

浏览全部资源
扫码关注微信
1.上海交通大学计算机学院,上海 200240
2.上海市信息安全综合管理技术研究重点实验室,上海 200240
Received:10 January 2025,
Revised:2025-05-28,
Published:25 October 2025
移动端阅览
孙康康, 李建华, 陈秀真, 等. 车-路-云协同自动驾驶安全技术研究进展[J]. 网络与信息安全学报, 2025, 11(5): 1-30.
SUN K K, LI J H, CHEN X Z, et al. Research progress on autonomous driving security technology for vehicle-road-cloud collaboration[J]. Chinese Journal of Network and Information Security, 2025, 11(5): 1-30.
孙康康, 李建华, 陈秀真, 等. 车-路-云协同自动驾驶安全技术研究进展[J]. 网络与信息安全学报, 2025, 11(5): 1-30. DOI: 10.11959/j.issn.2096-109x.2025058.
SUN K K, LI J H, CHEN X Z, et al. Research progress on autonomous driving security technology for vehicle-road-cloud collaboration[J]. Chinese Journal of Network and Information Security, 2025, 11(5): 1-30. DOI: 10.11959/j.issn.2096-109x.2025058.
随着边缘智能技术的进步和城市化进程的加快,智能交通系统(intelligent transportation system
ITS)得到快速发展。车-路-云协同实现车联网中车-车、车-路、车-云数据协同共享,构建了更加高效的协作智能交通系统(cooperative-intelligent transportation system
C-ITS)。然而,车-路-云协同存在大量的安全威胁,严重阻碍了协同自动驾驶的发展。首先总结了车-路-云协同的发展现状,详细介绍了自动驾驶发展历史及车-路-云协同自动驾驶环境。然后系统性地将车-路-云协同中遇到的攻击及安全防御技术归纳成两类:针对经典信息安全机制的威胁与防御技术,主要从基于信息可用性、基于信息完整性、基于信息保密性、基于信息真实性、基于信息不可否认性5部分进行详细介绍;针对基于机器学习技术的威胁与防御技术,主要从集中式与分布式两部分进行分析。最后展望了车-路-云协同安全技术的发展方向和研究重点,主要从联邦学习、区块链技术、安全多方计算、零知识证明、差分隐私技术展开介绍。
With the advancement of edge intelligence technology and the acceleration of urbanization
intelligent transportation systems (ITS) have experienced rapid development. Vehicle-road-cloud (VRC) collaboration was enabled through the coordinated sharing of vehicle-to-vehicle (V2V)
vehicle-to-road (V2R)
and vehicle-to-cloud (V2C) data in the Internet of vehicles
thereby constructing a more efficient cooperative intelligent transportation system (C-ITS). However
numerous security threats in VRC collaboration were found to severely impede the development of cooperative autonomous driving. The development status of VRC collaboration was first summarized
and the history of autonomous driving and the VRC-based autonomous driving environment were elaborated. Subsequently
attacks and security defense technologies in VRC collaboration were systematically categorized into two types: classical information security mechanisms and defense technologies
which were detailed from five aspects—information availability
integrity
confidentiality
authenticity
and non-repudiation; and machine learning-based security threats and defense technologies
which were analyzed from both centralized and distributed perspectives. Finally
future development directions and research priorities of VRC collaborative security technologies were forecasted
primarily covering federated learning
blockchain technology
secure multi-party computation
zero-knowledge proof
and differential privacy technology.
2023年交通运输行业发展统计公报 [EB ] . 2024 .
Annual statistical bulletin of transportation development in China, 2023 [EB ] . 2024 .
国务院关于印发《 中国制造 2025》 的通知 [EB ] . 2015 .
Notice of the State Council on issuing the "Made in China 2025 ” [EB]. 2015 .
关于开展智能网联汽车准入和上路通行试点工作的通知 [EB ] . 2023 .
Notice on the pilot work for the access and road passage of intelligent connected vehicles [EB ] . 2023 .
关于开展智能网联汽车 “车-路-云一体化” 应用试点工作的通知 [EB ] . 2024 .
Notice on the pilot work for the integrated application of intelligent connected vehicles in the "vehicle-road-cloud integration" system [EB ] . 2024 .
WANG W N , YING L , ZHANG J S . A game-theoretic approach to quality control for collecting privacy-preserving data [C ] // Proceedings of the 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton) . Piscataway : IEEE Press , 2015 : 474 - 479 .
王良民 , 刘晓龙 , 李春晓 , 等 . 5G车联网展望 [J ] . 网络与信息安全学报 , 2016 , 2 ( 6 ): 1 - 12 .
WANG L M , LIU X L , LI C X , et al . Overview of Internet of vehicles for 5G [J ] . Chinese Journal of Network and Information Security , 2016 , 2 ( 6 ): 1 - 12 .
道路交通法 [EB ] . 2023 .
Road traffic act [EB ] . 2023 .
中国信息通信研究院 . 车联网白皮书(2023年) [R ] . 2023 .
China Academy of Information and Communications Technology . White paper on internet of vehicles (2023) [R ] . 2023 .
丁飞 , 张楠 , 李升波 , 等 . 智能网联车-路-云协同系统架构与关键技术研究综述 [J ] . 自动化学报 , 2022 , 48 ( 12 ): 2863 - 2885 .
DING F , ZHANG N , LI S B , et al . A survey of architecture and key technologies of intelligent connected vehicle-road-cloud cooperation system [J ] . Acta Automatica Sinica , 2022 , 48 ( 12 ): 2863 - 2885 .
左志强 , 刘正璇 , 王一晶 . 基于车-路-云一体化的混合交通系统优化控制综述 [J ] . 控制与决策 , 2023 , 38 ( 3 ): 577 - 594 .
ZUO Z Q , LIU Z X , WANG Y J . A survey of optimal control for mixed traffic system with vehicle-roadcloud integration [J ] . Control and Decision , 2023 , 38 ( 3 ): 577 - 594 .
董晋之 . 2024 智能互联网蓝皮书:我国辅助自动驾驶乘用车市场渗透率达 47.3% [R ] . 2024 .
DONG J Z . 2024 smart internet blue book: the market penetration rate of China's assisted driving passenger cars reaches 47.3% [R ] . 2024 .
中华人民共和国工业和信息化部 . 装备工业一司召开智能网联汽车产品准入及软件在线升级管理工作推进会 [EB ] . 2025 .
The Ministry of Industry and Information Technology of the People's Republic of China . Equipment Industry Department I held a promotion meeting on the access of intelligent and connected vehicle products and the management of software over-the-air upgrades [EB ] . 2025 .
DE LA FORTELLE A , QIAN X J , DIEMER S , et al . Network of automated vehicles: the AutoNet 2030 vision [C ] // Proceedings of the 21st ITS World Congress . Detroit, USA , 2014 .
