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.

Digital Identity Challenge: The Security and Convenience Dilemma

This paper argues that the essential pieces of an enduring digital identity should be privacy, security, and convenience. Authentication should be frictionless. In this sense, the core of the digital identity of the future will be created around location sensing techniques. Incognia proposes a solution to secure and frictionless authentication for mobile apps that is composed of five steps. Its proprietary technology called environment fingerprinting can identify location spoofing and precisely determine the devices actual location. Incognia has found that most mobile logins, sensitive transactions, and purchases occur at trusted locations. To date, 90% of mobile logins and 89% of mobile banking sessions happen at a trusted location. Experimental results show false-negative rates below 0.004% and a decrease of over 85% of account takeover attacks.

Wasserstein Metric-Based Location Spoofing Attack Detection in WiFi Positioning Systems

WiFi positioning systems (WPS) have been introduced as parts of 5G location services (LCS) to provide fast positioning results of user devices in urban areas. However, they are prominently threatened by location spoofing attacks. To end this, we present a Wasserstein metric-based attack detection scheme to counter the location spoofing attacks in the WPS. The Wasserstein metric is used to measure the similarity of each two hotspots by their signal’s frequency offset distribution features. Then, we apply the clustering method to find the fake hotspots which are generated by the same device. When applied with WPS, the proposed method can prevent location spoofing by filtering out the fake hotspots set by attackers. We set up experimental tests by commercial WiFi devices, which show that our method can detect fake devices with 99% accuracy. Finally, the real-world test shows our method can effectively secure the positioning results against location spoofing attacks.

GPS Location Spoofing and FM Broadcast Intrusion Using Software-Defined Radio

This paper makes use of a simple and inexpensive software-defined radio (SDR) to demonstrate the potential threats posed to wireless communication. SDR is a radio communication system where components that are traditionally implemented in hardware are being replaced via software running on computing devices. The authors make use of a simple SDR to demonstrate how local disruption to wireless communication can be easily carried out. In particular, the authors show how FM radio broadcast can be hijacked and the spoofing of GPS location signals using a single SDR on a local basis as well as how Google Maps apps on an Android phone can be fooled by the spoofed GPS data. The authors also show how an ‘autonomous' car can be re-routed via emulation with a rigged up remote control toy car. The spoofing of GPS signals is a potential threat to all GPS-based applications, especially when powerful radios are used. The security threats on GPS-based navigation especially for drones and autonomous vehicles are real.