Aiming at the data tampering network attack on the fleet communication network, an attack detection method using the setter-Kalman filtering algorithm and a protection mechanism of the switching control algorithm are proposed. The method first analyzes the impact of data tampering attacks on fleet control system, and then designs an attack detection method based on the Collector-Kalman filtering algorithm under the assumption of mixed noise for the data tampering attacks, and also designs a protection mechanism for the switching control algorithm in consideration of the case that the system has been under attacks for a long period of time. Finally, through simulation experiments, it is verified that the proposed method can effectively detect the attacks on the system, and can effectively resist the adverse effects of data tampering attacks on the system, thus guaranteeing the safety of the fleet system.
AlamT (2024) Data privacy and security in autonomous connected vehicles in smart city environment. Big Data and Cognitive Computing8(9): 95.
2.
AwajiTTashimaRKuboR (2022) Detection and mitigation of data tampering attacks for cooperative acc systems based on C-V2X. In: 2022 IEEE International Conference on Consumer Electronics, Taiwan, Taipei, Taiwan, 6 July 2022, pp. 149–150. Available at: https://ieeexplore.ieee.org/document/9869113/ (accessed 6 July 2024).
3.
BachEGhilM (2023) A multi-model ensemble Kalman filter for data assimilation and forecasting. Journal of Advances in Modeling Earth Systems15(1): e2022MS003123.
4.
CastiglioneAPalmieriFColaceF, et al. (2020) Securing the internet of vehicles through lightweight block ciphers. Pattern Recognition Letters135: 264–270.
5.
ChangYHHuQTomlinCJ (2018) Secure estimation based Kalman Filter for cyber–physical systems against sensor attacks. Automatica95: 399–412.
6.
CsendesT (Ed) (1999) Developments in Reliable Computing. Cham: Springer Science & Business Media.
7.
DuttaRGHuYYuF, et al. (2022) Design and analysis of secure distributed estimator for vehicular platooning in adversarial environment. IEEE Transactions on Intelligent Transportation Systems23(4): 3418–3429.
8.
El-RewiniZSadatsharanKSelvarajDF, et al. (2020) Cybersecurity challenges in vehicular communications. Vehicular Communications23: 100214.
9.
El-RewiniZSadatsharanKSugunarajN, et al. (2020) Cybersecurity attacks in vehicular sensors. IEEE Sensors Journal20(22): 13752–13767.
10.
FarivarFHaghighiMSJolfaeiA, et al. (2020) On the security of networked control systems in smart vehicle and its adaptive cruise control. arXiv:2008.00414. arXiv. Available at: http://arxiv.org/abs/2008.00414 (accessed 10 March 2025).
11.
GengKLiuS (2020) Robust path tracking control for autonomous vehicle based on a novel fault tolerant adaptive model predictive control algorithm. Applied Sciences10(18): 6249.
12.
GuoCWangXSuL, et al. (2021) Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering235(2–3): 498–512.
13.
HabibovicAChenL (2021) Connected automated vehicles: Technologies, developments, and trends. In: VickermanR (ed.) International Encyclopedia of Transportation. Oxford: Elsevier, pp. 180–188. Available at: https://www.sciencedirect.com/science/article/pii/B9780081026717101101 (accessed 10 March 2025).
14.
LuoYLiaoYNiS, et al. (2021) Attack detection based on Kalman filter. In: 2021 (ICSIP): IEEE 6th International Conference on Signal and Image Processing October 2021, pp. 1228–1232. Available at: https://ieeexplore.ieee.org/document/9688628 (accessed 10 March 2025).
15.
MakridisMMattasKCiuffoB, et al. (n.d.) Response time and time headway of an adaptive cruise control. An empirical characterization and potential impacts on road capacity.
16.
ModeGRCalyamPHoqueKA (2019) False data injection attacks in internet of things and deep learning enabled predictive analytics. arXiv:1910.01716. arXiv. Available at: http://arxiv.org/abs/1910.01716 (accessed 10 March 2025).
17.
MousavinejadEGeXHanQ-L, et al. (2021) Resilient tracking control of networked control systems under cyber attacks. IEEE Transactions on Cybernetics51(4): 2107–2119.
18.
MousavinejadEYangFHanQ-L, et al. (2020) Distributed cyber attacks detection and recovery mechanism for vehicle platooning. IEEE Transactions on Intelligent Transportation Systems21(9): 3821–3834.
19.
ÖncüSPloegJvan de WouwN, et al. (2014) Cooperative adaptive cruise control: Network-aware analysis of string stability. IEEE Transactions on Intelligent Transportation Systems15(4): 1527–1537.
20.
RenGLiuHZhangY, et al. (2023) Attack detection based on encoding–decoding approach for cyber–physical systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems53(10): 6293–6302.
21.
SunLAlkhatibHKargollB, et al. (2019) Ellipsoidal and Gaussian Kalman filter model for discrete-time nonlinear systems. Mathematics7(12): 1168.
22.
TaylorSJAhmadFNguyenHN, et al. (2021) Vehicular platoon communication: Cybersecurity threats and open challenges. In: 2021(DSN-W): 51st Annual IEEE IFIP International Conference on Dependable Systems and Networks Workshops, June 2021, pp. 19–26. Available at: https://ieeexplore.ieee.org/document/9502434 (accessed 10 March 2025).
23.
van WykFWangYKhojandiA, et al. (2020) Real-time sensor anomaly detection and identification in automated vehicles. IEEE Transactions on Intelligent Transportation Systems21(3): 1264–1276.
24.
VangalaABeraBSahaS, et al. (2021) Blockchain-enabled certificate-based authentication for vehicle accident detection and notification in intelligent transportation systems. IEEE Sensors Journal21(14): 15824–15838.
25.
WenSGuoGChenB, et al. (2019) Cooperative adaptive cruise control of vehicles using a resource-efficient communication mechanism. IEEE Transactions on Intelligent Vehicles4(1): 127–140.
26.
YangTMurguiaCLvC (2023) Risk assessment for connected vehicles under stealthy attacks on vehicle-to-vehicle networks. IEEE Transactions on Intelligent Transportation Systems24(12): 13627–13638.
27.
ZhangHLiZChengS, et al. (2023) Cooperative perception for safe control of autonomous vehicles under LiDAR spoofing attacks. arXiv:2302.07341. arXiv. Available at: http://arxiv.org/abs/2302.07341 (accessed 10 March 2025).
28.
ZhouB (2009) A UD factorization-based adaptive extended set-membership filter: A UD factorization-based adaptive extended set-membership filter. Acta Automatica Sinica34(2): 150–158.
29.
ZihanZLirongCHaitaoZ, et al. (2021) Research on intrusion detection technology based on embedded ethernet. In: 2021 (ICCWAMTIP): 18th International Computer Conference on Wavelet Active Media Technology and Information Processing, December 2021, pp. 587–600. Available at: https://ieeexplore.ieee.org/document/9674069 (accessed 10 March 2025).
