Review Article
Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications
Table 3
Summary for secure IoV communications.
| Year | Source | Approaches | Features | Advantages | Challenges | Citations |
| 2020 | IEEE | Fog-based identity authentication (FBIA) | Fog-based identity authentication scheme and deep learning | IoV real-time security monitoring | Dual authentication levels for access authentication and vehicles’ timing detection | Song et al. [86] | 2020 | IEEE | SVM-based classifier | Authentication scheme based on SVM | Secure access frequencies and progressive protection of trusted communications | Fast authentication mechanism for large-scale IoV | Hasan et al. [83] | 2018 | IEEE | Certificateless Short Signature Scheme (CLSS) and ML | Anonymous authentication scheme-based ML | Secure communication between vehicles and roadside units | Security under adaptively chosen message and ID attacks | Liu et al. [80] | 2017 | IEEE | Aggregate privacy-preserving authentication protocol; Multiplicative Secret Sharing (MSS) technique | Distributed aggregate signature mechanism | Secure vehicular network authentication and trusted authority | Trade-off between security and storage resource management | Memon et al. [81] | 2016 | Elsevier | Smart Adaptive Data Aggregation (SADA); machine learning-based data fusion and analysis | Adaptive data aggregation-based ML | Secure data exchange between vehicles | Fully automated switching to unknown vehicle | Islam et al. [82] |
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