Exploring Human Factors in Autonomous Vehicle Cybersecurity - A Human-Computer Interaction Approach: Investigates human factors influencing cybersecurity in AVs, adopting a human-computer interaction approach
Published 20-09-2022
Keywords
- Autonomous Vehicles,
- Cybersecurity,
- Human-Computer Interaction
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Autonomous Vehicles (AVs) represent a transformative technology poised to revolutionize transportation. However, ensuring their cybersecurity is paramount to prevent malicious attacks that could endanger lives. While much attention has been given to technical aspects of AV cybersecurity, human factors play a crucial yet understudied role. This research paper explores human factors in AV cybersecurity, focusing on the human-computer interaction (HCI) perspective. By understanding how humans interact with AV cybersecurity systems, we aim to enhance the design and implementation of effective cybersecurity measures. This paper reviews existing literature, identifies key human factors, and proposes HCI-based strategies to mitigate cybersecurity risks in Avs.
Downloads
References
- Tatineni, Sumanth. "Cost Optimization Strategies for Navigating the Economics of AWS Cloud Services." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.6 (2019): 827-842.
- Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.
- Mahammad Shaik, et al. “Envisioning Secure and Scalable Network Access Control: A Framework for Mitigating Device Heterogeneity and Network Complexity in Large-Scale Internet-of-Things (IoT) Deployments”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 1-24, https://dlabi.org/index.php/journal/article/view/1.
- Tatineni, Sumanth. "Deep Learning for Natural Language Processing in Low-Resource Languages." International Journal of Advanced Research in Engineering and Technology (IJARET) 11.5 (2020): 1301-1311.