SU Z , HUI Y L , LUAN T H . Distributed task allocation to enable collaborative autonomous driving with network softwarization [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 10 ): 2175 - 2189 .
LI B , YANG L , XIAO J Z , et al . Collaborative mapping and autonomous parking for multi-story parking garage [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 19 ( 5 ): 1629 - 1639 .
LI Y F , TANG C C , LI K Z , et al . Consensus-based cooperative control for multi-platoon under the connected vehicles environment [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 6 ): 2220 - 2229 .
DENG R Q , DI B Y , SONG L Y . Cooperative collision avoidance for overtaking maneuvers in cellular V2X-based autonomous driving [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 5 ): 4434 - 4446 .
CHEN Q , TANG S H , YANG Q , et al . Cooper: cooperative perception for connected autonomous vehicles based on 3D point clouds [C ] // Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) . Piscataway : IEEE Press , 2019 : 514 - 524 .
CHEN Y M , LU C , CHU W B . A cooperative driving strategy based on velocity prediction for connected vehicles with robust path-following control [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 5 ): 3822 - 3832 .
YU C , WANG X , XU X , et al . Distributed multiagent coordinated learning for autonomous driving in highways based on dynamic coordination graphs [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 2 ): 735 - 748 .
LIU Y C , TIAN J J , MA C Y , et al . Who2com: collaborative perception via learnable handshake communication [C ] // Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE Press , 2020 : 6876 - 6883 .
WANG T H , MANIVASAGAM S , LIANG M , et al . V2VNet: vehicle-to-vehicle communication for joint perception and prediction [M ] // Computer Vision – ECCV 2020 . Cham : Springer International Publishing , 2020 : 605 - 621 .
CHEN S H , ZHANG N X , SUN H F . Collaborative localization based on traffic landmarks for autonomous driving [C ] // Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS) . Piscataway : IEEE Press , 2020 : 1 - 5 .
CHEN X S , LENG S P , HE J H , et al . Deep-learning-based intelligent intervehicle distance control for 6G-enabled cooperative autonomous driving [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 20 ): 15180 - 15190 .
ZHANG Y , LI C L , LUAN T H , et al . Collaborative driving: learning-aided joint topology formulation and beamforming [J ] . IEEE Vehicular Technology Magazine , 2022 , 17 ( 2 ): 103 - 111 .
LI Y M , MA D K , AN Z Y , et al . V2X-sim: multi-agent collaborative perception dataset and benchmark for autonomous driving [J ] . IEEE Robotics and Automation Letters , 2022 , 7 ( 4 ): 10914 - 10921 .
LIN C M , TIAN D X , DUAN X T , et al . V2VFormer: vehicle-to-vehicle cooperative perception with spatial-channel transformer [J ] . IEEE Transactions on Intelligent Vehicles , 2024 , 9 ( 2 ): 3384 - 3395 .
KONG X J , ZHU B , SHEN G J , et al . Spatial-temporal-cost combination based taxi driving fraud detection for collaborative Internet of vehicles [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 5 ): 3426 - 3436 .
YANG S H , YIN H H F , YEUNG R W , et al . On scalable network communication for infrastructure-vehicle collaborative autonomous driving [J ] . IEEE Open Journal of Vehicular Technology , 2022 , 4 : 310 - 324 .
YI S , ZHANG H , LIU K . V2IViewer: towards efficient collaborative perception via point cloud data fusion and vehicle-to-infrastructure communications [J ] . IEEE Transactions on Network Science and Engineering , 2024 , 11 ( 6 ): 6219 - 6230 .
QIAO G H , LENG S P , ZHANG K , et al . Collaborative task offloading in vehicular edge multi-access networks [J ] . IEEE Communications Magazine , 2018 , 56 ( 8 ): 48 - 54 .
XIE R C , TANG Q Q , WANG Q N , et al . Collaborative vehicular edge computing networks: architecture design and research challenges [J ] . IEEE Access , 2019 , 7 : 178942 - 178952 .
CHEN Y M , LU C , CHU W B . A cooperative driving strategy based on velocity prediction for connected vehicles with robust path-following control [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 5 ): 3822 - 3832 .
HE Y K , WU B F , DONG Z , et al . Towards C-V2X enabled collaborative autonomous driving [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 12 ): 15450 - 15462 .
QIAN H , ZHAO L , HAWBANI A , et al . Collaborative overtaking strategy for enhancing overall effectiveness of mixed connected and connectionless vehicles [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 12 ): 13556 - 13572 .
HU S K , FANG Z R , FANG Z H , et al . AgentsCoMerge: large language model empowered collaborative decision making for ramp merging [J ] . IEEE Transactions on Mobile Computing , 2025 , ( 99 ): 1 - 15 .
LIU G J , HU Y , XU C X , et al . Towards collaborative autonomous driving: simulation platform and end-to-end system [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025 , ( 99 ): 1 - 18
LIU S S , LIU L K , TANG J , et al . Edge computing for autonomous driving: opportunities and challenges [J ] . Proceedings of the IEEE , 2019 , 107 ( 8 ): 1697 - 1716 .
MILLER C , VALASEK C . Adventures in automotive networks and control units [J ] . Def Con , 2013 , 21 ( 260-264 ): 15 - 31 .
CHO K T , SHIN K G . Fingerprinting electronic control units for vehicle intrusion detection [C ] // 25th USENIX Security Symposium . 2016 : 911 - 927 .
CHO K T , SHIN K G . Viden: attacker identification on in-vehicle networks [C ] // Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2017 : 1109 - 1123 .
CHOI W , JO H J , WOO S , et al . Identifying ECUs using inimitable characteristics of signals in controller area networks [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 6 ): 4757 - 4770 .
CHOI W , JOO K , JO H J , et al . VoltageIDS: low-level communication characteristics for automotive intrusion detection system [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 8 ): 2114 - 2129 .
GROZA B , MURVAY P S . Efficient intrusion detection with bloom filtering in controller area networks [J ] . IEEE Transactions on Information Forensics and Security , 2019 , 14 ( 4 ): 1037 - 1051 .
PIRAYESH H , SANGDEH P K , ZHANG S C , et al . JammingBird: jamming-resilient communications for vehicular ad hoc networks [C ] // Proceedings of the 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) . Piscataway : IEEE Press , 2021 : 1 - 9 .
OKYERE B , MUSAVIAN L , ÖZBEK B , et al . The resilience of massive MIMO PNC to jamming attacks in vehicular networks [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 7 ): 4110 - 4117 .
汪涵 , 陈立全 , 王忠民 , 等 . 基于深度学习的车联网无线密钥生成系统 [J ] . 网络与信息安全学报 , 2024 , 10 ( 1 ): 102 - 111 .
WANG H , CHEN L Q , WANG Z M , et al . Wireless key generation system for Internet of vehicles based on deep learning [J ] . Chinese Journal of Network and Information Security , 2024 , 10 ( 1 ): 102 - 111 .
