# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
## 1 Introduction
### Contributions and Organization  
To address the security vulnerability posed by blackhole attacks in VANETs, we propose a deep-learning-based secure routing (DLSR) protocol to avoid blackhole attacks. In addition, we also propose a deep-learning-based clustering (DLC) protocol to enhance \begin{table}
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**Authors** & **Clustering** & **Routing** & **Attack Detection** & **Remarks** \\ \hline [PERSON] et al. (2018) & LEACH & - & - & The cluster head is randomly chosen, regardless of their energy levels, affecting network longevity \\ \hline [PERSON] et al. (2019) & Highest-degree-based clustering & - & Consistent clustering algorithm to classify suspicious nodes & Does not consider other important metrics such as energy levels, which can impact the network’s overall performance \\ \hline [PERSON] et al. (2019) & Lowest-ID Algorithm & - & - & Does not take into account any metric other than the ID itself \\ \hline [PERSON] et al. (2019) & PSO & PSO & - & The iteration-based algorithm spends so much time finding the optimal cost \\ \hline [PERSON] et al. (2019) & Optimized Fuzzy Clustering Algorithm & PSO & - & The iteration-based algorithm spends so much time finding the optimal cost \\ \hline [PERSON] et al. (2019) & Genetic Algorithm & - & - & The iteration-based algorithm spends so much time finding the optimal cost \\ \hline [PERSON] et al. (2019) & - & mAODV & Intrusion detection system for blackhole attack & The false-positive detection in a harsh environment may occur \\ \hline [PERSON] et al. (2019) & - & D\&PMV & Distrust value for malicious node detection and prevention. \\ \hline [PERSON] et al. (2019) & - & AODV, ZRP & Encrypted random number for blackhole attack detection & Additional fields in the control packets for cryptography algorithms lead to substantial routing overhead and increased end-to-end delay \\ \hline [PERSON] et al. (2019) & - & - & SSAE and softmax classifier deep network schemes for DDo attack detection & Failed to improve the security of SDN-based VANETs using hardware application with limited resources \\ \hline [PERSON] et al. (2019) & - & - & SVM using three ML algorithms (GA, ACO, and the PSO) for classifying intrusion & Failed to include large amounts of data obtained via vehicular communication for SVM training \\ \hline [PERSON] et al. (2019) & Modified K-harmonic means clustering & - & CMEHA-DNN for identifying Sybil attack & Unable to identify different types of attacks in VANETs \\ \hline The proposed approach & DLC & DLSR & DNN for blackhole attacks detection & Using DL in clustering, routing, and blackhole attacks detection improves network security and efficiency by delivering accurate results and very different from conventional optimization methods that involve iteration processes \\ \hline \hline \end{tabular}
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Table 1: Summarized literature review. route connectivity and reduce control overhead. One of the key features of our proposed routing protocol is its ability to adaptively choose the flexibility between secure routing and normal routing based on network security conditions. This flexibility allows the protocol to maintain a balance between security and routing performance, ensuring that the network remains both safe from blackhole attacks and efficient in terms of performance. The main contribution of this paper can be summarized as follows:  
* We propose the DLSR protocol to avoid blackhole attacks in VANETs. The proposed DLSR protocol utilizes DL on each node to select secure routing or normal routing depending on network security conditions to maintain a balance between security and routing performance. More specifically, DL is used to identify any suspicious behavior of a node and to determine the best possible next hop based on its fitness function value. * We propose the DLC protocol as the underlying structure to enhance route connectivity and reduce control overhead in VANETs.