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SCIENTIA SINICA Informationis, Volume 49 , Issue 12 : 1640-1658(2019) https://doi.org/10.1360/SSI-2019-0176

Integrated security of cyber-physical vehicle networked systems in the age of 5G

More info
  • ReceivedAug 21, 2019
  • AcceptedOct 8, 2019
  • PublishedDec 13, 2019

Abstract


Funded by

国家重点研发计划(2016YFB0901900)

国家自然科学基金(U1736205,U1766215,61972313)


References

[1] World Health Organization. Global status report on road safety 2018. 2018. https://apps.who.int/iris/bitstream/ handle/10665/276462/9789241565684-eng.pdf?ua=1. Google Scholar

[2] National Bureau of Statistics of China. China statistical yearbook-2018. 2018. Google Scholar

[3] Gupta A, Jha R K. A Survey of 5G Network: Architecture and Emerging Technologies. IEEE Access, 2015, 3: 1206-1232 CrossRef Google Scholar

[4] Jiang D J, Liu G Y. An overview of 5G requirements. In: 5G Mobile Communications. Berlin: Springer, 2017. 3--26. Google Scholar

[5] Li S C, Xu L D, Zhao S S. 5G Internet of Things: A survey. J Industrial Inf Integration, 2018, 10: 1-9 CrossRef Google Scholar

[6] Huawei. Top ten application scenarios in the 5G era white paper. 2017. Google Scholar

[7] Chen Z Y, Yu J D, Zhu Y M, et al. D3: Abnormal driving behaviors detection and identification using smartphone sensors. In: Proceedings of the 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, 2015. 524--532. Google Scholar

[8] Fang S H, Liang Y C, Chiu K M. Developing a mobile phone-based fall detection system on android platform. In: Proceedings of Computing, Communications and Applications Conference, Hong Kong, 2012. 143--146. Google Scholar

[9] Morris B T, Trivedi M M. A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance. IEEE Trans Circuits Syst Video Technol, 2008, 18: 1114-1127 CrossRef Google Scholar

[10] Zaldivar J, Calafate C T, Cano J C, et al. Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones. In: Proceedings of the 36th Conference on Local Computer Networks, Bonn, 2011. 813--819. Google Scholar

[11] Zhang M M, Chen C, Wo T Y. SafeDrive: Online Driving Anomaly Detection From Large-Scale Vehicle Data. IEEE Trans Ind Inf, 2017, 13: 2087-2096 CrossRef Google Scholar

[12] Jabon M, Bailenson J, Pontikakis E, et al. Facial expression analysis for predicting unsafe driving behavior. IEEE Pervasive Computing, 2010, 10(4): 84-95. Google Scholar

[13] Ahmad R, Borole J N. Drowsy driver identification using eye blink detection. Int J Comput Sci Inf Tech, 2015, 6: 270--274. Google Scholar

[14] Jain A, Singh A, Koppula H S, et al. Recurrent neural networks for driver activity anticipation via sensory-fusion architecture. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016. 3118--3125. Google Scholar

[15] Hallac D, Sharang A, Stahlmann R, et al. Driver identification using automobile sensor data from a single turn. In: Proceedings of the 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016. 953--958. Google Scholar

[16] Wu J D, Ye S H. Driver identification using finger-vein patterns with Radon transform and neural network. Expert Syst Appl, 2009, 36: 5793-5799 CrossRef Google Scholar

[17] Barr J R, Bowyer K W, Flynn P J. FACE RECOGNITION FROM VIDEO: A REVIEW. Int J Patt Recogn Artif Intell, 2012, 26: 1266002 CrossRef Google Scholar

[18] Deniz O, Serrano I, Bueno G, et al. Fast violence detection in video. In: Proceedings of International Conference on Computer Vision Theory and Applications, Lisbon Portugal, 2014. 2: 478--485. Google Scholar

[19] Sivaraman S, Trivedi M M. Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis. IEEE Trans Intell Transp Syst, 2013, 14: 1773-1795 CrossRef Google Scholar

[20] Kumar S, Shi L, Ahmed N, et al. Carspeak: a content-centric network for autonomous driving. In: Proceedings of ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Helsinki, 2012. 259--270. Google Scholar

[21] Sakiz F, Sen S. A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Networks, 2017, 61: 33-50 CrossRef Google Scholar