XIAO L , LU X Z , XU D J , et al . UAV relay in VANETs against smart jamming with reinforcement learning [J ] . IEEE Transactions on Vehicular Technology , 2018 , 67 ( 5 ): 4087 - 4097 .
BENSLIMANE A , NGUYEN-MINH H . Jamming attack model and detection method for beacons under multichannel operation in vehicular networks [J ] . IEEE Transactions on Vehicular Technology , 2017 , 66 ( 7 ): 6475 - 6488 .
LYAMIN N , KLEYKO D , DELOOZ Q , et al . Real-time jamming DoS detection in safety-critical V2V C-ITS using data mining [J ] . IEEE Communications Letters , 2019 , 23 ( 3 ): 442 - 445 .
MOKDAD L , BEN-OTHMAN J , NGUYEN A T . DJAVAN: Detecting jamming attacks in vehicle ad hoc networks [J ] . Performance Evaluation , 2015 , 87 : 47 - 59 .
ALIPOUR-FANID A , DABAGHCHIAN M , ZENG K . Impact of jamming attacks on vehicular cooperative adaptive cruise control systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 11 ): 12679 - 12693 .
PUÑAL O , AKTAŞ I , SCHNELKE C J , et al . Machine learning-based jamming detection for IEEE 802.11: design and experimental evaluation [C ] // Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 . Piscataway : IEEE Press , 2014 : 1 - 10 .
PAWLAK J , LI Y C , PRICE J , et al . A machine learning approach for detecting and classifying jamming attacks against OFDM-based UAVs [C ] // Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning . New York : ACM , 2021 : 1 - 6 .
KARAGIANNIS D , ARGYRIOU A . Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning [J ] . Vehicular Communications , 2018 , 13 : 56 - 63 .
XU Z S , ZHANG R R , HUANG P , et al . An aggregation protocol resisting collusion attacks in the Internet of vehicles environment [C ] // Proceedings of the 2023 IEEE 10th International Conference on Cyber Security and Cloud Computing (CSCloud)/2023 IEEE 9th International Conference on Edge Computing and Scalable Cloud (EdgeCom) . Piscataway : IEEE Press , 2023 : 24 - 29 .
BUSI REDDY V , VENKATARAMAN S , NEGI A . Communication and data trust for wireless sensor networks using D-S theory [J ] . IEEE Sensors Journal , 2017 , 17 ( 12 ): 3921 - 3929 .
GAUTAM A K , KUMAR R . A robust trust model for wireless sensor networks [C ] // Proceedings of the 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) . Piscataway : IEEE Press , 2018 : 1 - 5 .
JAIN A , KUMAR S . FriendShare: a secure and reliable framework for file sharing on network [J ] . Journal of Network and Computer Applications , 2018 , 120 : 1 - 16 .
LI X H , LIU J , OBAIDAT M S , et al . An unlinkable authenticated key agreement with collusion resistant for VANETs [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 8 ): 7992 - 8006 .
KANDAH F , SINGH Y , ZHANG W Y , et al . Mitigating colluding injected attack using monitoring verification in mobile ad-hoc networks [J ] . Security and Communication Networks , 2013 , 6 ( 4 ): 539 - 547 .
MARTI S , GIULI T J , LAI K , et al . Mitigating routing misbehavior in mobile ad hoc networks [C ] // Proceedings of the 6th Annual International Conference on Mobile Computing and Networking . New York : ACM , 2000 : 255 - 265 .
JEDARI B , XIA F , CHEN H L , et al . A social-based watchdog system to detect selfish nodes in opportunistic mobile networks [J ] . Future Generation Computer Systems , 2019 , 92 : 777 - 788 .
HALABI T , ZULKERNINE M . Trust-based cooperative game model for secure collaboration in the Internet of vehicles [C ] // Proceedings of the ICC 2019 - 2019 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2019 : 1 - 6 .
YANG J , NI Q H , LUO G Y , et al . A trustworthy Internet of vehicles: the DAO to safe, secure, and collaborative autonomous driving [J ] . IEEE Transactions on Intelligent Vehicles , 2023 , 8 ( 12 ): 4678 - 4681 .
FENG J Y , LIU N , CAO J , et al . Securing traffic-related messages exchange against inside-and-outside collusive attack in vehicular networks [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 6 ): 9979 - 9992 .
SONG H M , KIM H R , KIM H K . Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network [C ] // Proceedings of the 2016 International Conference on Information Networking (ICOIN) . Piscataway : IEEE Press , 2016 : 63 - 68 .
ABHISHEK N V , AMAN M N , LIM T J , et al . DRiVe: detecting malicious roadside units in the Internet of vehicles with low latency data integrity [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 5 ): 3270 - 3281 .
ALI ALHEETI K M , MCDONALD-MAIER K . Hybrid intrusion detection in connected self-driving vehicles [C ] // Proceedings of the 2016 22nd International Conference on Automation and Computing (ICAC) . Piscataway : IEEE Press , 2016 : 456 - 461 .
LIANG J W , LIN Q Z , CHEN J Y , et al . A filter model based on hidden generalized mixture transition distribution model for intrusion detection system in vehicle ad hoc networks [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 7 ): 2707 - 2722 .
PETRILLO A , PESCAPÉ A , SANTINI S . A collaborative control strategy for platoons of autonomous vehicles in the presence of message falsification attacks [C ] // Proceedings of the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) . Piscataway : IEEE Press , 2017 : 110 - 115 .
PETRILLO A , PESCAPÉ A , SANTINI S . A collaborative approach for improving the security of vehicular scenarios: The case of platooning [J ] . Computer Communications , 2018 , 122 : 59 - 75 .
SHARMA P , LIU H , WANG H G , et al . Securing wireless communications of connected vehicles with artificial intelligence [C ] // Proceedings of the 2017 IEEE International Symposium on Technologies for Homeland Security (HST) . Piscataway : IEEE Press , 2017 : 1 - 7 .
SATCHIDANANDAN B , KUMAR P R . Dynamic watermarking: active defense of networked cyber–physical systems [J ] . Proceedings of the IEEE , 2017 , 105 ( 2 ): 219 - 240 .
PORTER M , HESPANHOL P , ASWANI A , et al . Detecting generalized replay attacks via time-varying dynamic watermarking [J ] . IEEE Transactions on Automatic Control , 2021 , 66 ( 8 ): 3502 - 3517 .
NAHA A , TEIXEIRA A , AHLÉN A , et al . Sequential detection of replay attacks [J ] . IEEE Transactions on Automatic Control , 2023 , 68 ( 3 ): 1941 - 1948 .
KUMAR A , SHAHID M A , JAEKEL A , et al . Machine learning based detection of replay attacks in VANET [C ] // Proceedings of the NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium . Piscataway : IEEE Press , 2023 : 1 - 6 .
