# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 3 Related Literature  
The primary purpose of a secure vehicular ad hoc network is to provide secure and successful data transmission between sources and destinations. It is necessary to develop a security mechanism that can make a network resistant to different attacks in order to perform the network efficiently. Attacks such as Wormhole, Blackhole, and Sybil attacks seem to be very easily recognizable because the attacker node does not employ any protocol instructions and disrupts the routing protocol's operating conditions by inserting bogus data, modifying information, and removing data in control packets [27; 28]. Attacks such as Jellyfish attacks, on the other hand, are more difficult to detect since they respect the variety of protocol standards and disrupt normal network operations [29]. Figure 3: Jellyfish delay variance attack. Figure 2: Jellyfish periodic drop attack. Simulation research examining the impact of Jellyfish attacks clearly reveals that they have a significant influence on throughput and end-to-end delay [30]. To detect and mitigate the malicious activities of different Jellyfish attack variations, the research community has put significant effort into developing detection and countermeasure approaches, but so far no approach has been developed to protect against all three types of Jellyfish attacks together. A proposed solution is to identify Jellyfish attacks by using a timer to measure delay and then finding a different route as well as blocking the attacker node in order to avoid delay variance attacks and improve throughput [31]. A modified hash function and time-space cryptography are also used to determine whether packet reordering is being carried out as a result of a Jellyfish attack, and, if so, to prevent the attack and improve network performance [32]. Other strategies, based on trust and reputation, were considered effective security strategies for open environments such as MANETs [26; 33]. To solve DoS/distributed denial of service (DDoS) attacks, a trust-based approach that depends on the history and profile of attacks is used to provide a solution for vehicular ad hoc network (VANET) security by [PERSON] et al. [34]. The packet delivery ratio, delay, average latency, and energy usage are all metrics that are taken into account. According to their acquired data, it achieved a detection rate of 95.8%, an average delay of 30 s, and a packet delivery ratio of 86%. The authors in [35] are developing another trust-based collaborative intrusion detection system. This approach is also designed to protect availability services from DoS/DDoS attacks. In such trust-based situations, sophisticated attacks can still be launched. Machine learning is a well-established approach for VANETs that has been proven to be particularly useful for attack prediction and analysis. Denial of service (DoS) and its variants are commonly prevented through the use of machine learning and deep learning techniques. The detection of DDoS attacks has been presented in [36] through the use of an efficient hybrid approach that is based on the support vector machine (SVM) kernel. Jitters, packet drops, collisions, and other features are employed in the simulation to generate various forms of data in modeling a real-time situation that contains characteristics of both regular and DDoS attacks. The suggested algorithm is used to evaluate the performance of the hybrid model in order to determine how well it can distinguish between regular communications and DDoS attacks. The authors of [37] used an intrusion detection system (IDS) algorithm to prevent ad hoc on-demand distance vector (AODV) routing protocols against Jellyfish and Blackhole attacks in VANETs. They compare the conditions without attacks to those with Blackhole and Jellyfish attacks when analyzing the AODV routing protocol with and without an IDS algorithm.