[22] Halimeh J C, Roser M. Raindrop detection on car windshields using geometric-photometric environment construction and intensity-based correlation. In: Proceedings of IEEE Intelligent Vehicles Symposium, Xi'an, 2009. 610--615. Google Scholar

[23] Sato R, Domany K, Deguchi D, et al. Visibility estimation of traffic signals under rainy weather conditions for smart driving support. In: Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, 2012. 1321--1326. Google Scholar

[24] Liu W, Maruya K. Detection and recognition of traffic signs in adverse conditions. In: Proceedings of IEEE Intelligent Vehicles Symposium, Xi'an, 2009. 335--340. Google Scholar

[25] Mori K, Takahashi T, Ide I, et al. Recognition of foggy conditions by in-vehicle camera and millimeter wave radar. In: Proceedings of IEEE Intelligent Vehicles Symposium, Istanbul, 2007. 87--92. Google Scholar

[26] Li Q Q, Chen L, Li M. A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios. IEEE Trans Veh Technol, 2014, 63: 540-555 CrossRef Google Scholar

[27] Eriksson J, Girod L, Hull B, et al. The pothole patrol: using a mobile sensor network for road surface monitoring. In: Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services, Breckenridge, 2008. 29--39. Google Scholar

[28] Chen K Y, Lu M M, Fan X P, et al. Road condition monitoring using on-board three-axis accelerometer and GPS sensor. In: Proceedings of the 6th International ICST Conference on Communications and Networking in China (CHINACOM), Harbin, 2011. 1032--1037. Google Scholar

[29] Mohan P, Padmanabhan V N, Ramjee R. Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, Raleigh, 2008. 323--336. Google Scholar

[30] Bhoraskar R, Vankadhara N, Raman B, et al. Wolverine: traffic and road condition estimation using smartphone sensors. In: Proceedings of the 4th International Conference on Communication Systems and Networks (COMSNETS 2012), Bangalore, 2012. 1--6. Google Scholar

[31] Glaser S, Nouveliere L, Lusetti B. Speed limitation based on an advanced curve warning system. In: Proceedings of IEEE Intelligent Vehicles Symposium, Istanbul, 2007. 686--691. Google Scholar

[32] García F, Jiménez F, Anaya J. Distributed pedestrian detection alerts based on data fusion with accurate localization.. Sensors, 2013, 13: 11687-11708 CrossRef PubMed Google Scholar

[33] Gámez Serna C, Ruichek Y. Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits.. Sensors, 2017, 17: 1383 CrossRef PubMed Google Scholar

[34] Hussein A, García F, Armingol J M, et al. P2V and V2P communication for pedestrian warning on the basis of autonomous vehicles. In: Proceedings of the 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016. 2034--2039. Google Scholar

[35] Nkenyereye L, Liu C H, Song J S. Towards secure and privacy preserving collision avoidance system in 5G fog based Internet of Vehicles. Future Generation Comput Syst, 2019, 95: 488-499 CrossRef Google Scholar

[36] Kokuti A, Hussein A, Marín-Plaza P, et al. V2X communications architecture for off-road autonomous vehicles. In: Proceedings of IEEE International Conference on Vehicular Electronics and Safety (ICVES), Vienna, 2017. 69--74. Google Scholar

[37] Koscher K, Czeskis A, Roesner F, et al. Experimental security analysis of a modern automobile. In: Proceedings of IEEE Symposium on Security and Privacy, Berkeley/Oakland, 2010. 447--462. Google Scholar

[38] Sau S, Haj-Yahya J, Wong M M, et al. Survey of secure processors. In: Proceedings of International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), Pythagorion, 2017. 253--260. Google Scholar

[39] Bécsi T, Aradi S, Géspár P. Security issues and vulnerabilities in connected car systems. In: Proceedings of International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, 2015. 477--482. Google Scholar

[40] Wang Q, Sawhney S. VeCure: a practical security framework to protect the CAN bus of vehicles. In: Proceedings of International Conference on the Internet of Things (IOT), Cambridge, 2014. 13--18. Google Scholar

[41] Avatefipour O, Malik H. State-of-the-art survey on in-vehicle network communication (CAN-Bus) security and vulnerabilities. Int J Comput Sci Netw, 2017, 6: 720--727. Google Scholar

