# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 8 Conclusions  
While V2V networks have the potential to improve driving safety, misbehaving vehicles aim to disrupt their communication reliability, particularly in disconnected rural areas with limited infrastructure. Existing systems like SCMS depend on concepts related to MAs and CRLs, which are inadequate in such scenarios. This paper has presented a novel scheme for accurately detecting and classifying misbehavior in V2V networks.  
DRAMBR identifies and mitigates misbehavior by using LMDM, which uses local observations and neighboring feedback in offline settings. These observations are consolidated upon connectivity as reports to be submitted to a centralized RS. The remainder of DRAMBR concerns storing, reporting, aggregating, and integrating these reports using existing classification techniques. DRAMBR classification is a multi-stage process, starting with DBSCAN to classify whether multiple reports concern the same event, the iForest to analyze the anomaly, and then Gaussian Mixture Models for probabilistic classification of malicious versus honest behavior. Additionally, Random Forest and XGBoost models are combined to improve decision accuracy.  
Our findings demonstrate the effectiveness of the DRAMBR in reducing false reporting which improves decision accuracy, ensures reliable detection of misbehavior, and supports the RS's ability to maintain system integrity. Adopting the proposed system results in  
Figure 19: Accuracy, precision, recall, and F1-score across scenarios and overall system.  
enhanced V2V communication reliability, and ensuring a safer network in infrastructure-limited environments.  
While this demonstrates a reasonable level of reliability in distinguishing between honest, malicious, and erroneous reporters, there remains room for improvement. For example, when up to half of the vehicles are malicious or traffic conditions are highly dynamic, DRAMBR's robustness will be affected. Future work will focus on enhancing feature engineering, addressing any class imbalances, and fine-tuning model hyperparameters to further increase the classification performance and ensure more precise identification of reporter types.  
Conceptualization, D.A., T.M. and S.F.; Methodology, D.A., T.M. and S.F.; Formal analysis, D.A.; Investigation, D.A.; Data curation, D.A.; Writing--original draft, D.A.; Writing--review & editing, T.M. and S.F.; Visualization, D.A.; Supervision, T.M. and S.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be shared upon request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this paper:  
CA Certificate Authority  
CRL Certification Revocation List  
DBSCAN Density Based Spatial Clustering of Applications with Noise  
DSRC Dedicated Short-Range Communication  
ETSI European Telecommunications Standards Institute  
ITS Intelligent Transport Systems  
LA Linkage Authorities  
LMDM Local Misbehavior Detection Mechanism  
MA Misbehavior Authority  
MR Misbehavior Report  
OBUs On-Board Units  
OE Offline Evaluation  
OR Online Reporting  
PCA Pseudonym Certificate Authority  
PCs Pseudonym Certificates  
RA Registration Authority  
RS Reputation Server  
RSUs Roadside Units  
RV Reputation Value  
SCMS Security Credential Management System  
SUMO Simulation of Urban MObility  
VANETs Vehicular Ad hoc Networks  
V2I Vehicle-to-Infrastructure  
V2V Vehicle-to-Vehicle