Blockchain localization spoofing detection based on Fuzzy AHP in IoT systems

Location spoofing detection is an important part of location proof schemes in IoT systems. It is important for location systems to accurately evaluate the credibility of location data uploaded by users. Differ with the works employing physical layer features such as signal strength or channel state information, we focus on laying the foundation for solutions aiming to build the location spoofing detection functions in the blockchain based IoT systems. More specifically,an IoT system is established at the node level and the moving-track level for the evaluation of the credibility of location proof with the characteristics of the blockchain positioning system. A multi-layer fuzzy Analytic Hierarchy Process (AHP) evaluation method is considered for the detection of location spoofing in blockchains. The simulation results show that the proposed scheme is practical, and can provide a reference for the credibility evaluation of location proof.

GNSS Spoofing Detection Based on Coupled Visual/Inertial/GNSS Navigation System

Spoofing attacks are one of the severest threats for global navigation satellite systems (GNSSs). This kind of attack can damage the navigation systems of unmanned air vehicles (UAVs) and other unmanned vehicles (UVs), which are highly dependent on GNSSs. A novel method for GNSS spoofing detection based on a coupled visual/inertial/GNSS positioning algorithm is proposed in this paper. Visual inertial odometry (VIO) has high accuracy for state estimation in the short term and is a good supplement for GNSSs. Coupled VIO/GNSS navigation systems are, unfortunately, also vulnerable when the GNSS is subject to spoofing attacks. The method proposed in this article involves monitoring the deviation between the VIO and GNSS under an optimization framework. A modified Chi-square test triggers the spoofing alarm when the detection factors become abnormal. After spoofing detection, the optimal estimation algorithm is modified to prevent it being deceived by the spoofed GNSS data and to enable it to carry on positioning. The performance of the proposed spoofing detection method is evaluated through a real-world visual/inertial/GNSS dataset and a real GNSS spoofing attack experiment. The results indicate that the proposed method works well even when the deviation caused by spoofing is small, which proves the efficiency of the method.