[42] Keenlab. Experimental security assessment of BMW cars: a summary report. 2018. https://keenlab.tencent.com/en/ 2018/05/22/New-CarHacking-Research-by-KeenLab-Experimental-Security-Assessment-of-BMW-Cars/. Google Scholar

[43] Nie S, Liu L, Du Y. Free-fall: hacking tesla from wireless to CAN bus. Briefing, Black Hat USA, 2017: 1--16. Google Scholar

[44] Hashem Eiza M, Ni Q. Driving with Sharks: Rethinking Connected Vehicles with Vehicle Cybersecurity. IEEE Veh Technol Mag, 2017, 12: 45-51 CrossRef Google Scholar

[45] Checkoway S, McCoy D, Kantor B, et al. Comprehensive experimental analyses of automotive attack surfaces. In: Proceedings of the 20th USENIX Security Symposium, San Francisco, 2011. 447--462. Google Scholar

[46] Ishtiaq Roufa R M, Mustafaa H, Travis Taylora S O, et al. Security and privacy vulnerabilities of in-car wireless networks: A tire pressure monitoring system case study. In: Proceedings of the 19th USENIX Security Symposium, Washington, 2010. 11--13. Google Scholar

[47] Markovitz M, Wool A. Field classification, modeling and anomaly detection in unknown CAN bus networks. Vehicular Commun, 2017, 9: 43-52 CrossRef Google Scholar

[48] Marchetti M, Stabili D. READ: Reverse Engineering of Automotive Data Frames. IEEE TransInformForensic Secur, 2019, 14: 1083-1097 CrossRef Google Scholar

[49] Pawelec K, Bridges R A, Combs F L. Towards a CAN IDS based on a neural network data field predictor. In: Proceedings of ACM Workshop on Automotive Cybersecurity, Dallas, 2019. 31--34. Google Scholar

[50] Olufowobi H, Ezeobi U, Muhati E, et al. Anomaly detection approach using adaptive cumulative sum algorithm for controller area network. In: Proceedings of ACM Workshop on Automotive Cybersecurity, Dallas, 2019. 25--30. Google Scholar

[51] Young C, Olufowobi H, Bloom G, et al. Automotive intrusion detection based on constant CAN message frequencies across vehicle driving modes. In: Proceedings of ACM Workshop on Automotive Cybersecurity, Dallas, 2019. 9--14. Google Scholar

[52] Koyama T, Shibahara T, Hasegawa K, et al. Anomaly detection for mixed transmission CAN messages using quantized intervals and absolute difference of payloads. In: Proceedings of ACM Workshop on Automotive Cybersecurity, Dallas, 2019. 19--24. Google Scholar

[53] 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. In: Proceedings of International Conference on Information Networking (ICOIN), Kota Kinabalu, 2016. 63--68. Google Scholar

[54] Choi W, Joo K, Jo H J. VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System. IEEE TransInformForensic Secur, 2018, 13: 2114-2129 CrossRef Google Scholar

[55] Gerla M, Lee E K, Pau G, et al. Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: Proceedings of IEEE World Forum on Internet of Things (WF-IoT), Seoul, 2014. 241--246. Google Scholar

[56] Petit J, Stottelaar B, Feiri M, et al. Remote attacks on automated vehicles sensors: experiments on camera and lidar. Black Hat Europe, 2015, 11: 2015. Google Scholar

[57] Yan C, Xu W, Liu J. Can you trust autonomous vehicles: Contactless attacks against sensors of self-driving vehicle. DEF CON, 2016, 24. Google Scholar

[58] Sitawarin C, Bhagoji A N, Mosenia A, et al. Darts: deceiving autonomous cars with toxic signs. 2018,. arXiv Google Scholar

[59] Rahimi-Eichi H, Ojha U, Baronti F. Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles. EEE Ind Electron Mag, 2013, 7: 4-16 CrossRef Google Scholar

[60] Sagstetter F, Lukasiewycz M, Steinhorst S, et al. Security challenges in automotive hardware/software architecture design. In: Proceedings of Conference on Design, Automation and Test in Europe, Grenoble, 2013. 458--463. Google Scholar

[61] Falk R, Fries S. Electric vehicle charging infrastructure security considerations and approaches. In: Proceedings of the 4th International Conference on Evolving Internet, 2012. 58--64. Google Scholar

