## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Threat Model  
The threat model (Figure 8) outlined in this section serves as a conceptual framework for specifying the classes of attacks that the proposed blockchain-based reputation management system in the vehicular ad hoc network (VANET) is designed to detect and mitigate. In this model, we make the assumption that potential adversaries are both internally and externally located within the network, thus driven by varying motives ranging from economic gains to intentional system disruption.  
Adversaries may engage in a diverse array of attack vectors, thus targeting both the integrity and availability of the network. These could include, but are not limited to, internal attacks such as false information injection, and external attacks such as denial-of-service (DoS), or man-in-the-middle (MitM) attacks. Moreover, the threat model encompasses collaborative attacks involving multiple malicious nodes, commonly referred to as Sybil or collusion attacks. Specific forms of attacks like on-off attack patterns, newcomer attacks, and inconsistency attacks are also considered within the scope of this model.  
By providing a comprehensive threat model, we aim to elucidate the inherent risks and challenges that VANETs may encounter, thereby informing the security measures and countermeasures that should be incorporated into the blockchain-based reputation management system. This structured approach aids in aligning the security objectives of the proposed system with the actual threat landscape, thereby facilitating more effective and targeted defensive strategies.  
To dissect the vulnerabilities, we consider three archetypical attack modalities that are particularly challenging for any reputation-based model:  
1. Direct Attack: In this scenario, adversaries initially masquerade as legitimate network participants to amass a positive reputation. Upon reaching a critical reputation threshold, they deviate from normative behavior to execute malicious actions. This type of attack poses significant challenges in terms of detection, as the malicious entities maintain a semblance of normalcy for substantial periods.
2. On-Off Attack: Here, adversaries alternate between conforming and deviating from expected behavior throughout their activity cycles. Such erratic conduct aims to sow confusion among other network participants, including roadside units (RSUs). Although less covert than direct attacks, the on-off modality presents its own set of detection challenges due to its intermittent nature.
3. Collusion Attack: In its most insidious form, multiple adversaries collaborate to launch coordinated attacks against specific targets or events. Their tactics may involve manipulating trust scores, not only by artificially lowering the scores of genuine nodes, but also by inflating trust metrics within the colluding group. The orchestrated nature of these attacks makes them particularly difficult to detect and counter.  
Figure 8: Threat model for VANETs.  
In our innovative double-layer blockchain-based reputation management model, a malicious node's trust value undergoes a precipitous decline once it engages in malevolent activities. Should a node's trust score fall below a predetermined threshold, the trust authority (TA) will flag it for immediate relocation to an observation zone, thus rendering its subsequent network activities null and void. After a secondary verification phase, the TA issues a conclusive judgement, thus classifying the node as either malicious or falsely accused. All data pertaining to this node are then indelibly recorded on the blockchain, thus ensuring the system's long-term integrity.