# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 2 Related Work  
As mentioned in the previous section, Sybil attacks [2] are first proposed on a distributed P2P system. It verifies that the Sybil attack occurs in the distributed storage algorithms without authentication, which can easily destroy the distributed key hash table of the system. Then, [PERSON] and [PERSON] [16] proved that distributed routing and geographic routing protocols can also be easily attacked by Sybil attacks in WSN. [PERSON] [17] contrasts multiple intermediate defence mechanisms such as radio resource testing and random key pre-distribution. Random key pre-distribution, which is authenticated by each node ID, has obtained relatively good results. However, each node needs to store and generate a large number of keys, which severely consumes the resources of the system. Meanwhile, the algorithm cannot resist the replay attack. Other algorithms based on authentication mechanisms [18; 19] also have a similar defect as long as this is a reinforcement for the protocol layer. [PERSON]. [20] proposes new methods of key agreement protocol, which can effectively resist Sybil attacks for P2P VoIP application from the transport layer. At the same time, another of his methods [21] makes the routing and the storage and retrieval of resources in a Kademlia network more secure through exploiting reputation techniques for the Sybil attack in the network layer. With the development of public cloud storage [22] and new schemes for outsourced databases recently, the new encryption methods [23; 24; 25] and a practical oblivious RAM with a variable block size [26] may solve the privacy and security problems caused by the Sybil attack. This paper mainly discusses the attack detection of the physical layer in the WLAN [27], so the application of these algorithms will not be done. In recent years, Sybil attacks have been extensively studied in Wireless Sensor Networks (WSN), ad hoc networks, Vehicular Ad Hoc Networks(VANETs) [28] and Wireless Body Area Networks (WBANs) [29]. Most of the detection schemes are active and static in collecting user information, which may be data packets or RSSI values. RSSI has the advantages of lightweight and accurate detection when applied to attack detection. Consequently, in WSN, [PERSON] et al. [4] firstly judged Sybil nodes by comparing the RSSI ratio differences between the neighbours' nodes in the past. This method requires at least four detection nodes to achieve higher detection accuracy, and the wireless network environment must be arranged in advance. Furthermore, [PERSON] [30] was able to detect attacks in the initialization phase when combining the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol features with RSSI-ID tables. Still in WSN, [PERSON] [31; 32] used the K-nest and SVM algorithm in RSSI to achieve high detection efficiency and low error detection rate. However, the algorithm consumes too much detection time. In other methods, the ToA or TDoA detection algorithm [33; 34] did not collect RSSI and used special positioning devices to collect every node's angle information, including Sybil nodes. Obviously, the attacker will deliberately avoid being collected. In ad hoc networks, [PERSON] [7] proposed an system INTERLOC for automatic adjustment of the positioning precision according to the level of environmental interference, which can effectively improve the detection precision of Sybil nodes. In VANET, RSSI-based detection methods are also widely used. Combining the statistical method with the RANSAC algorithm for RSSI, [PERSON], [PERSON] et al. [35] can distinguish the image of traffic congestion caused by attack detection. However, this method does not detect when the attack node is close to the physical vehicle. The work in [36] adds multiple detection nodes to collect the RSSI of the vehicle to determine whether it is consistent with the beacon node. [PERSON], [PERSON] et al. [37] used an event-based reputation system to defend against Sybil attacks. They can keep the vehicle's identity private.