BRECHT B , THERRIAULT D , WEIMERSKIRCH A , et al . A security credential management system for V2X communications [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 19 ( 12 ): 3850 - 3871 .
WANG Y Y , MASOUD N , KHOJANDI A . Real-time sensor anomaly detection and recovery in connected automated vehicle sensors [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 3 ): 1411 - 1421 .
HUANG S E , FENG Y H , LIU H X . A data-driven method for falsified vehicle trajectory identification by anomaly detection [J ] . Transportation Research Part C: Emerging Technologies , 2021 , 128 : 103196 .
HOANG T M , NGUYEN N M , DUONG T Q . Detection of eavesdropping attack in UAV-aided wireless systems: unsupervised learning with one-class SVM and K-means clustering [J ] . IEEE Wireless Communications Letters , 2020 , 9 ( 2 ): 139 - 142 .
WANG Y Y , MASOUD N , KHOJANDI A . Real-time sensor anomaly detection and recovery in connected automated vehicle sensors [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 22 ( 3 ): 1411 - 1421 .
WANG Y Z , LIU Q P , MIHANKHAH E , et al . Detection and isolation of sensor attacks for autonomous vehicles: framework, algorithms, and validation [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 7 ): 8247 - 8259 .
FANG Y , MIN H , WU X , et al . Anomaly diagnosis of connected autonomous vehicles: A survey [J ] . Information Fusion , 2024 , 105 : 102223 .
MATHUR S , TRAPPE W , MANDAYAM N , et al . Radio-telepathy: extracting a secret key from an unauthenticated wireless channel [C ] // Proceedings of the 14th ACM International Conference on Mobile Computing and Networking . New York : ACM , 2008 : 128 - 139 .
ZENG K , WU D , CHAN A , et al . Exploiting multiple-antenna diversity for shared secret key generation in wireless networks [C ] // 2010 Proceedings IEEE INFOCOM . Piscataway : IEEE Press , 2010 : 1 - 9 .
KARIMI R , KALANTARI M . Enhancing security and confidentiality in location-based data encryption algorithms [C ] // Proceedings of the Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011) . Piscataway : IEEE Press , 2011 : 30 - 35 .
KIDO H , YANAGISAWA Y , SATOH T . An anonymous communication technique using dummies for location-based services [C ] // Proceedings . International Conference on Pervasive Services , 2005 . Piscataway : IEEE Press , 2005: 88 - 97 .
NIU B , LI Q H , ZHU X Y , et al . Achieving k-anonymity in privacy-aware location-based services [C ] // Proceedings of the IEEE INFOCOM 2014 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2014 : 754 - 762 .
CHEN Y , TRAPPE W , MARTIN R P . Attack detection in wireless localization [C ] // Proceedings of the IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications . Piscataway : IEEE Press , 2007 : 1964 - 1972 .
JOVER R P . LTE security and protocol exploits [C ] // Proceeding of Shmoocn 2016 . Washington, DC : Shnoo Group , 2016 : 1 - 15 .
BERESFORD A R , STAJANO F . Location privacy in pervasive computing [J ] . IEEE Pervasive Computing , 2003 , 2 ( 1 ): 46 - 55 .
HO S S , RUAN S H . Differential privacy for location pattern mining [C ] // Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS . New York : ACM , 2011 : 17 - 24 .
DEWRI R . Local differential perturbations: location privacy under approximate knowledge attackers [J ] . IEEE Transactions on Mobile Computing , 2013 , 12 ( 12 ): 2360 - 2372 .
ZHAO P , JIANG H B , LUI J C S , et al . P3-LOC: a privacy-preserving paradigm-driven framework for indoor localization [J ] . IEEE/ACM Transactions on Networking , 2018 , 26 ( 6 ): 2856 - 2869 .
ALICHERRY M , KEROMYTIS A D . DoubleCheck: Multi-path verification against man-in-the-middle attacks [C ] // Proceedings of the 2009 IEEE Symposium on Computers and Communications . Piscataway : IEEE Press , 2009 : 557 - 563 .
ABADI M , BIRRELL A , MIRONOV I , et al . Global authentication in an untrustworthy world [C ] // 14th Workshop on Hot Topics in Operating Systems (HotOS XIV) . 2013 .
ALI M , NELSON J , SHEA R , et al . Blockstack: a global naming and storage system secured by blockchains [C ] // 2016 USENIX Annual Technical Conference (USENIX ATC 16). 2016 : 181 - 194 .
RYAN M D . Enhanced certificate transparency and end-to-end encrypted mail [C ] // Proceedings 2014 Network and Distributed System Security Symposium . Internet Society , 2014 .
LI B Y , LIN J Q , LI F J , et al . Certificate transparency in the wild: exploring the reliability of monitors [C ] // Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2019 : 2505 - 2520 .
WANG Z , LIN J Q , CAI Q W , et al . Blockchain-based certificate transparency and revocation transparency [C ] // Financial Cryptography and Data Security . Berlin, Heidelberg : Springer , 2019 : 144 - 162 .
DOUCEUR J R . The sybil attack [C ] // the 1st International Workshop on Peer-to-Peer Systems (IPTPS’02 ), Lecture Notes in Computer Science, Berlin, Heidelberg : Springer , 2022 , 2429 : 51 - 260 .
NEWSOME J , SHI E , SONG D , et al . The sybil attack in sensor networks: analysis & defenses [C ] // Proceedings of the Third International Symposium on Information Processing in Sensor Networks , 2004 . Piscataway : IEEE Press , 2004: 259 - 268 .
RAYA M , PAPADIMITRATOS P , HUBAUX J . Securing vehicular communication [J ] . IEEE Wireless Communications , 2006 , 13 ( 5 ): 8 - 15 .
PARK S , ASLAM B , TURGUT D , et al . Defense against Sybil attack in vehicular ad hoc network based on roadside unit support [C ] // Proceedings of the MILCOM 2009 - 2009 IEEE Military Communications Conference . Piscataway : IEEE Press , 2009 : 1 - 7 .
CHANG S , QI Y , ZHU H Z , et al . Footprint: detecting sybil attacks in urban vehicular networks [J ] . IEEE Transactions on Parallel and Distributed Systems , 2012 , 23 ( 6 ): 1103 - 1114 .
DEMIRBAS M , SONG Y . An RSSI-based scheme for sybil attack detection in wireless sensor networks [C ] // Proceedings of the 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06) . Piscataway : IEEE Press , 2006 : 1 - 5 .
LV S H , WANG X D , ZHAO X , et al . Detecting the sybil attack cooperatively in wireless sensor networks [C ] // Proceedings of the 2008 International Conference on Computational Intelligence and Security . Piscataway : IEEE Press , 2008 : 442 - 446 .
