# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 3 Related Works
Another study [25] proposed an innovative clustering algorithm (CH election algorithm) and an intrusion detection framework based on game theory. By establishing stable vehicular clusters with greater communication among member vehicles, the proposed clustering technique maintains the IDS framework's stability. After that, strategies for electing the CH and a collection of agent nodes for each cluster are used. In order to conduct the intrusion detection operation in a distributed manner, the proposed IDS architecture employs agent nodes, CHs, and RSUs operating at three distinct levels of the vehicular network. In [26], researchers introduced a new algorithm consisting of three stages for detecting black hole. In the first stage, RSU serves as a certificate authority (CA) which keeps and provides a public and private key as well as certificates for the vehicles. Before launching any communication, vehicles must be approved by the RSU. The source transmits afterward the RREQ along with the correct certificate, nonce encryption, and the destination's public key. Authors of [27] implemented a modified version of the AODV routing protocol named SVODR to detect and mitigate the black hole attack in VANETs. They introduced a modified structure of both the RREQ and the RREP control packets by incorporating an Encrypted Random Number to the source and destination. A legitimate destination node is tested using the destination vehicle packet sequence number and the function (encryption/decryption) performed by the source or destination vehicle. Researchers tested the efficiency of their solution by conducting a comparison study with IDS in VANET. In Table 1, we summarize various proposed detection techniques with regard to black hole attack. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author** & **Detection** & **Content** & **Simulator** & **Performance** & **Routing** & **Data** & **Multivariate** & **Limitations** & \\
**Year** & **Technique** & **Technique** & **Parameters** & **Parameters** & **Products** & **Rescaling** & **Analysis** & **Analysis** & **Limitations** \\ \hline [PERSON] & Improved ERQE and & & & drop packets, end-to-end delay packet delivery ratio & & & & More overhead caused by the & \\  & & RBF routing packets & & & delay, packet delivery ratio & & & & & \\  & & & (PD), routing request & & & & & & \\  & & & & overhead & & & & \\ \hline [PERSON] et al., 2020 [20] & Intelligent block hole & & sequence number, base count, & & & & & The computation of four thresholds & \\  & attack detection scheme & & packet delivery ratio and end & no & no & no & no & simultaneously leads to higher \\  & (IDBA) & & & to or delay & & & & processing the and additional & \\ \hline [PERSON] et al., 2019 [21] & The CDS approach and & & & detection rate, packet delivery ratio, throughput, & no & no & no & & Thoughboard and packet delivery \\  & IDS nodes & & & routing overhead, delay & & & & & \\ \hline [PERSON] et al., 2019 [22] & Adaptive Fuzzy Neuro & & & & & & & & \\  & Inference System with & & & & & & & \\  & Particle Swarm & & & & & & & \\  & Optimization (PSO) & & & & & & & \\ \hline [PERSON] et al [based] & & & & & & & & \\ [PERSON], 2019 [22] & Heuristic approach & & & & throughput, packet loss rate, & & & & \\  & & & & end-to-end delay & yes & no & no & no & \\  & & & & & & & & & \\ \hline [PERSON] et al., 2019 [24] & Novel Integrated Cross & & & & communication efficiency & & & & \\  & Inference (IC) & & & ratio, average throughput, & & & & \\  & & & & delay, average throughput, & & & & \\  & & & & destination utilization index, & & & & \\  & & & & neighbor utilization index & & & & \\ \hline [PERSON] et al., 2018 [25] & A game theory based & & & & & & & \\  & multi layered intrusion detection framework & & & & & & \\  & & & & & rate & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Proposed techniques in literature to detect black hole attack.