[62] Mustafa M A, Zhang N, Kalogridis G, et al. Smart electric vehicle charging: security analysis. In: Proceedings of IEEE PES Innovative Smart Grid Technologies Conference (ISGT), Washington, 2013. 1--6. Google Scholar

[63] Siemens. Traffic control via the Siemens private cloud. 2014. www.siemens.com/press/pool/de/events/2014/infrastructure-cities/2014-03-intertraffic/background-private-cloud-e.pdf. Google Scholar

[64] Tennessee Department of Transportation. TDOT smartway. www.tdot.state.tn.us/tdotsmartway/. Google Scholar

[65] Liu J, Li J T, Zhang L. Secure intelligent traffic light control using fog computing. Future Generation Comput Syst, 2018, 78: 817-824 CrossRef Google Scholar

[66] Ghena B, Beyer W, Hillaker A, et al. Green lights forever: Analyzing the security of traffic infrastructure. In: Proceedings of the 8th USENIX Workshop on Offensive Technologies, Berkeley, 2014. 7. Google Scholar

[67] Laszka A, Potteiger B, Vorobeychik Y, et al. Vulnerability of transportation networks to traffic-signal tampering. In: Proceedings of the 7th International conference on Cyber-Physical Systems (ICCPS), Vienna, 2016. 1--10. Google Scholar

[68] Mazloom S, Rezaeirad M, Hunter A, et al. A security analysis of an in-vehicle infotainment and app platform. In: Proceedings of 10th USENIX Workshop on Offensive Technologies, Austin, 2016. 1--12. Google Scholar

[69] Jo H J, Choi W, Na S Y. Vulnerabilities of Android OS-Based Telematics System. Wireless Pers Commun, 2017, 92: 1511-1530 CrossRef Google Scholar

[70] Wang X, Konstantinou C, Maniatakos M, et al. Confirm: detecting firmware modifications in embedded systems using hardware performance counters. In: Proceedings of IEEE/ACM International Conference on Computer-aided Design, Austin, 2015. 544--551. Google Scholar

[71] Faruki P, Bharmal A, Laxmi V. Android Security: A Survey of Issues, Malware Penetration, and Defenses. IEEE Commun Surv Tut, 2015, 17: 998-1022 CrossRef Google Scholar

[72] Zhou W, Zhou Y J, Jiang X X, et al. Detecting repackaged smartphone applications in third-party android marketplaces. In: Proceedings of the 2nd ACM Conference on Data and Application Security and Privacy, San Antonio, 2012. 317--326. Google Scholar

[73] Mutchler P, Doupé A, Mitchell J, et al. A large-scale study of mobile web app security. In: Proceedings of Mobile Security Technologies Workshop (MoST), San Jose, 2015. 1--11. Google Scholar

[74] Martin W, Sarro F, Jia Y. A Survey of App Store Analysis for Software Engineering. IIEEE Trans Software Eng, 2017, 43: 817-847 CrossRef Google Scholar

[75] Watanabe T, Akiyama M, Kanei F, et al. Understanding the origins of mobile app vulnerabilities: a large-scale measurement study of free and paid apps. In: Proceedings of the 14th International Conference on Mining Software Repositories, Buenos Aires, 2017. 14--24. Google Scholar

[76] Sbirlea D, Burke M G, Guarnieri S. Automatic detection of inter-application permission leaks in Android applications. IBM J Res Dev, 2013, 57: 10:1-10:12 CrossRef Google Scholar

[77] Woo S, Jo H J, Lee D H. A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN. IEEE Trans Intell Transp Syst, 2014, : 1-14 CrossRef Google Scholar

[78] Rastogi S, Bhushan K, Gupta B B. Android Applications Repackaging Detection Techniques for Smartphone Devices. Procedia Comput Sci, 2016, 78: 26-32 CrossRef Google Scholar

[79] Hsueh S C, Li J T. Secure transmission protocol for the IoT. In: Proceedings of the 3rd International Conference on Industrial and Business Engineering, Sapporo, 2017. 73--76. Google Scholar

[80] Granjal J, Monteiro E, Sa Silva J. Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Commun Surv Tut, 2015, 17: 1294-1312 CrossRef Google Scholar

[81] Pelzl J, Wolf M, Wollinger T. Virtualization Technologies for Cars. Technical Report 2008, escrypt GmbH-Embedded Security, 2008. Google Scholar

