## 2 Theoretical Foundation and Sybil Attack Landscape  
### Sybil Attacks: Concepts, Core Characteristics, Varieties, and Impact on Network Integrity  
Scholars are concerned about how cybercrime is escalating rapidly, with unprecedented incidents. Considering that modern technologies facilitate swift data transfer through various communication protocols, with the key communication player being wireless technologies, e.g., 5G and WLAN, security must be assured for such wireless technologies [24]. By compromising robust security measures, numerous essential applications like mobile phones, satellites, access points, and wireless internet may face vulnerabilities, such as data breaches via such devices [25]. Researchers are keen to counter the security attacks on such technologies. Such wireless technology, due to its extensive usage across a wide spectrum of applications, is vulnerable, according to the Pew Research Center (2023), which highlights that about 97% of Americans own mobile phones, 85% of which are smartphones, with the reported American population being roughly 307 million [26]. According to [PERSON] [27], the count of mobile phones in 2010 was around 4.5 billion, a sizable target pool for attackers. As per NBC News [26], three-quarters of Americans possess laptops or desktops, which are thus sizable, vulnerable targets. Thus, considering their data sensitivity, an initiative to safeguard such a large pool of targets is required, particularly when such targets use wireless sensors in their modern applications, causing security concerns to escalate further. One drawback of wireless communication systems is unrestricted data and media sharing over networks, which are susceptible to signal interception, allowing potential attackers to spoof crucial information for launching attacks. When wireless networks are exploited, information necessary for successful attacks, i.e., an identity-based attack, is exploited. The literature sheds light on two simple yet destructive identity-based attacks: spoofing and Sybil attacks. Spoofing attacks are where attackers monitor targeted networks, gather legitimate user information, e.g., IP or MAC addresses, and pose as legitimate users to gain access to unauthorized services and data. A Sybil attack is a malicious node network infiltration to form multiple fake nodes that claim to legitimately deceive neighboring nodes by forwarding inappropriate information. By spoofing and legitimate information, attackers gradually introduce newer fake nodes, assuming control over time, thus enabling unauthorized data access and launching various attacks, e.g., denial of service attacks and data manipulation [28]. A Sybil attack targets P2P networks to create and control multiple fake identities, allowing an attacker to launch deceptive activities. Through an attacker's multiple created and controlled identities, they can concurrently deploy multiple nodes, misleading the network into granting unauthorized data access, like a reviewer fabricating numerous product reviews on Amazon to fake positive customer feedback or creating multiple accounts on Twitter to influence users. Also, sensor networks suffer from Sybil attacks when attackers pose as legitimate nodes, cascading to deceptively obtaining precise user information through social engineering or network spoofing. Such private information fuels Sybil's attacks, eventually granting attackers undue network influence [29]. According to [PERSON] [8], Sybil nodes illicitly claim multiple identities or fake IDs. Figure 1 depicts a Sybil attack, where a yellow node denotes legitimate ones and red nodes represent Sybil nodes. Sybil misleads the victim network during an attack when posing as legitimate, consequently redirecting traffic. For instance, node (A) mimics (H) to node (L) and (J) to node (K), causing traffic heading to node (A), which either forwards, alters, or blocks messages. Such a tactic extends to other red nodes. Attackers exploit victim nodes to wreak havoc, breach data, or lurk and launch attacks, yielding diverse outcomes.