BOUASSIDA M S , GUETTE G , SHAWKY M , et al . Sybil nodes detection based on received signal strength variations within VANET [J ] . International Journal of Network Security , 2009 , 9 ( 1 ): 22 - 33 .
SHARMA S , KAUL A . A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud [J ] . Vehicular Communications , 2018 , 12 : 138 - 164 .
KHATRI S , VACHHANI H , SHAH S , et al . Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges [J ] . Peer-to-Peer Networking and Applications , 2021 , 14 ( 3 ): 1778 - 1805 .
CHAUHAN S S , JAIN N , PANDEY S C . Digital signature with message security process [C ] // Proceedings of the 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) . Piscataway : IEEE Press , 2022 : 182 - 187 .
HEGDE N , MANVI S S . Distributed integrity and non-repudiation scheme in the dynamic vehicular cloud environment [J ] . International Journal of Information and Computer Security , 2023 , 20 ( 3/4 ): 315 .
ARM J , FIEDLER P , BASTAN O . Offline access to a vehicle via PKI-based authentication [M ] //Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops . Cham : Springer International Publishing , 2021 : 76 - 88 .
CHEN F , WANG J H , LI J Q , et al . TrustBuilder: a non-repudiation scheme for IoT cloud applications [J ] . Computers & Security , 2022 , 116 : 102664 .
CHEN F , WANG J H , JIANG C K , et al . Blockchain based non-repudiable IoT data trading: simpler, faster, and cheaper [C ] // Proceedings of the IEEE INFOCOM 2022 - IEEE Conference on Computer Communications . Piscataway : IEEE Press , 2022 : 1958 - 1967 .
MOUSSAOUI D , KADRI B , FEHAM M , et al . A Distributed Blockchain Based PKI (BCPKI) architecture to enhance privacy in VANET [C ] // Proceedings of the 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) . Piscataway : IEEE Press , 2021 : 75 - 79 .
CHOW S , EISEN P , JOHNSON H , et al . A white-box DES implementation for DRM applications [M ] // Digital Rights Management . Berlin : Springer , 2003 : 1 - 15 .
BILLET O , GILBERT H , ECH-CHATBI C . Cryptanalysis of a white box AES implementation [C ] // Selected Areas in Cryptography . Berlin : Springer , 2004 : 227 - 240 .
BIRYUKOV A , BOUILLAGUET C , KHOVRATOVICH D . Cryptographic schemes based on the ASASA structure: black-box, white-box, and public-key (extended abstract) [M ] // Advances in Cryptology-ASIACRYPT 2014 . Berlin : Springer , 2014 : 63 - 84 .
BOGDANOV A , ISOBE T . White-box cryptography revisited: space-hard ciphers [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . New York : ACM , 2015 : 1058 - 1069 .
BOGDANOV A , ISOBE T , TISCHHAUSER E . Towards practical whitebox cryptography: optimizing efficiency and space hardness [M ] // Advances in Cryptology-ASIACRYPT 2016 . Berlin : Springer , 2016 : 126 - 158 .
WANG J Y , ZHANG H C . Bilateral adversarial training: towards fast training of more robust models against adversarial attacks [C ] // Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2019 : 6628 - 6637 .
NAZEMI A , FIEGUTH P . Potential adversarial samples for white-box attacks [EB ] . 2019 .
WANG Y X , LIU J Q , CHANG X L , et al . DI-AA: an interpretable white-box attack for fooling deep neural networks [J ] . Information Sciences , 2022 , 610 : 14 - 32 .
LIU H Y , GE Z J , ZHOU Z Y , et al . Gradient correction for white-box adversarial attacks [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 ( 12 ): 18419 - 18430 .
KAYA Y , HONG S , DUMITRAS T . On the effectiveness of regularization against membership inference attacks [EB ] . 2020 .
LI T J , LI M S , YANG Y H , et al . Frequency domain regularization for iterative adversarial attacks [J ] . Pattern Recognition , 2023 , 134 : 109075 .
易杰 , 曹腾飞 , 郜帅 , 等 . 车联网中基于攻防博弈的蜜罐防御及传输策略 [J ] . 网络与信息安全学报 , 2022 , 8 ( 4 ): 157 - 167 .
YI J , CAO T F , GAO S , et al . Honeypot defense and transmission strategy based on offensive and defensive games in vehicular networks [J ] . Chinese Journal of Network and Information Security , 2022 , 8 ( 4 ): 157 - 167 .
STEINHARDT J , KOH P W , LIANG P . Certified defenses for data poisoning attacks [EB ] . 2017 .
SHAFAHI A , HUANG W R , NAJIBI M , et al . Poison frogs targeted clean-label poisoning attacks on neural networks [EB ] . 2018 .
CHACON H , SILVA S , RAD P . Deep learning poison data attack detection [C ] // Proceedings of the 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) . Piscataway : IEEE Press , 2019 : 971 - 978 .
ROSENFELD E , WINSTON E , RAVIKUMAR P , et al . Certified robustness to label-flipping attacks via randomized smoothing [C ] // International Conference on Machine Learning . PMLR , 2020 : 8230 - 8241 .
TALPUR A , GURUSAMY M . GFCL: a GRU-based federated continual learning framework against data poisoning attacks in IoV [EB ] . 2022 .
LI X , HUANG K X , YANG W H , et al . On the convergence of FedAvg on non-IID data [EB ] . 2019 .
AVENT B , DUBEY Y , KOROLOVA A . The power of the hybrid model for mean estimation [EB ] . 2018 .
DWORK C , ROTHBLUM G N , VADHAN S . Boosting and differential privacy [C ] // Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science . Piscataway : IEEE Press , 2010 : 51 - 60 .
ZHOU H , YANG G , DAI H , et al . PFLF: privacy-preserving federated learning framework for edge computing [J ] . IEEE Transactions on Information Forensics and Security , 2022 , 17 : 1905 - 1918 .
WANG N Y , YANG W T , WANG X D , et al . A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles [J ] . Digital Communications and Networks , 2024 , 10 ( 1 ): 126 - 134 .
王瑞锦 , 唐榆程 , 张巍琦 , 等 . 基于同态加密和区块链技术的车联网隐私保护方案 [J ] . 网络与信息安全学报 , 2020 , 6 ( 1 ): 46 - 53 .
WANG R J , TANG Y C , ZHANG W Q , et al . Privacy protection scheme for Internet of vehicles based on homomorphic encryption and block chain technology [J ] . Chinese Journal of Network and Information Security , 2020 , 6 ( 1 ): 46 - 53 .
CHANG Y S , ZHANG K , GONG J Q , et al . Privacy-preserving federated learning via functional encryption, revisited [J ] . IEEE Transactions on Information Forensics and Security , 2023 , 18 : 1855 - 1869 .
