# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 2 Related Work
### RSU-aided detection  
Some misbehavior detection schemes utilize fixed RSUs as cluster heads to manage vehicle clusters. These RSUs group vehicles within their communication range and monitor exchanged information in the area to identify and filter out anomalous data [17,18]. Such detection mechanisms [19,20] primarily address issues like disseminating false position information and Sybil attacks in VANETs. RSUs play a key role in verifying the authenticity of a node's location through a series of checks, including acceptance range validation, maximum speed limit verification, node density evaluation, speed consistency analysis,  
Figure 1: VANETs Environment.  
and time interval confirmation. Additionally, various technologies, such as trajectory tracking and identity monitoring, enhance RSU-based detection mechanisms.  
RSU-aided detection, on the other hand, struggles with the high mobility and random distribution of vehicle nodes, making it challenging to accurately identify anomalous nodes within the fixed coverage area of RSUs.  
Node collaboration detection  
This approach relies on exchanging additional information among neighboring vehicles to detect misbehavior. [PERSON] et al. [21] initially proposed a data-centric method that used proactive neighbor table exchange in node-centric detection. Their position verification strategy combined proactive sharing of neighbor tables with reactive position requests. Vehicles share their neighbor tables and verify whether the reported positions correspond to their observations. Building on this, [PERSON] et al. [22] introduced a statistical model-based system where vehicles calculate and broadcast a flow parameter. This system utilized location data and applied subjective logic for misbehavior detection, enhancing two key processes: the Acceptance Range Threshold (ART) and Proactive Neighbor Exchange.  
Node collaboration detection, which depends on information exchange among neighboring vehicles and lacks the support of reliable infrastructure-based detection, is vulnerable to malicious nodes broadcasting false or manipulated information, ultimately compromising the accuracy of detection mechanisms.  
Based on the previous review, node-centric detection in VANETs faces several critical challenges. First, vehicle networks' high mobility and dynamic nature make it difficult to maintain accurate and reliable trust assessments over time. Second, the random distribution of vehicle nodes adds complexity, as the detection mechanism must adapt to constantly changing network topologies. Third, malicious nodes can manipulate or broadcast false information, compromising the accuracy and reliability of detection mechanisms. Finally, the lack of a comprehensive framework to integrate multidimensional trust information and interrelationships between nodes reduces the robustness of existing detection approaches, making it challenging to identify anomalous behavior in complex and large-scale networks. Consequently, our scheme utilizes a layered trust assessment within Dynamic Behavior-Homogeneous Clusters to compute a composite trust value. Leveraging hypergraph mining quantifies the trust value weights based on node relationships, thereby effectively identifying anomalous nodes in the region that fall below the trust threshold.