5038-5049.
[19] LIU Y C.Research on multi-sensor data fusion optimization algorithm in the internet of things environment[J].Technology of IoT&AI,2025, 57(2): 33-36.
[20] Topcuoglu H,Hariri S,Wu M Y.Performance-effective and low-complexity task scheduling for heterogeneous computing[J].IEEE Transactions on Parallel and Distributed Systems,2002,13(3):260-274.
[21] LI Y H,LIU P P,WANG W M,et al.Deep reinforcement learning task offloading method in edge end collaboration scenario[J].Journal of Chinese Computer Systems,2025,46(2):280-288.
[22] Dearden R,Friedman N,Russell S.Bayesian Q-learning[J].Association for the Advancement of Artificial Intelligence,1998:761-768,doi:10.1007/BF00993412.
[23] Buttazzo G C.Hard real-time computing systems:predictable scheduling algorithms and applications(3rd ed.)[M].Berlin:Springer,2011.
[24] Chauhan N K,Tyagi I,Kumar H,et al.Tasks scheduling through hybrid genetic algorithm in real-time system on heterogeneous environment[J].SN Computer Science,2022,3(1):75,doi:10.1007/s42979-021-00959-0.
[25] Xie G,Zeng G,Kurachi R,et al.Exact WCRT analysis for message-processing tasks on gateway-integrated in-vehicle CAN clusters[J].ACM Transactions on Embedded Computing Systems,2018,17(6):1-29.

附中文参考文献:
[19] 刘裕灿.物联网环境下多传感器数据融合优化算法研究[J].智能物联技术,2025,57(2):33-36.
[21] 李英豪,刘盼盼,王文猛,等.边端协同场景下的深度强化学习任务卸载方法[J].小型微型计算机系统,2025,46(2):280-288.

基金资助

国家自然科学基金项目(62072175)资助.