Abstract
Given the deficiencies in human-focused cybersecurity research, the authors set out to explore the usability of a subset of training requirements in the autonomous vehicle domain. This paper focuses on the question: Can trained operators perform their duties according to the security design standards when exposed to cybersecurity threats according to situational intelligence? The training assessment program that addresses this question is referred to as a usability evaluation, which measures the data's quality in an inspection process. The research examines physical security, cybersecurity, and other high-level applications in light of situational reinforcements for identifying suspicious behaviors. Furthermore, the full design specifications and training sequences are used to help participants perform their duties. The evaluation results determine the impact of training when participants perform a set task simulation. The paper thus answers the research question. Guidelines and recommendations that are suitable for the 19 tasks are then presented.
References
V. Švábenský, J. Vykopal, P. Čeleda, K. Tkáčik et al., "Student assessment in cybersecurity training automated by pattern mining and clustering," 2022. ncbi.nlm.nih.gov
I. Pekaric, C. Sauerwein, and M. Felderer, "Applying Security Testing Techniques to Automotive Engineering," 2023. [PDF]
Q. Song, E. Engström, and P. Runeson, "Concepts in Testing of Autonomous Systems: Academic Literature and Industry Practice," 2021. [PDF]
H. W. Alomari, V. Ramasamy, J. D. Kiper, and G. Potvin, "A User Interface (UI) and User eXperience (UX) evaluation framework for cyberlearning environments in computer science and software engineering education," 2020. ncbi.nlm.nih.gov
R. Ošlejšek, V. Rusňák, K. Burská, V. Švábenský et al., "Conceptual Model of Visual Analytics for Hands-on Cybersecurity Training," 2020. [PDF]
W. Morales Alvarez, N. Smirnov, E. Matthes, and C. Olaverri-Monreal, "Vehicle Automation Field Test: Impact on Driver Behavior and Trust," 2020. [PDF]
S. Lee, Y. Cho, and B. C. Min, "Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems," 2017. [PDF]
P. Xiong, S. Buffett, S. Iqbal, P. Lamontagne et al., "Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective," 2021. [PDF]
Tatineni, Sumanth. "Beyond Accuracy: Understanding Model Performance on SQuAD 2.0 Challenges." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.1 (2019): 566-581.
Venkataramanan, Srinivasan, Ashok Kumar Reddy Sadhu, and Mahammad Shaik. "Fortifying The Edge: A Multi-Pronged Strategy To Thwart Privacy And Security Threats In Network Access Management For Resource-Constrained And Disparate Internet Of Things (IOT) Devices." Asian Journal of Multidisciplinary Research & Review 1.1 (2020): 97-125.
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.
P. Theodorou, K. Tsiligkos, A. Meliones, and C. Filios, "A Training Smartphone Application for the Simulation of Outdoor Blind Pedestrian Navigation: Usability, UX Evaluation, Sentiment Analysis," 2022. ncbi.nlm.nih.gov
K. Dočkalová Burská, V. Rusňák, and R. Ošlejšek, "Data-driven insight into the puzzle-based cybersecurity training," 2021. [PDF]
A. Shah, "Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms," 2023. [PDF]
G. Pappas, J. E. Siegel, J. Rutkowski, and A. Schaaf, "Game and Simulation Design for Studying Pedestrian-Automated Vehicle Interactions," 2021. [PDF]
V. Linkov, P. Zámečník, D. Havlíčková, and C. W. Pai, "Human Factors in the Cybersecurity of Autonomous Vehicles: Trends in Current Research," 2019. ncbi.nlm.nih.gov
P. McDaniel and F. Koushanfar, "Secure and Trustworthy Computing 2.0 Vision Statement," 2023. [PDF]
M. Ebnali, R. Lamb, and R. Fathi, "Familiarization tours for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity," 2020. [PDF]
S. Nordhoff, "A conceptual framework for automation disengagements," 2024. ncbi.nlm.nih.gov