# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 6 Conclusions  
In this work, we presented an innovative protocol for preventing Jellyfish attacks in V2X, called secure and efficient routing protocol for Jellyfish attack prevention in vehicle-to-everything networks (SecE-V2X). The developed mechanism ensures secure routing using trusted nodes and offers the successful prevention of different Jellyfish attack categories (Jellyfish reorder attacks, Jellyfish periodic drop attacks, and Jellyfish delay variance attacks) with a single solution, despite the fact that no protocol can satisfy all of the requirements of a secure system. In the SecE-V2X technique, the communication behavior between nodes, which is expressed in terms of the packet loss rate, delay, packet reordering, and confidence of a node, is used to compute the overall Honesty value of a node. By choosing the trusted next forwarding node with the highest Honesty value from a current node's neighbors, SecE-V2X ensures secure routing without attacker involvement. With OMNET++ and SUMO simulators, we conducted simulations in an urban environment. In networks of 100 nodes, our protocol was tested against all three Jellyfish attack types. The performance result is compared with two secured routing approaches--[37], 2017, and [20], 2018--in terms of three performance metrics: the packet delivery ratio, end-to-end delay, and throughput. In a scenario without attacks, the results state that SecE-V2X is better in terms of the PDR, with almost 1.54% of [20], 2018, and 3.74% of [37], 2017. SecE-V2X is also better in terms of end-to-end delay (almost 0.31 ms with regard to [20], 2018, and 1.84 ms with regard to [37], 2017). The proposed SecE-V2X maintains a better throughput of almost 41.82 packets/s with regard to [20], 2018, and 103.6 packets/s with regard to [37], 2017. In the presence of six Jellyfish attacker nodes, the results demonstrate that SecE-V2X is better in terms of the PDR, with a PDR of almost 90.50% compared to 79.18% of [20], 2018, and 68.73% of [37], 2017. With a ratio of 8.71 ms, SecE-V2X, with the lowest delay, outperforms [37], 2017 (17.21 ms), and [20], 2018 (11.48 ms), in terms of end-to-end delay. In terms of throughput, SecE-V2X retains the best throughput of 556.02 packets/s, whereas [20], 2018, has a throughput of 472.08 packets/s and [37], 2017, has a throughput of 377.43 packets/s. The results demonstrate that SecE-V2X maintains better performance than that of the competing approaches and works effectively even in the presence of Jellyfish nodes.  
With regard to future work, we plan to evaluate and validate our work on top of a realistic dataset of vehicles' mobility.  
Figure 18: Nodes’ data throughput under Jellyfish reordering attacks [20; 37].  
**Author Contributions:** The major contributions of all the authors are summarized as: Conceptualization, M.B.; methodology, B.B. and N.A.; software, M.B.; validation, A.K. and B.B.; formal analysis, M.B.; investigation, A.K. and B.B.; resources, N.A.; data curation, M.B. and A.K.; writing--original draft preparation, M.B., A.K., B.B. and N.A.; writing--review and editing, M.B. and N.A.; visualization, M.B.; supervision, A.K.; project administration, A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no potential conflict of interests.