# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 4 Security Solutions
### Hybrid Solutions for VANETs  
Hybrid solutions use a combination of techniques to handle various vehicular attacks. Some of these attack solutions are provided in Table 8. Plausibility validation network is shown in [51]. Five sets of rules are used. Entropy-based solution for Distributed Denial of Service (DDoS) detection is shown in [60]. It uses data type and flag values for feature extraction. A secure genetic-based framework for VANETs is researched in [47]. It claims for 86.54% of accuracy in simulation. A mathematical model for security features for VANETs has been derived with features of lost packets, total busy time, total lost packets, etc. [124]. Attack vehicle detection is 100% accurate as claimed by the authors. A non-learning based method with drop-in packets feature is shown in [49]. With various types of vehicles and EV infrastructure, the VANETs are becoming heterogeneous in nature. A solution for such a network is shown based on communication framework with the hybrid information exchange [125]. Its uses waiting time, packet delivery ratio, and overhead analysis for the security measurements. All these algorithms are significant in VANETs, but having some limitations in their applicability or validation. A summarization of such problems and other security features are listed in Table 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference(s)** & **Year** & **Service** & **Attack Handling** & **Research Gap** \\ \hline [PERSON] et al. [108] & 2011 & Availability & DoS/DDoS & Not applicable for temporal attacks in a realistic scenario \\ \hline [PERSON] et al. [111] & 2015 & Availability & DoS/DDoS & Validation not successful \\ \hline [PERSON] et al. [115] & 2017 & Availability & DoS/DDoS & Validation is done without considering attacks of DoS/DDoS \\ \hline [PERSON] et al. [112] & 2017 & Availability & DoS/DDoS & Suitable only for software-defined VANET \\ \hline [PERSON] et al. [113] & 2018 & Availability & DoS/DDoS & Suitable only for software-defined VANET \\ \hline [PERSON] and Argyriou [76] & 2018 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [116] & 2018 & Availability & DoS/DDoS & Increased computational overheads \\ \hline [PERSON] et al. [109] & 2019 & Availability & DoS/DDoS & Suitable for electric vehicles \\ \hline [PERSON] et al. [117] & 2019 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [118] & 2019 & Availability & DoS/DDoS & The dataset is not VANET-based \\ \hline [PERSON] et al. [119] & 2019 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [120] & 2019 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [122] & 2020 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [121] & 2020 & Availability & DoS/DDoS & Non-reputational and non-trustable \\ \hline [PERSON] et al. [123] & 2020 & Availability & DoS/DDoS & Accuracy rate can be increased \\ \hline [PERSON] et al. [114] & 2020 & Availability & DoS/DDoS & Parametric evaluation not validated \\ \hline [PERSON] et al. [110] & 2020 & Availability & DoS/DDoS & Non-reputation and non-trustable \\ \hline \hline \end{tabular}
\end{table} 
Table 7: List of machine learning and deep learning-based solutions for VANETs