Abstract
With the proliferation of connectivity and smart computing in vehicles, there has been significant recent work on developing connected autonomous vehicle (CAV) applications. Unfortunately, connectivity brings in the possibility of new security attacks that target subversion of vehicular applications by compromising sensors and communication. Consequently, adoption of these applications depends crucially on our ability to develop resiliency technologies to protect against them. In this chapter, we discuss an approach, called ReDeM that enables development of real-time resiliency in CAV application through machine learning (ML) techniques. We discuss some unique challenges in application of ML in this domain, and our approaches to address such challenges.
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Notes
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When doing this, care has to be taken so that we are still considering an adversary against whom it is possible to have a viable defense, e.g., if the adversary can collusively corrupt all the perception channels of the ego vehicle it is easy to see that no resiliency solution is possible.
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Boddupalli, S., Owoputi, R., Duan, C., Choudhury, T., Ray, S. (2023). Machine Learning for Security Resiliency in Connected Vehicle Applications. In: Kukkala, V.K., Pasricha, S. (eds) Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-28016-0_16
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