# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 3 Related Work
Vehicles for event verification can access the public global blockchain. Unlike previous consensus methods, Proof of Event uses timestamps to select the block submitter, which reduces power usage. However, based on their proposal, the size of the global blockchain is enormous since it might encompass all the events in a very large geographic area, meaning that a significant number of events are likely to be added to the blockchain every day. Furthermore, their work lacks mutual authentication details. Out of all alert submitters, 40% of them are internal attackers, resulting in a 100% false event success rate. In 2023, the work by [9] presented the trust model that employed an ID-based signature, a Hash Message Authentication Code (HMAC), and an RSA-based method to detect malicious and incorporated messages. The authors in [27] suggested an elaborate reputation approach that considers the message's reliability and the sending vehicle's participation. Every vehicle is tracked, and based on the conduct of the watched vehicle, a trust score is assigned. However, these solutions still have several shortcomings and limitations, such as the fact that they are only helpful for limited types of attacks, a high level of computational complexity, and a lack of scalability. The proximity-based approach prioritizes accident messages from the vehicle closest to the accident location [28]. Their method improves accident detection accuracy and successfully reduces the influence of conflicting messages. However, rather than taking into account other factors, their approach focuses mostly on using proximity as a factor in dispute resolution. In 2021, [PERSON] et al. [29] introduced a hybrid approach that combines reputation values, proximity analysis, and severity assessment for evaluating accident reports. Their study shows the effectiveness of considering severity in reconciling conflicting messages and improving accident detection accuracy. In 2023, the work by [30], proposed a cooperative scheme to detect and prevent false emergency messages, improving V2V reliability with minimal computational overhead. In 2024, [PERSON] et al. [31] suggested the use of a reputation server as a reputation authority to generate reputation certificates; this approach is referred to as certified reputation,which was first proposed in [32]. [PERSON] and [PERSON] [33] proposed a new reputation mechanism to identify malicious vehicles in V2V. This mechanism issues Valid Certificates (VCs) or Invalid Certificates (ICs) status for each vehicle and allows the vehicle to make a decision based on the sender's certificate status. The authors address the delays, overhead processing, and channel interference associated with CRL. However, this study ignored the use of traditional (SCMS), which is an essential part of V2V communication. In the context of misbehavior detection in vehicular networks, various machine learning techniques have been researched and explored to improve misbehavior detection and reputation evaluation. DBSCAN, as a density-based clustering algorithm, has been one of the practical approaches in detecting anomalies and setting the ground truth by identifying dense clusters against outliers. The work by [34] indicated the effectiveness of the DBSCAN in black hole attack detection by integrating it with decision trees, showing its capability to isolate malicious behavior. Similarly, the Isolation Forest has also been an efficient anomaly detection technique; for example, in 2020, [PERSON] et al. [35] applied iForest for cyber anomaly classification by leveraging its ability to isolate outliers through recursive partitioning. Further extended this concept with Deep Isolation Forest, incorporating deep learning to refine anomaly detection accuracy, as shown in [36]. Combining this classification further shares several similarities with DRAMBR. In our proposed system, DBSCAN is implemented to cluster misbehavior reports and set the ground truth based on the reporters' reputations.