VANET Security Optimization with Blowfish Algorithm and Adversarial Transfer Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7077-3_23
Reference13 articles.
1. Almomani I, Ahmed M, Kosmanos D, Alkhayer A, Maglaras L (2022) An efficient localization and avoidance method of jammers in vehicular ad hoc networks. IEEE Access 10:131640–131655. https://doi.org/10.1109/ACCESS.2022.3229623
2. Boucetta SI, Johanyák ZC (2022) Survey on security attacks in software defined VANETs. In: 2022 IEEE 16th international symposium on applied computational intelligence and informatics (SACI), Timisoara, Romania, pp 000185–000190. https://doi.org/10.1109/SACI55618.2022.9919595
3. Corona I, Giacinto G, Roli F (2013) Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues. Inf Sci 239:201–25. https://doi.org/10.1016/j.ins.2013.03.022, ISSN 0020-0255
4. Debicha I, Bauwens R, Debatty T, Dricot JM, Kenaza T, Mees W (2023) TAD: transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems. Fut Gener Comput Syst 138:185–197. https://doi.org/10.1016/j.future.2022.08.011, ISSN 0167-739X
5. Funderburg LE, Ren H, Lee I-Y (2021) Pairing-free signatures with insider- attack resistance for vehicular ad-hoc networks (VANETs). IEEE Access 9:159587–159597. https://doi.org/10.1109/ACCESS.2021.3131189
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