## 6 Conclusions and Future Work  
This paper provides a voting-based ensemble learning (VEL) and gossip learning (GL) framework for Mode 3 and Mode 4 of the V2X communication environment to detect DoS attacks on AVs using the UNSW-NB15 dataset [42]. This study contributes to the detection of DoS attacks in autonomous vehicles (AVs) by offering two distinct approaches, considering both infrastructure-based (Mode 3) and infrastructureless (Mode 4) scenarios. Unlike most existing studies, which typically focus on a single environment, this research addresses both. For Mode 3, a VEL mechanism is proposed, combining multiple classifiers to enhance detection accuracy. In contrast, for Mode 4, a graph-learning (GL) approach is employed, which is well-suited for AVs' decentralized communication in the absence of infrastructure. The proposed scheme's performance is evaluated using the University of South Wales's UNSW-NB15 dataset, from the intelligent security group. For both schemes, the proposed VEL and GL schemes, initially, the local models are trained using multiple classifiers such as ANN, LSVM, and CGSVM. In VEL, the trained model predictions are shared with the RSU, which performs voting on the received prediction outcomes and decides the ultimate output based on the majority vote. This mechanism helps to mitigate any possible bias or variance in the prediction of any individual classifier prediction, resulting in a boost in prediction performance. Then RSU shares the prediction model with the associated AVs. Since AVs also experience many situations where communication infrastructure is not available, in these cases, AVs communicate with each other. For such a situation, we provide a gossip learning framework, in which AVs communicate with each other and share their trained models, and the AVs, by themselves, decide to keep or discard a specific trained mode. This study presents a significant advancement in the detection of various cyber-attacks, particularly denial of service (DoS) attacks, within intelligent transportation systems (ITS). Its reliability is demonstrated by a high detection rate. Moreover, this study is closely aligned with real-world scenarios by accounting for the environmental conditions experienced by autonomous vehicles (AVs). Given that DoS attacks are among the most critical types of cyber threats, the findings of this research could also be applicable to the detection of other forms of cyber attacks. In the future, we intend to find a sophisticated ensemble method that dynamically adjusts to the environment, data distribution, and number of entities participating in the ensemble process. We also want to make ensemble methods more aligned with specific situations, where systems are required to handle big data, to enhance the scalability of ensemble learning. Ensemble learning can provide a robust interpretable mechanism, especially for critical situations, such as healthcare, disaster management, and finance. We \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Refs. No** & **Article** & **Algorithm** & **Classification Accuracy** \\ \hline
[32] & [PERSON] et al. (2024) & Federated learning & 98.51\% \\
[36] & [PERSON] et al. (2018) & SVM & 95.24\% \\
[43] & [PERSON] et al. (2021) & ML + statistical methods & 97.65\% \\
[44] & [PERSON] et al. (2020) & SVM + GA & 98.03\% \\
[45] & [PERSON] et al. (2019) & ACO & 97.4\% \\
[46] & [PERSON] et al. (2021) & Entropy & 98.2\% \\
[47] & [PERSON] et. al. (2021) & GAN & 94.38\% \\
[48] & [PERSON] et al. (2020) & Factorization machine & 95.80\% \\
[49] & [PERSON]. (2022) & DFL & 97\% \\
**Our** & **GL** & **GL** & **98.82\%** \\
**Our** & VEL & **VEL** & **99.16\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with existing approaches. also plan to carry out an in-depth investigation of the bias mitigation strategies within ensemble learning for various cybersecurity situations. Conceptualization, M.N.A. and M.I. ; methodology, M.N.A., M.I., H.-Y.K. and B.-S.K. ; validation, M.N.A., I.U., G.M.R. and B.-S.K.