KANAGAVELU R , LI Z X , SAMSUDIN J , et al . Two-phase multi-party computation enabled privacy-preserving federated learning [C ] // Proceedings of the 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) . Piscataway : IEEE Press , 2020 : 410 - 419 .
LAURENDEAU C , BARBEAU M . Threats to security in DSRC/WAVE [C ] // Ad-Hoc, Mobile, and Wireless Networks : 5th International Conference . Ottawa, Canada , 2006 , 4104 : 266 - 279 .
MALEKI M , MALIK M , FOLKESSON P , et al . Modeling and evaluating the effects of jamming attacks on connected automated road vehicles [C ] // Proceedings of the 2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC) . Piscataway : IEEE Press , 2022 : 12 - 23 .
MOSER D , LENDERS V , CAPKUN S . Digital radio signal cancellation attacks: an experimental evaluation [C ] // Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks . New York : ACM , 2019 : 23 - 33 .
LICHTMAN M , POSTON J D , AMURU S , et al . A communications jamming taxonomy [J ] . IEEE Security & Privacy , 2016 , 14 ( 1 ): 47 - 54 .
SIKARWAR H , DAS D . A novel MAC-based authentication scheme (NoMAS) for Internet of vehicles (IoV) [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 5 ): 4904 - 4916 .
JEONG S , PARK E , SEO K U , et al . MUVIDS: false MAVLink injection attack detection in communication for unmanned vehicles [C ] // Proceedings Third International Workshop on Automotive and Autonomous Vehicle Security . Internet Society , 2021 .
PAN D F , DING D R , GE X H , et al . Edge-based event-triggered platooning control of heterogeneous connected automated vehicles under replay attacks [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 11 ): 17443 - 17456 .
PENG Z F , PENG C G , TIAN Y L , et al . Bilateral control for secure communication against replay attack in ORAN-based vehicular networks [J ] . IEEE Transactions on Vehicular Technology , 2025 , 74 ( 5 ): 7565 - 7579 .
SHANGGUAN L T , CHOUR K , KO W H , et al . Dynamic watermarking for cybersecurity of autonomous vehicles [J ] . IEEE Transactions on Industrial Electronics , 2023 , 70 ( 11 ): 11735 - 11743 .
FANG C R , QI Y F , CHENG P , et al . Optimal periodic watermarking schedule for replay attack detection in cyber–physical systems [J ] . Automatica , 2020 , 112 : 108698 .
DU D J , ZHANG C D , LI X , et al . Attack detection for networked control systems using event-triggered dynamic watermarking [J ] . IEEE Transactions on Industrial Informatics , 2023 , 19 ( 1 ): 351 - 361 .
陈明奇 , 钮心忻 , 杨义先 . 数字水印的研究进展和应用 [J ] . 通信学报 , 2001 , 22 ( 5 ): 71 - 79 .
CHEN M Q , NIU X X , YANG Y X . The research developments and applications of digital watermarking [J ] . Journal of China Institute of Communications , 2001 , 22 ( 5 ): 71 - 79 .
PETIT J , SHLADOVER S E . Potential cyberattacks on automated vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 2 ): 546 - 556 .
SCOTT L . Anti-spoofing & authenticated signal architectures for civil navigation systems [C ] // Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS 2003). 2003 : 1543 - 1552 .
KUHN M G . An asymmetric security mechanism for navigation signals [M ] . Information Hiding . Berlin : Springer , 2004 : 239 - 252 .
PAPADIMITRATOS P , JOVANOVIC A . GNSS-based positioning: attacks and countermeasures [C ] // Proceedings of the MILCOM 2008 IEEE Military Communications Conference . Piscataway : IEEE Press , 2008 : 1 - 7 .
YIN S , ZHU X P , JING C . Fault detection based on a robust one class support vector machine [J ] . Neurocomputing , 2014 , 145 : 263 - 268 .
SHEN J J , WON J Y , CHEN Z Y , et al . Drift with devil: Security of multi-sensor fusion based localization in high-level autonomous driving under GPS spoofing [C ] // 29th USENIX Security Symposium (USENIX Security 20). 2020 : 931 - 948 .
WANG F L , HONG Y , BAN X G . Infrastructure-enabled GPS spoofing detection and correction [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 12 ): 13878 - 13892 .
VAN WYK F , WANG Y Y , KHOJANDI A , et al . Real-time sensor anomaly detection and identification in automated vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 3 ): 1264 - 1276 .
MOKHTAR B , AZAB M . Survey on security issues in vehicular ad hoc networks [J ] . Alexandria Engineering Journal , 2015 , 54 ( 4 ): 1115 - 1126 .
HOLTMANNS S , RAO S P , OLIVER I . User location tracking attacks for LTE networks using the interworking functionality [C ] // Proceedings of the 2016 IFIP Networking Conference (IFIP Networking) and Workshops . Piscataway : IEEE Press , 2016 : 315 - 322 .
SEDDIGH N , NANDY B , MAKKAR R , et al . Security advances and challenges in 4G wireless networks [C ] // Proceedings of the 2010 Eighth International Conference on Privacy, Security and Trust . Piscataway : IEEE Press , 2010 : 62 - 71 .
CHEN X H , MIZERA A , PANG J . Activity tracking: a new attack on location privacy [C ] // Proceedings of the 2015 IEEE Conference on Communications and Network Security (CNS) . Piscataway : IEEE Press , 2015 : 22 - 30 .
GIVEHCHIAN H , BHASKAR N , HERRERA E R , et al . Evaluating physical-layer BLE location tracking attacks on mobile devices [C ] // Proceedings of the 2022 IEEE Symposium on Security and Privacy (SP) . Piscataway : IEEE Press , 2022 : 1690 - 1704 .
LI R H , HU H B , YE Q Q . RFTrack: stealthy location inference and tracking attack on Wi-Fi devices [J ] . IEEE Transactions on Information Forensics and Security , 2024 , 19 : 5925 - 5939 .
DE ARAUJO-FILHO P F , NAILI M , KADDOUM G , et al . Unsupervised GAN-based intrusion detection system using temporal convolutional networks and self-attention [J ] . IEEE Transactions on Network and Service Management , 2023 , 20 ( 4 ): 4951 - 4963 .
JIANG H B , LI J , ZHAO P , et al . Location privacy-preserving mechanisms in location-based services [J ] . ACM Computing Surveys , 2022 , 54 ( 1 ): 1 - 36 .
SUN K K , XU H S , HUA K , et al . Joint top-k sparsification and shuffle model for communication-privacy-accuracy tradeoffs in federated-learning-based IoV [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 11 ): 19721 - 19735 .
梁应敞 , 谭俊杰 , NIYATO D . 智能无线通信技术研究概况 [J ] . 通信学报 , 2020 , 41 ( 7 ): 1 - 17 .
LIANG Y C , TAN J J , NIYATO D . Overview on intelligent wireless communication technology [J ] . Journal on Communications , 2020 , 41 ( 7 ): 1 - 17 .
