Cognitive Cybersecurity Frameworks for Autonomous Vehicles - Adapting to Emerging Threats: Develops cognitive cybersecurity frameworks for AVs to adapt to emerging cyber threats in real-time
Published 14-05-2023
Keywords
- Autonomous Vehicles,
- Cybersecurity,
- Cognitive Computing
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Autonomous Vehicles (AVs) are at the forefront of technological advancement, promising a future of safer and more efficient transportation. However, with this innovation comes the critical need to address cybersecurity challenges. Traditional cybersecurity approaches are often insufficient due to the dynamic and complex nature of AV systems. This research paper presents a novel approach: Cognitive Cybersecurity Frameworks (CCFs) for AVs. These frameworks leverage cognitive computing capabilities to adapt to emerging threats in real-time, enhancing the security and resilience of AVs. The paper discusses the design principles, implementation strategies, and potential benefits of CCFs in securing AVs against cyber threats.
Downloads
References
- Tatineni, Sumanth. "Federated Learning for Privacy-Preserving Data Analysis: Applications and Challenges." International Journal of Computer Engineering and Technology 9.6 (2018).
- Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.
- 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.