How to Cite
Abstract
Autonomous vehicles, also known as driverless, connected or self-driving cars, are no longer a concept of the more or less distant future, but a transportation factor that, depending on the formal definitions (such as "level-5" autonomy), should reach the full-public availability in the next fifteen years. Encouraged by major vehicle manufacturers who have declared innumerable efforts in research and development, as well as by the spread of vehicles with progressively "partial" or "conditional" self-driving functions, numerous entities have undertaken a wide range of activities to prepare the communications infrastructure and protocols for the full development of these revolutionary vehicles, covering issues related to safety, latency, quality and security of communications. Indeed, such an integration of digital technology can define more efficient traffic flows and stretches in both urban and road areas, revolutionizing some current models and rules. To date, the majority of the functional platforms proposed and some prototypes of autonomous vehicles using recent technology, such as clusters of processors connected to networks with different topologies, i.e., CAN, LIN, FlexRay, Ethernet (IP), DSRC (Dedicated Short Range Communication), with different requirements for continuity and safety, cyclic transmission and redundancy protocols for certain functions, have been designed and tested for certain types of roads and services. Due to the intrinsic security problems related to each technology, sensible to external attacks or faults, the subject of cybersecurity is recognized as an enabling factor both for privacy-sensitive services for passengers and for security-sensitive applications.
References
S. Zhang, S. Zhao, Y. Liu, W. Shi, and L. Su, "Dynamic risk assessment in the Internet of Things," in 2016 IEEE International Conference on Communications (ICC), 2016, pp. 1-6.
Y. Fang, Y. Zhang, Y. Qian, and S. Li, "A dynamic risk assessment method for IoT-based intelligent transportation systems," in 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2017, pp. 1125-1130.
S. R. Mishra and S. K. Satapathy, "A dynamic risk assessment system for IoT based real time applications," in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017, pp. 3304-3309.
A. Albogami, S. El-Alfy, and E. Shakshuki, "A dynamic risk assessment model for IoT systems," in 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC), 2019, pp. 246-253.
S. Ouadoudi, S. Benamar, A. Ait Ouahman, and K. Sabri, "Dynamic risk assessment in IoT networks using Bayesian networks," in 2019 IEEE 5th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), 2019, pp. 1-6.
R. S. Hidayat, S. A. Suryana, and Y. Choi, "Dynamic risk assessment model based on fuzzy logic for IoT network security," in 2019 5th International Conference on Science in Information Technology (ICSITech), 2019, pp. 44-49.
Y. Wang, F. Xu, J. Wang, and Y. Liu, "Dynamic risk assessment of Internet of Things based on ELM algorithm," in 2020 IEEE 3rd International Conference on Electronics Technology (ICET), 2020, pp. 90-93.
H. P. Nguyen, T. H. Dao, and D. N. Nguyen, "Dynamic risk assessment model for Internet of Things based on Dempster-Shafer theory," in 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), 2020, pp. 122-127.
H. Geng, X. Chen, Z. Lu, and D. L. Lee, "Dynamic risk assessment in IoT networks using a deep learning approach," in 2020 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2020, pp. 1161-1166.
Z. Li, S. Zhang, and Z. Liu, "Dynamic risk assessment method for IoT system based on adaptive boosting algorithm," in 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2020, pp. 1342-1345.
S. S. Hamad, R. A. H. Abdulsalam, and M. N. G. Khan, "Dynamic risk assessment in Internet of Things (IoT) using machine learning algorithms," in 2020 5th International Conference on Computing, Communication and Security (ICCCS), 2020, pp. 1-5.
Tatineni, Sumanth. "Compliance and Audit Challenges in DevOps: A Security Perspective." International Research Journal of Modernization in Engineering Technology and Science 5.10 (2023): 1306-1316.
Vemori, Vamsi. "From Tactile Buttons to Digital Orchestration: A Paradigm Shift in Vehicle Control with Smartphone Integration and Smart UI–Unveiling Cybersecurity Vulnerabilities and Fortifying Autonomous Vehicles with Adaptive Learning Intrusion Detection Systems." African Journal of Artificial Intelligence and Sustainable Development3.1 (2023): 54-91.
Mahammad Shaik. “Rethinking Federated Identity Management: A Blockchain-Enabled Framework for Enhanced Security, Interoperability, and User Sovereignty”. Blockchain Technology and Distributed Systems, vol. 2, no. 1, June 2022, pp. 21-45, https://thesciencebrigade.com/btds/article/view/223.
S. Kim, J. Cho, and J. H. Park, "Dynamic risk assessment model for IoT devices using machine learning," in 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021, pp. 941-944.
J. Zhou, X. Zhang, Y. Qin, and W. Zhang, "Dynamic risk assessment in the Internet of Things using a hybrid model," in 2021 5th IEEE International Conference on Computer and Communications (ICCC), 2021, pp. 3205-3209.
J. H. Yoon and J. Lee, "A dynamic risk assessment model for Internet of Things security using machine learning," in 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), 2021, pp. 1-4.
M. M. Rahman, M. Z. Shakir, and I. Zeadally, "Dynamic risk assessment for IoT networks using machine learning," in 2021 2nd International Conference on Computer Applications & Information Security (ICCAIS), 2021, pp. 1-5.
S. W. Lee, K. Kim, and S. K. Baik, "Dynamic risk assessment in IoT networks using machine learning and blockchain," in 2021 IEEE 21st International Conference on Advanced Communication Technology (ICACT), 2021, pp. 431-435.
Y. Ren, J. Zhao, and M. R. Javed, "Dynamic risk assessment for IoT security using deep reinforcement learning," in 2021 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2021, pp. 1060-1065.
M. S. Elbamby, A. Zoha, and M. Hassan, "A survey of machine learning in Internet of Things (IoT) security," IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7289-7310, Oct. 2019.