YAO Y , XIAO B , WU G F , et al . Multi-channel based sybil attack detection in vehicular ad hoc networks using RSSI [J ] . IEEE Transactions on Mobile Computing , 2019 , 18 ( 2 ): 362 - 375 .
TASLIMASA H , DADKHAH S , NETO E C P , et al . Security issues in Internet of vehicles (IoV): a comprehensive survey [J ] . Internet of Things , 2023 , 22 : 100809 .
ALIMOHAMMADI M , POUYAN A A . Sybil attack detection using a low cost short group signature in VANET [C ] // Proceedings of the 2015 12th International Iranian Society of Cryptology Conference on Information Security and Cryptology (ISCISC) . Piscataway : IEEE Press , 2015 : 23 - 28 .
GHOSAL A , CONTI M . Security issues and challenges in V2X: a survey [J ] . Computer Networks , 2020 , 169 : 107093 .
KERRACHE C A , CALAFATE C T , CANO J C , et al . Trust management for vehicular networks: an adversary-oriented overview [J ] . IEEE Access , 2016 , 4 : 9293 - 9307 .
翟宝琴 , 王健 , 韩磊 , 等 . 基于信任值的车联网分层共识优化协议 [J ] . 网络与信息安全学报 , 2022 , 8 ( 3 ): 142 - 153 .
ZHAI B Q , WANG J , HAN L , et al . Hierarchical proxy consensus optimization for IoV based on blockchain and trust value [J ] . Chinese Journal of Network and Information Security , 2022 , 8 ( 3 ): 142 - 153 .
YAN C G , XIE H T , YANG D B , et al . Supervised hash coding with deep neural network for environment perception of intelligent vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 19 ( 1 ): 284 - 295 .
RAMOS S , GEHRIG S , PINGGERA P , et al . Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling [C ] // Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2017 : 1025 - 1032 .
KATRAKAZAS C , QUDDUS M , CHEN W H , et al . Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions [J ] . Transportation Research Part C: Emerging Technologies , 2015 , 60 : 416 - 442 .
ZHANG Y , SUN P , YIN Y H , et al . Human-like autonomous vehicle speed control by deep reinforcement learning with double Q-learning [C ] // Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2018 : 1251 - 1256 .
MILANÉS V , SHLADOVER S E , SPRING J , et al . Cooperative adaptive cruise control in real traffic situations [J ] . IEEE Transactions on Intelligent Transportation Systems , 2014 , 15 ( 1 ): 296 - 305 .
BALUJA S , FISCHER I . Learning to attack: adversarial transformation networks [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2018 , 32 ( 1 ).
MOUKAHAL L J , ZULKERNINE M , SOUKUP M . Boosting grey-box fuzzing for connected autonomous vehicle systems [C ] // Proceedings of the 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C) . Piscataway : IEEE Press , 2021 : 516 - 527 .
SU J W , VARGAS D V , SAKURAI K . One pixel attack for fooling deep neural networks [J ] . IEEE Transactions on Evolutionary Computation , 2019 , 23 ( 5 ): 828 - 841 .
XU D L , LI T , LI Y , et al . Edge intelligence: empowering intelligence to the edge of network [J ] . Proceedings of the IEEE , 2021 , 109 ( 11 ): 1778 - 1837 .
LANGER M , HE Z , RAHAYU W , et al . Distributed training of deep learning models: a taxonomic perspective [J ] . IEEE Transactions on Parallel and Distributed Systems , 2020 , 31 ( 12 ): 2802 - 2818 .
VERBRAEKEN J , WOLTING M , KATZY J , et al . A survey on distributed machine learning [J ] . ACM Computing Surveys , 2021 , 53 ( 2 ): 1 - 33 .
CHEN X Z , QIU W C , CHEN L X , et al . Fast and practical intrusion detection system based on federated learning for VANET [J ] . Computers & Security , 2024 , 142 : 103881 .
KUMAR K N , VISHNU C , MITRA R , et al . Black-box adversarial attacks in autonomous vehicle technology [C ] // Proceedings of the 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) . Piscataway : IEEE Press , 2020 : 1 - 7 .
WON J , SEO S H , BERTINO E . A secure shuffling mechanism for white-box attack-resistant unmanned vehicles [J ] . IEEE Transactions on Mobile Computing , 2020 , 19 ( 5 ): 1023 - 1039 .
SARKER A , SHEN H Y , SEN T , et al . An advanced black-box adversarial attack for deep driving maneuver classification models [C ] // Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) . Piscataway : IEEE Press , 2020 : 184 - 192 .
SUN J C , CAO Y L , CHEN Q A , et al . Towards robust LiDAR-based perception in autonomous driving: General black-box adversarial sensor attack and countermeasures [C ] // 29th USENIX Security Symposium (USENIX Security 20). 2020 : 877 - 894 .
DENG Y , ZHENG X , ZHANG T Y , et al . An analysis of adversarial attacks and defenses on autonomous driving models [C ] // Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom) . Piscataway : IEEE Press , 2020 : 1 - 10 .
XIONG Z B , XU H H , LI W , et al . Multi-source adversarial sample attack on autonomous vehicles [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 3 ): 2822 - 2835 .
LI Y , LIN J , XIONG K Q . An adversarial attack defending system for securing in-vehicle networks [C ] // Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) . Piscataway : IEEE Press , 2021 : 1 - 6 .
XU X , ZHANG J R , LI Y J , et al . Adversarial attack against urban scene segmentation for autonomous vehicles [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 6 ): 4117 - 4126 .
SARKER A , SHEN H Y , SEN T . A context-aware black-box adversarial attack for deep driving maneuver classification models [C ] // Proceedings of the 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) . Piscataway : IEEE Press , 2021 : 1 - 9 .
JIA S , SONG Y B , MA C , et al . IoU attack: towards temporally coherent black-box adversarial attack for visual object tracking [C ] // Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2021 : 6705 - 6714 .
SUN C , ZHANG Y , CHAOQUN W , et al . Towards lightweight black-box attack against deep neural networks [J ] . Advances in Neural Information Processing Systems , 2022 , 35 : 19319 - 19331 .
JIA W , LU Z J , YU R Z , et al . Fooling decision-based black-box automotive vision perception systems in physical world [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 7 ): 7081 - 7092 .
BIGGIO B , NELSON B , LASKOV P . Poisoning attacks against support vector machines [C ] // Proceedings of the 29th International Conference on Machine Learning . 2012 : 1807 - 1814 .
BALAKRISHNAN A , PURANIC A G , QIN X , et al . Specifying and evaluating quality metrics for vision-based perception systems [C ] // Proceedings of the 2019 Design , Automation & Test in Europe Conference & Exhibition (DATE) . Piscataway : IEEE Press , 2019 : 1433 - 1438 .
