Doppler-Shift-Based Sybil Attack Detection for Mobile IoT Networks

Abstract

The rapid growth of Internet of Things (IoT) networks brings new security challenges for service providers. Due to the resource constrained nature of IoT networks, conventional security methods are not always suitable. Therefore, physical layer security (PLS) has come to the forefront, providing a high level of security while respecting the limited resources of IoTs. A Sybil attack is an insider attack in which a malicious node illegitimately fakes multiple identities, to impersonate legitimate nodes in the IoT network. This study introduces a novel Sybil attack detection scheme for mobile IoT networks that corresponds to time-varying channel. Doppler shift caused by mobile IoT nodes is considered as a novel detection metric to identify available Sybil attacks. The proposed scheme is analyzed by both the true positive rate and the false positive rate, which are mathematically formulated to verify the simulation results. Results demonstrate that as the randomness in the mobility pattern of IoT nodes increases, the proposed detection mechanism based on Doppler shift offers improved identification of the Sybil nodes. Moreover, this work provides receiver operating characteristics (RoCs) for mobile IoT networks to evaluate the effect of different system parameters including carrier frequency, velocity, subcarrier spacing (SCS), and the size of cyclic prefix (CP). The performance of the proposed scheme improves with an increase in both the carrier frequency and velocity, as the Doppler shift becomes more pronounced.