Enhanced Dragonfly-Based Secure Intelligent Vehicular System in Fog via Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1435-7_57
Reference15 articles.
1. Devi A, Kait R, Ranga V (2019) Security challenges in fog computing. In: Handbook of research on the IoT, cloud computing, and wireless network optimization. IGI Global, pp 148–164. https://doi.org/10.4018/978_1_5225_7335_7.ch008
2. Aadil F, Ahsan W, Rehman ZU, Shah PA, Rho S, Mehmood I (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74(9):4542–4567. https://doi.org/10.1007/s11227_018_2305_x
3. Chen JIZ, Hengjinda P (2021) Enhanced dragonfly algorithm based K-medoid clustering model for VANET. J ISMAC 3(01):50–59. https://doi.org/10.36548/jismac.2021.1.005
4. Abdussami AA (2021) Incremental deep neural network intrusion detection in fog based IoT environment: an optimization assisted framework. Indian J Comput Sci Eng 12(6):1847–1859. https://doi.org/10.21817/indjcse/2021/v12i6/211206191
5. Fei L, Jiayan Z, Jiaqi S, Szczerbicki E (2020) Deep learning-based intrusion system for vehicular ad-hoc networks. CMC-Comput Mater Contin 65:653–681. https://doi.org/10.32604/cmc.2020.011264
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