## 5 Conclusions and Future Research
### Future Research  
In the future, we plan to work on further analysis by adopting deep learning-based advanced persistent threats (APT) to detect and protect the network. The future recommendations also adopt more accurate results prediction analysis and provide a brief justification for the results and outcomes. Other possible future research directions on real-time malicious node detection using machine learning are:  
* **Developing novel machine learning algorithms for malicious node detection:** Researchers can develop novel machine learning algorithms specifically designed for VANETs. These algorithms can be optimized for real-time processing and high accuracy.  
Figure 20: Comparison of the proposed model (IVSDDM) with previous research works [41; 42; 43; 44].  
* **Improving feature selection:** Feature selection is a critical step in the machine learning process. Researchers can investigate which features are most relevant for detecting malicious nodes in VANETs. They can also explore new features that have not been previously used for this purpose.
* **Analyzing the impact of different attacks on malicious node detection:** Various attacks can be launched on VANETs, such as jamming, impersonation, and denial-of-service attacks. Researchers can analyze the impact of different attacks on the performance of machine learning algorithms for malicious node detection.
* **Designing a hybrid approach:** A hybrid approach that combines multiple machine learning algorithms can be developed to improve the accuracy of malicious node detection. For example, an ensemble of algorithms such as decision trees, neural networks, and support vector machines can be used to detect malicious nodes.
* **Investigating the trade-off between detection accuracy and computational complexity:** Real-time processing is crucial for malicious node detection in VANETs. However, processing large amounts of data in real time can be computationally complex. Researchers can investigate the trade-off between detection accuracy and computational complexity to develop a system with high accuracy and low computational overhead.
* **Evaluating the system's robustness to different network scenarios:** VANETs are subject to different network scenarios, such as varying traffic densities, topologies, and mobility patterns. Researchers can evaluate the robustness of the proposed system in these different scenarios to ensure that it works effectively in different environments.
* **Testing the proposed system in a real-world environment:** The proposed system should be tested in a real-world environment to evaluate its effectiveness in detecting malicious nodes in VANETs. This can involve setting up a testbed and collecting data from real vehicles to evaluate the system's real-time accuracy and efficiency.  
Conceptualization, F.J.; data curation, K.R., A.A., R.A., Y.S. and A.M.; formal analysis, K.R., Y.S., A.A., A.M. and F.J.; funding acquisition, A.A., M.R., A.F.J. and Y.S.; investigation, A.A.; methodology, Y.S., Y.S., R.A. and A.M.; project administration, K.R. and R.A.; resources, R.A., A.M. and Y.S.; software, Y.S. and F.J.; visualization, A.A. and A.M.; writing--original draft, K.R. and F.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Norwegian University of Science and Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge the support of the Norwegian University of Science and Technology for paying the Article Processing Charges (APC) of this publication. Special acknowledgment to [PERSON] Nourah [PERSON] Abdulrahman University Researchers Supporting Project number (PNURSP2023R323), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. [PERSON] was in part supported through an ERCIM Fellowship.  
The authors declare no conflict of interest.