Page 472 - 《软件学报》2025年第4期

1878 软件学报 2025 年第 36 卷第 4 期

Cyber-physical Systems (SEsCPS). Montreal: IEEE, 2019. 39–45. [doi: 10.1109/SEsCPS.2019.00014]
[67] Sargolzaei A, Crane CD, Abbaspour A, Noei S. A machine learning approach for fault detection in vehicular cyber-physical systems. In:
Proc. of the 15th IEEE Int’l Conf. on Machine Learning and Applications (ICMLA). Anaheim: IEEE, 2016. 636–640. [doi: 10.1109/
ICMLA.2016.0112]
[68] Othmane LB, Weffers H, Mohamad MM, Wolf M. A survey of security and privacy in connected vehicles. In: Benhaddou D, Al-Fuqaha
A, eds. Wireless Sensor and Mobile Ad-hoc Networks: Vehicular and Space Applications. New York: Springer, 2015. 217–247. [doi: 10.
1007/978-1-4939-2468-4_10]
[69] Ali I, Lawrence T, Li FG. An efficient identity-based signature scheme without bilinear pairing for vehicle-to-vehicle communication in
VANETs. Journal of Systems Architecture, 2020, 103: 101692. [doi: 10.1016/j.sysarc.2019.101692]
[70] Ali Alheeti KM, Gruebler A, McDonald-Maier K. Intelligent intrusion detection of grey hole and rushing attacks in self-driving
vehicular networks. Computers, 2016, 5(3): 16. [doi: 10.3390/computers5030016]
[71] Lu XZ, Xiao L, Xu TW, Zhao YF, Tang YL, Zhuang WH. Reinforcement learning based PHY authentication for VANETs. IEEE Trans.
on Vehicular Technology, 2020, 69(3): 3068–3079. [doi: 10.1109/TVT.2020.2967026]
[72] Gomides TS, Kranakis E, Lambadaris I, Viniotis Y. Optimal control for platooning in vehicular networks. In: Proc. of the 2023 IEEE Int’l
Conf. on Communications. Rome: IEEE, 2023. 6597–6602. [doi: 10.1109/ICC45041.2023.10279610]
[73] Xu H, Ji JQ, Zhu K, Wang R. Deep reinforcement learning for resource allocation in multi-platoon vehicular networks. In: Proc. of the
16th Int’l Conf. on Wireless Algorithms, Systems, and Applications. Nanjing: Springer, 2021. 402–416. [doi: 10.1007/978-3-030-86130-
8_32]
[74] Chang S, Qi Y, Zhu HZ, Zhao JZ, Shen XM. Footprint: Detecting Sybil attacks in urban vehicular networks. IEEE Trans. on Parallel
and Distributed Systems, 2012, 23(6): 1103–1114. [doi: 10.1109/TPDS.2011.263]
[75] Lu RX, Lin XD, Liang XH, Shen XM. A dynamic privacy-preserving key management scheme for location-based services in VANETs.
IEEE Trans. on Intelligent Transportation Systems, 2012, 13(1): 127–139. [doi: 10.1109/TITS.2011.2164068]
[76] Junaidi DR, Ma MD, Su R. Secure vehicular platoon management against Sybil attacks. Sensors, 2022, 22(22): 9000. [doi: 10.3390/
s22229000]
[77] Gu PWL, Khatoun R, Begriche Y, Serhrouchni A. Support vector machine (SVM) based Sybil attack detection in vehicular networks.
In: Proc. of the 2017 IEEE Wireless Communications and Networking Conf. (WCNC). San Francisco: IEEE, 2017. 1–6. [doi: 10.1109/
WCNC.2017.7925783]
[78] Gong J, Murguia C, Bayuwindra A, Cao JD. Resilient controller synthesis against DoS attacks for vehicular platooning in spatial
domain. arXiv:2307.15874, 2023.
[79] Ravindran R, Santora MJ, Jamali MM. Multi-object detection and tracking, based on DNN, for autonomous vehicles: A review. IEEE
Sensors Journal, 2021, 21(5): 5668–5677. [doi: 10.1109/JSEN.2020.3041615]
[80] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM,
2017, 60(6): 84–90. [doi: 10.1145/3065386]
[81] Bijjahalli S, Sabatini R, Gardi A. Advances in intelligent and autonomous navigation systems for small UAS. Progress in Aerospace
Sciences, 2020, 115: 100617. [doi: 10.1016/j.paerosci.2020.100617]
[82] Waymo LLC. Waymo safety report: On the road to fully self-driving. 2017. https://storage.googleapis.com/sdc-prod/v1/safety-report/
waymo-safety-report-2017-10.pdf
[83] Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang JK, Zhang X, Zhao J,
Zieba K. End to end learning for self-driving cars. arXiv:1604.07316, 2016.
[84] Muñoz-González L, Biggio B, Demontis A, Paudice A, Wongrassamee V, Lupu EC, Roli F. Towards poisoning of deep learning
algorithms with back-gradient optimization. In: Proc. of the 10th ACM Workshop on Artificial Intelligence and Security. Dallas: ACM,
2017. 27–38. [doi: 10.1145/3128572.3140451]
[85] Suciu O, Mărginean R, Kaya Y, Daumé H III, Dumitraş T. When does machine learning FAIL? Generalized transferability for evasion
and poisoning attacks. In: Proc. of the 27th USENIX Conf. on Security Symp. Baltimore: USENIX Association, 2018. 1299–1316.
[86] Shafahi A, Huang WR, Najibi M, Suciu O, Studer C, Dumitras T, Goldstein T. Poison frogs! Targeted clean-label poisoning attacks on
neural networks. In: Proc. of the 32nd Int’l Conf. on Neural Information Processing Systems. Montréal: Curran Associates Inc., 2018.
6106–6116.
[87] Zhu C, Huang WR, Li HD, Taylor G, Studer C, Goldstein T. Transferable clean-label poisoning attacks on deep neural nets. In: Proc. of
the 36th Int’l Conf. on Machine Learning. Long Beach: PMLR, 2019. 7614–7623.
[88] Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R. Intriguing properties of neural networks.