Firm-Vehicle: Trusted Communication Enabled Instruction Embedding Model for Resource-Constrained VANET Environments

Author:

Chen RuiORCID,Younas Waleed,Zhao Jing

Publisher

Springer Nature Singapore

Reference28 articles.

1. Chen, H., Liu, J., Wang, J., et al.: Towards secure intra-vehicle communications in 5G advanced and beyond: vulnerabilities, attacks and countermeasures. Veh. Commun. 39, 100548 (2023)

2. Chua, Z.L., Shen, S., Saxena, P., et al.: Neural nets can learn function type signatures from binaries. In: 26th USENIX Security Symposium (USENIX Security 17), pp. 99–116 (2017)

3. Dalvi, F., Sajjad, H., Durrani, N., et al.: Analyzing redundancy in pretrained transformer models. http://arXiv.org/abs/2004.04010 (2020)

4. Devlin, J., Chang, M., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805. http://arXiv.org/abs/1810.04805 (2018)

5. Ding, S.H., Fung, B.C., Charland, P.: Asm2vec: boosting static representation robustness for binary clone search against code obfuscation and compiler optimization. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 472–489. IEEE (2019)

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