MUÑOZ-GONZÁLEZ L , BIGGIO B , DEMONTIS A , et al . Towards poisoning of deep learning algorithms with back-gradient optimization [C ] // Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security . New York : ACM , 2017 : 27 - 38 .
KOH P W , LIANG P . Understanding black-box predictions via influence functions [C ] // International conference on machine learning . 2017 : 1885 - 1894 ..
HAN X , HUANG X , CLAUDIA E . Adversarial label flips attack on support vector machines [C ] // ECAI 2012 . IOS Press , 2012 .
SITAWARIN C , BHAGOJI A N , MOSENIA A , et al . DARTS: deceiving autonomous cars with toxic signs [EB ] . 2024 .
CUI C , DU H P , JIA Z J , et al . Data poisoning attack using hybrid particle swarm optimization in connected and autonomous vehicles [C ] // Proceedings of the 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) . Piscataway : IEEE Press , 2022 : 1 - 5 .
ZHU C , HUANG W R , LI H , et al . Transferable clean-label poisoning attacks on deep neural nets [C ] // International Conference on Machine Learning . 2019 : 7614 - 7623 .
LI J Y , GUO W Z , XIE L H , et al . Privacy-preserving object detection with poisoning recognition for autonomous vehicles [J ] . IEEE Transactions on Network Science and Engineering , 2023 , 10 ( 3 ): 1487 - 1500 .
YIN D , CHEN Y D , RAMCHANDRAN K , et al . Byzantine-robust distributed learning: Towards optimal statistical rates [C ] // International Conference on Machine Learning . 2018 : 5650 - 5659 .
BLANCHARD P , EL MHAMDI E M , GUERRAOUI R , et al . Machine learning with adversaries: byzantine tolerant gradient descent [J ] . Advances in Neural Information Processing Systems , 2017 , 30 .
CHANG K , BALACHANDAR N , LAM C , et al . Distributed deep learning networks among institutions for medical imaging open access [J ] . Journal of the American Medical Informatics Association , 2018 , 25 ( 8 ): 945 - 954 .
GUO J , SONG B , CHEN S Q , et al . Context-aware object detection for vehicular networks based on edge-cloud cooperation [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 7 ): 5783 - 5791 .
SUN K K , WU J , LI J H . Reputation-aware incentive mechanism of federated learning: a mean field game approach [C ] // Proceedings of the 2024 9th IEEE International Conference on Smart Cloud (SmartCloud) . Piscataway : IEEE Press , 2024 : 48 - 53 .
SUN K K , XU H S , ZHANG X J , et al . Time-sensitive local differential privacy-based federated learning for vehicular digital twin networks [C ] // Proceedings of the International Symposium on Intelligent Computing and Networking 2024 . Cham : Springer Nature Switzerland , 2024 : 105 - 118 .
RAZZAQ W , ARIF U , DIN Z M U . Visual perception deep drive model for self-driving car [J ] . Pakistan Journal of Scientific Research , 2022 , 1 ( 1 ): 18 - 21 .
XIONG J B , BI R W , ZHAO M F , et al . Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles [J ] . IEEE Wireless Communications , 2020 , 27 ( 3 ): 24 - 30 .
ZHANG Q C , YANG L T , CHEN Z K . Privacy preserving deep computation model on cloud for big data feature learning [J ] . IEEE Transactions on Computers , 2016 , 65 ( 5 ): 1351 - 1362 .
SUN K K , LIU L Z , PAN Q Q , et al . Large-scale mean-field federated learning for detection and defense: a Byzantine robustness approach in IoT [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 22 ): 36370 - 36383 .
SUN L C , QIAN J W , CHEN X . LDP-FL: Practical private aggregation in federated learning with local differential privacy [C ] // Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence . 2021 : 1571 - 1578 .
陈秀真 , 吴越 , 李建华 . 车载信息系统的安全测评体系及方法 [J ] , 信息安全学报 , 2017 , 2 ( 2 ): 15 - 23 .
CHEN X Z , WU Y , LI J H . Security evaluation system and method of in-vehicle information system [J ] . Journal of Information Security , 2017 , 2 ( 2 ): 15 - 23 .
SUN K K , WU J , BASHIR A K , et al . Personalized privacy-preserving distributed artificial intelligence for digital-twin-driven vehicle road cooperation [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 22 ): 35902 - 35916 .
LIANG F Y , YANG Q L , LIU R Q , et al . Semi-synchronous federated learning protocol with dynamic aggregation in Internet of vehicles [J ] . IEEE Transactions on Vehicular Technology , 2022 , 71 ( 5 ): 4677 - 4691 .
PAN D F , DING D R , GE X H , et al . Edge-based event-triggered platooning control of heterogeneous connected automated vehicles under replay attacks [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 11 ): 17443 - 17456 .
KHAN N , COLERI S . Event-triggered reinforcement learning based joint resource allocation for ultra-reliable low-latency V2X communications [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 11 ): 16991 - 17006 .
SUN K K , WU J , PAN Q Q , et al . Leveraging digital twin and DRL for collaborative context offloading in C-V2X autonomous driving [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 4 ): 5020 - 5035 .
LI Y J , TAO X F , ZHANG X F , et al . Privacy-preserved federated learning for autonomous driving [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 7 ): 8423 - 8434 .
PENG T , ZHONG W T , WANG G J , et al . Privacy-preserving truth discovery based on secure multi-party computation in vehicle-based mobile crowdsensing [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 7 ): 7767 - 7779 .
GABAY D , AKKAYA K , CEBE M . Privacy-preserving authentication scheme for connected electric vehicles using blockchain and zero knowledge proofs [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 6 ): 5760 - 5772 .
ZHAO Y , ZHAO J , YANG M M , et al . Local differential privacy-based federated learning for Internet of things [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 11 ): 8836 - 8853 .
HOU S , CHEN X Z , MA J , et al . An ontology-based dynamic attack graph generation approach for the internet of vehicles [J ] . Frontiers in energy research 2022 , 10 : 928919 .
QIU W , MA Y , CHEN X Z , et al . Hybrid intrusion detection system based on Dempster-Shafer evidence theory [J ] . Computers & Security 2022 , 117 : 102709 .
于海洋 , 陈秀真 , 马进 , 等 . 面向智能汽车的信息安全漏洞评分模型 [J ] . 网络与信息安全学报 , 2022 , 8 ( 1 ): 167 - 179 .
YU H Y , CHEN X Z , MA J , et al . Information security vulnerability scoring model for intelligent vehicles [J ] . Journal of Network and Information Security , 2022 , 8 ( 1 ): 167 - 179 .
XU C , ZHENG F , GUO G . Stealthy false data injection attacks in multi-channel vehicular communication: white-box and gray-box strategies [J ] . IEEE Transactions on Vehicular Technology , 2025 .
0
Views
56
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621