[82] Le V H, den Hartog J, Zannone N. Security and privacy for innovative automotive applications: A survey. Comput Commun, 2018, 132: 17-41 CrossRef Google Scholar

[83] Jeong E, Park J, Son B, et al. Study on signature verification process for the firmware of an android platform. In: Proceedings of International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Matsue, 2018. 540--545. Google Scholar

[84] Petri R, Springer M, Zelle D, et al. Evaluation of lightweight TPMs for automotive software updates over the air. In: Proceedings of the 4th International Conference on Embedded Security in Car USA, Detroit, 2016. 1--15. Google Scholar

[85] Luo Q, Liu J J. Wireless Telematics Systems in Emerging Intelligent and Connected Vehicles: Threats and Solutions. IEEE Wireless Commun, 2018, 25: 113-119 CrossRef Google Scholar

[86] Chawan A, Sun W, Javaid A, et al. Security enhancement of over-the-air update for connected vehicles. In: Proceedings of International Conference on Wireless Algorithms, Systems, and Applications, Tianjin, 2018. 853--864. Google Scholar

[87] Ashraf M I, Liu C F, Bennis M, et al. Towards low-latency and ultra-reliable vehicle-to-vehicle communication. In: Proceedings of European Conference on Networks and Communications, Oulu, 2017. 1--5. Google Scholar

[88] Shah S A A, Ahmed E, Imran M. 5G for Vehicular Communications. IEEE Commun Mag, 2018, 56: 111-117 CrossRef Google Scholar

[89] Engoulou R G, Bella^ıche M, Pierre S, et al. VANET security surveys. Comput Commun, 2014, 44: 1--3. Google Scholar

[90] Series M. IMT Vision---framework and overall objectives of the future development of IMT for 2020 and beyond. Recommendation ITU, 2015: 2083-0. Google Scholar

[91] Poli F. Vehicular communications: from DSRC to Cellular V2X. Politecnico di Torino, 2018. Google Scholar

[92] Zhou H B, Xu W C, Bi Y G. Toward 5G Spectrum Sharing for Immersive-Experience-Driven Vehicular Communications. IEEE Wireless Commun, 2017, 24: 30-37 CrossRef Google Scholar

[93] Masini B, Bazzi A, Zanella A. A Survey on the Roadmap to Mandate on Board Connectivity and Enable V2V-Based Vehicular Sensor Networks.. Sensors, 2018, 18: 2207 CrossRef PubMed Google Scholar

[94] Nguyen T V, Shailesh P, Sudhir B, et al. A comparison of cellular vehicle-to-everything and dedicated short range communication. In: Proceedings of 2017 IEEE Vehicular Networking Conference (VNC), Torino, 2017. 101--108. Google Scholar

[95] Ancans A, Petersons E, Ancans G. Technical and economic analysis of transport telecommunication infrastructure. Procedia Comput Sci, 2019, 149: 206-214 CrossRef Google Scholar

[96] Woolley M, Schmidt S. Bluetooth 5/Go Faster, Go Further. Bluetooth SIG, 2017, 1: 1--25. Google Scholar

[97] Funai C, Tapparello C, Heinzelman W. Enabling multi-hop ad hoc networks through WiFi Direct multi-group networking. In: Proceedings of 2017 International Conference on Computing, Networking and Communications (ICNC), Silicon Valley, 2017. 491--497. Google Scholar

[98] Schneider P, Horn G. Towards 5G security. In: Proceedings of IEEE Trustcom/BigDataSE/ISPA, Helsinki, 2015. 1165--1170. Google Scholar

[99] Fang D F, Qian Y, Hu R Q. Security for 5G Mobile Wireless Networks. IEEE Access, 2018, 6: 4850-4874 CrossRef Google Scholar

[100] Zaidi K, Rajarajan M. Vehicular Internet: Security & Privacy Challenges and Opportunities. Future Internet, 2015, 7: 257-275 CrossRef Google Scholar

[101] Petit J, Shladover S E. Potential Cyberattacks on Automated Vehicles. IEEE Trans Intell Transp Syst, 2014, : 1-11 CrossRef Google Scholar

[102] Ahmad I, Kumar T, Liyanage M. Overview of 5G Security Challenges and Solutions. IEEE Comm Stand Mag, 2018, 2: 36-43 CrossRef Google Scholar

