# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 3 Related Literature
The authors created a multidimensional taxonomy of attacks by classifying them according to security services, attack layers, categories of attackers, and types of components targeted. After this, they compared relevant studies on the solution approaches to determine the advantages and disadvantages. Furthermore, [PERSON] and [PERSON] [46] studied and compared the security solutions that are currently in use based on performance standards and actual security measures. The authors also examined whether the properties of the chosen strategies satisfy VANETs' specifications. According to numerous criteria, including detection strategy, attacks covered, simulators utilized, performance metrics, and limitations, in Table 2 we compare the security approaches outlined above. As we can observe, a variety of works have been proposed to deal with Jellyfish attacks and other attacks of a similar nature, such as DDoS, Blackholes, and Rushing. The pre-existing algorithms attempted to address the issue, but there is currently no known method of protecting against all three types of Jellyfish attacks. Therefore, our new protocol offers the successful prevention of various Jellyfish attack categories (Jellyfish reorder attacks, Jellyfish periodic drop attacks, and Jellyfish delay variance attacks) with a single solution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Reference** & **Detection Strategy** & **Attack Covered** & **Network Type** & **Simulator** & **Performance Metrics** & **Approach Limitations** \\ \hline
[32], 2013 & Hash function and time-space cryptography & Jellyfish reordering & Wireless ad hoc & Network simulator 2 & Congestion window, & The attacker node is not identified \\ \hline
[38], 2014 & Genetic algorithm & Jellyfish periodic & MANET & NS-2 &
\begin{tabular}{c} End-to-end delay, energy & The algorithm is not properly described \\  & & dropping & & & & consumption, PDR, and & The accuracy of detection is not mentioned \\  & & & dropping & & throughput & Not compared with existing methods \\ \hline
[39], 2015 & Secure link establishment & Jellyfish reordering & MANET & ExataCyber & PDR, throughput, and end-to-end delay & The attacker node is not identified \\ \hline
[31], 2016 & Time measure & Jellyfish delay variance & MANET & NS-2 &
\begin{tabular}{c} Throughput, end-to-end delay & Not compared with existing methods \\  & & & & delay & & More performance metrics are required \\ \hline
[57], 2017 & Intrusion detection system & Blackhole and jellyfish & VANET & NS-2 & PDR, throughput, and end-to-end delay & Not compared with existing methods \\  & & & & & & & The attacker node is not identified \\  & & & & & & end-to-end delay & Evaluated only for the AODV protocol \\  & & & & & Only jellyfish periodic dropping attacks are considered & \\ \hline
[20], 2018 & Fusion of an authenticated routing-based framework and support vector machine & Jellyfish machine & MANET & NS-2 &
\begin{tabular}{c} Throughput, PDR, dropped packet ratio \\ (DFR), and end-to-end delay & Not tested in the presence of attacks \\ \hline
[41], 2018 & Direct trust-based detection & Jellyfish & MANET & MATLAB &
\begin{tabular}{c} Throughput, PDR, end-to-end delay, detection rate, false positive rate & The attacker node is not identified \\  & & & & & & Not compared with existing methods \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison between detection techniques.