## 4 Evaluation
### Security Performance  
This subsection evaluates the security-related performance of the distributed ledger applied in the proposed framework. To evaluate the security performance, we implemented a simulation environment for race attacks, a representative attack that can occur in general distributed ledger systems. A race attack is a type of double-spending attack, a common distributed ledger attack scenario, and refers to an attack with the intention of disrupting the verification system of the distributed ledger using multiple transactions. In a simulation environment, some of the total transactions that occur over a period of time are defined as transactions with malicious intent to attack the entire system. When malicious transactions are verified by the _prover_ and recorded in the DAG, the attack is ultimately successful. Figure 6: The confirmation saturation rate at different confirmation thresholds (a). Figure 7: The average confirmation time at different network scales. Therefore, users with malicious intent strive to add to the distributed ledger by continuously generating attack transactions. First, we measured the average deactivation time for the malicious transactions that occurred and validated the performance of the distributed ledger framework. In order to compare the performance of the proposed framework according to the race attack probability change in the simulation, we used IOTA [59], the most-representative DAG-based distributed ledger system. IOTA is a representative example of a cryptocurrency based on DAG-based distributed ledger technology, which, unlike blockchain, does not use blocks. IOTA uses a structure called a Tangle to build a graph by connecting each transaction with previous transactions. Additionally, this graph-creation method has an advantageous structure for supporting micro-transactions between small devices, so it has high potential for use in the vehicular network field. Therefore, in this experiment, we would like to evaluate the security performance of the proposed system by comparing the proposed system with IOTA, the most-commonly used DAG-based distributed ledger system. Both the proposed system and IOTA have a DAG-based data storage structure, but there is a significant difference in the consensus algorithm that verifies the generated transactions. The proposed system updates transactions through two components: _miner_ and _prover_. During the update process, the cumulative score for a single transaction is increased, and the transaction is verified when the transaction exceeds a certain threshold. The verification process based on this consensus algorithm is advantageous for processing multiple transactions created simultaneously, and we intended to verify this through experiments. In the simulation environment, general network conditions such as the transaction creation speed, network communication delay, and bandwidth settings were reflected using empirical values [46]. Figure 8 shows the results of a simulation performed by changing the race attack probability from 10% to 50% in an environment with a total of 20 nodes using 10 tags, a single server, and 10 anchors. In a simulation environment consisting of a total of 20 nodes, each tag communicates with a fixed anchor and continuously generates transactions related to the location data. We assumed an attack by a malicious node and added some incorrect data transaction information to these periodically generated normal transactions. Attacks caused by these malicious transactions can be identified through the verification process of the distributed ledger system. Deactivation time refers to the time it takes to deactivate a transaction that occurs in a distributed ledger system by determining it to be a malicious transaction. In this experiment, we evaluated the security-related performance of each distributed ledger system by averaging the time it takes to identify and disable malicious transactions.