## 4 Experiments and Results: Performance Analysis
### Experimental Methodology: ns-3 Simulations  
Our experimental methodology is founded on detailed simulations conducted using the ns-3 network simulator. This advanced tool enabled us to create a virtual environment for implementing and testing our proposed blockchain-based architecture for VANETs. These simulations were meticulously designed to reflect various traffic conditions, from low to high traffic volumes, thus providing a comprehensive assessment of the architecture under different network scenarios. The performance of the proposed architecture was evaluated in terms of the following four metrics:  
* The probability of successful detection of falsification assaults: This metric quantifies the system's ability to identify and prevent counterfeit information from being injected into the network. * The probability of successful detection of wormhole intrusions: This metric assesses the system's effectiveness in detecting and thwarting clandestine tunnels that manipulate the spatial distribution of network traffic. * The probability of successful detection of packet dropping attacks: This metric evaluates the system's capability in recognizing and mitigating malicious node behavior that involves intentionally discarding incoming packets. * The average latency under various attack scenarios: This metric measures the impact of different attack types on the network's latency performance. The scope of our security analysis extends to a gamut of attack vectors that a compromised VANET node might initiate or be susceptible to. To provide a meticulous characterization, we categorized these potential threats into three primary classes:  
1. Falsification Assaults: In this adversarial model, the compromised node injects counterfeit information into the network. Our framework incorporates advanced cryptographic verification procedures, thus elevating the likelihood of detecting such disinformation campaigns. 2. Wormhole Intrusions: Here, an adversarial node may craft a clandestine tunnel, thereby manipulating the spatial distribution of network traffic. To counteract such illicit activities, our architecture integrates spatiotemporal analytics that facilitate the timely detection of unauthorized tunneling mechanisms. 3. Packet-Dropping Attacks: This type of attack represents a more surreptitious but equally principous threat, where a malicious node intentionally discards incoming packets. Such actions contribute to data loss and degraded network performance. For each aforementioned attack type, our evaluation framework calculates a metric dubbed as the \"Probability of Successful Detection\". This metric serves as a quantitative gauge of the system's efficacy in identifying and counteracting various classes of security threats. High values of this metric are indicative of a robust system with a strong defense against malicious behavior. To accurately evaluate the performance of the proposed security solution, we designed a comprehensive simulation setup replicating a vehicular ad hoc network (VANET). Our simulation environment comprised a network of 100 nodes strategically distributed across an expansive area of 10 km \(\times\) 10 km. This configuration was chosen to emulate a realistic urban setting with diverse vehicular movement patterns, thereby providing a robust testbed for our blockchain-enabled VANET architecture. The parameters chosen for the simulation, as detailed in Table 1, were meticulously selected to mirror real-world traffic conditions and network dynamics. These parameters included variables such as node density, data packet size, physical layer specifications, transmission range, and node mobility speed. By simulating a diverse range of traffic scenarios--from low to high vehicle density--we aimed to test the system's adaptability and resilience under various operational conditions:  
Node Density and Distribution: The selection of 100 nodes offered a balanced representation of a moderately populated urban vehicular network.