[103] Malla A M, Sahu R K. Security attacks with an effective solution for dos attacks in VANET. Int J Comput Appl, 2013, 66. Google Scholar

[104] Jeske T. Floating car data from smartphones: what google and waze know about you and how hackers can control traffic. In: Proceedings of the BlackHat Europe, Amsterdam, 2013. 1--12. Google Scholar

[105] Lee E, Lee E K, Gerla M. Vehicular cloud networking: architecture and design principles. IEEE Commun Mag, 2014, 52: 148-155 CrossRef Google Scholar

[106] Boukerche A, De Grande R E. Vehicular cloud computing: Architectures, applications, and mobility. Comput Networks, 2018, 135: 171-189 CrossRef Google Scholar

[107] Whaiduzzaman M, Sookhak M, Gani A. A survey on vehicular cloud computing. J Network Comput Appl, 2014, 40: 325-344 CrossRef Google Scholar

[108] Ning Z L, Wang X J, Huang J. Mobile Edge Computing-Enabled 5G Vehicular Networks: Toward the Integration of Communication and Computing. IEEE Veh Technol Mag, 2019, 14: 54-61 CrossRef Google Scholar

[109] Hashem Eiza M, Ni Q, Shi Q. Secure and Privacy-Aware Cloud-Assisted Video Reporting Service in 5G-Enabled Vehicular Networks. IEEE Trans Veh Technol, 2016, 65: 7868-7881 CrossRef Google Scholar

[110] Sun Y C, Zhang J S, Xiong Y P. Data Security and Privacy in Cloud Computing. Int J Distributed Sens Networks, 2014, 10: 190903 CrossRef Google Scholar

[111] Nafi K W, Kar T S, Hoque S A. A Newer User Authentication, File encryption and Distributed Server Based Cloud Computing security architecture. IJACSA, 2012, 3 CrossRef Google Scholar

[112] Wang C, Wang Q, Ren K, et al. Privacy-preserving public auditing for data storage security in cloud computing. In: Proceedings of IEEE Infocom, San Diego, 2010. 1--9. Google Scholar

[113] Zhang W Y, Li S G, Liu L Y, et al. Hetero-edge: orchestration of real-time vision applications on heterogeneous edge clouds. In: Proceedings of IEEE Conference on Computer Communications, Paris, 2019. 1270--1278. Google Scholar

[114] Taleb T, Samdanis K, Mada B. On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration. IEEE Commun Surv Tut, 2017, 19: 1657-1681 CrossRef Google Scholar

  • Figure 1

    (Color online)Integrated security framework for cyber-physical vehicle networked systems

  • Figure 2

    (Color online)Security event perception metrics for cyber-physical vehicle networked systems

  • Figure 3

    (Color online)Key capabilities comparison between 4G and 5G [91]

  • Figure 4

    Event-based integrated security monitoring & intelligent decision-making scheme

  • Table 1   Key capabilities comparison among vehicular communication technologies
    Data rate Frequency band Range Mobility support Coverage
    3G [90] 2 Mbit/s 700–2600 MHz Up to 10 km High Ubiquitous
    4G [90-92] 1 Gbit/s Licensed band Up to 30 km High (350 km/h) Ubiquitous
    5G [91] 20 Gbit/s Licensed band 300–400 m High (500 km/h) Intermittent
    Drive-thru Internet [92] 150 Mbit/s 2.4 GHz/5 GHz 500 m High (120 km/h) Intermittent
    DSRC [92] 3–27 Mbit/s 5.86–5.92 GHz 300–1000 m High (140 km/h) Intermittent
    TV white space [92] 420 Mbit/s 470–790 MHz 1 km/17–33 km High (114 km/h) Intermittent
    Cellular V2X [90,93-95] 3 Gbit/s 5.9 GHz 1.6 km High (up to 250 km/h) Intermittent
    Wi-Fi [90] 6–54 Mbit/s 2.4 GHz/5.2 GHz Up to 100 m Low Intermittent
    Bluetooth 5 [90,96] 50 Mbit/s 2.4 GHz 240 m N/A N/A
    WiFi direct [90,97] 250 Mbit/s 2.4 GHz/5 GHz 200 m N/A N/A
    LTE direct [90] 13.5 Mbit/s Licensed LTE spectrum 500 m N/A N/A