A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computing

Abstract

Background: Internet of things (IoT) has been used in many places. IoT make devices connected to the Internet via sensor devices to achieve the interconnection between things and things, people and things. Sybil attacker attacks IoT by imitating the identity of users. Few methods are applicable for mobile IoT in previous Sybil attack detecting methods, while the methods are mainly focus on static IoT. Results: A distributive and lightweight Sybil attack detection scheme in the mobile IoT is proposed in this paper. This scheme works around received signal strength indications (RSSI). The scheme consists of two rounds. Identity information is sent from member nodes to edge nodes in both of the two rounds. In the first round edge, nodes calculate the possible RSSI interval for each member node; in the second round, they check the RSSI value of member nodes to detect Sybil attacks. Intelligent algorithms are used to predict the position of member nodes, which makes the theoretical interval more accurate. Extensive experimental studies show that in the true and false detection rate, this scheme is superior to many existing schemes.