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pro vyhledávání: '"Pese, Mert D."'
In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle network (IVN) security is paramount. This paper introduces an advanced intrusion detection system (IDS) called KD-XVAE that uses a Variational Autoencoder (VAE)-based knowle
Externí odkaz:
http://arxiv.org/abs/2410.09043
Autor:
MohajerAnsari, Pedram, Domeke, Alkim, de Voor, Jan, Mitra, Arkajyoti, Johnson, Grace, Salarpour, Amir, Olufowobi, Habeeb, Hamad, Mohammad, Pesé, Mert D.
Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount importance is the study of physical-world adversarial examples (AEs) aimed at exploiting vulnerabilities in perception systems. However, most of the prevailing
Externí odkaz:
http://arxiv.org/abs/2409.18248
Modern vehicles have become sophisticated computation and sensor systems, as evidenced by advanced driver assistance systems, in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various embe
Externí odkaz:
http://arxiv.org/abs/2409.15561
Autor:
Curran, Noah T., Cho, Minkyoung, Feng, Ryan, Liu, Liangkai, Tang, Brian Jay, MohajerAnsari, Pedram, Domeke, Alkim, Pesé, Mert D., Shin, Kang G.
In the current landscape of autonomous vehicle (AV) safety and security research, there are multiple isolated problems being tackled by the community at large. Due to the lack of common evaluation criteria, several important research questions are at
Externí odkaz:
http://arxiv.org/abs/2409.03899
Cameras are crucial sensors for autonomous vehicles. They capture images that are essential for many safety-critical tasks, including perception. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipel
Externí odkaz:
http://arxiv.org/abs/2409.01234