## 2 Theoretical Foundation and Sybil Attack Landscape
### Unleashing Defense Strategies: Safeguarding against Sybil Attacks  
Various techniques protect against Sybil attacks by mitigating their likelihood. However, no efficient solution can prevent such a threat [31; 32; 33; 15]. [PERSON] et al. [4] suggested a cryptography-based approach with overheads; [PERSON] et al. [17] proposed controllable overheads. From the view of the authors of this study, an optimal approach should balance security and performance, as also elaborated by [PERSON] et al. [4]. It is essential to assess the widely recognized techniques for mitigating Sybil attacks, categorized by protection,  
Figure 1: Sybil Attack on a P2P network. detection, and mitigation. Such frequently cited strategies/techniques/approaches can be categorized into six types: (1) Certifications are recognized mitigation approaches involving certifying nodes through authorized identification [22]. [PERSON] and [PERSON] exemplified VeriSign as a certification system that foils Sybil attacks, though challenges emerge without central certification. (2) Resource testing in known resource networks like sensor arrays [34], where nodes' physical resources validate legitimacy through commonly known resource allocation statistics [3]. Consider a network of temperature sensors where all sensors possess identical computational power, memory, and battery capacity. In this mitigation approach, nodes' physical resources are examined to ascertain their alignment with the attributes of legitimate nodes. While nodes execute tasks, reputation systems assess if the node is legitimate or Sybil. [PERSON] et al. [35] used the resource-testing approach to detect Sybil nodes in wireless networks. Their system determines whether a node is Sybil based on how each node responds to a request. [PERSON] et al. (2004) showed how attackers use a single physical device to introduce multiple nodes into a network. Additionally, (3) in incentive-based detection, where [PERSON] et al. [13] proposed an economic detection protocol for Sybil attacks, an attacker is financially incentivized for two or more controlled identities. (4) Location/position and timing verification is based on detecting a Sybil attack based on its location. To demonstrate legitimacy, every node must be in a specific location. It detects the Sybil attackers by discovering if multiple nodes are in an exact location or moving in a coordinated or synchronous pattern [18]. (5) Random key pre-distribution, where nodes receive unique identifying keys before deployment [14], is a more suitable approach for Wireless Sensor Networks (WSNs) due to their limited computational and energy capabilities. [PERSON] et al. [36] and [PERSON] and [PERSON] [37] introduced key management schemes specifically designed for distributed sensor networks. (6) Privilege attenuation, where [PERSON] [12] counters Sybil attacks in Facebook-style social network systems (FSNSs) with privilege attenuation, an approach aligned with [PERSON]'s principle of privilege attenuation, to limit and prevent Sybil attacks such as access restrictions among friends to reduce Sybil attack risks in FSNSs. Sybil detection algorithms have been deployed to detect if a Sybil attack is occurring. They determine nodes' legitimacy by differencing malicious from legitimate nodes. Several scholars [2; 31; 32; 38] have explored techniques for preventing and mitigating Sybil attacks. [PERSON] et al. [39] suggested a SyDVELM mechanism employing the extreme learning machine (ELM) algorithm to detect Sybil attacks in VANETs. [PERSON] et al. [40] proposed a SybilLimit protocol to counter Sybil attacks in OSNs. [PERSON] et al. [16] examined Sybil detection algorithms for wireless sensor networks where nodes function as sensors. Often, WSNs are deployed for monitoring purposes, e.g., military applications, and their unattended nature renders them susceptible to Sybil attacks and cyber threats, such as a network of sensors measuring air speed and temperature to predict future weather.