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Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s tapping rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

Continuous location authentication (CLA) seeks to continuously and automatically verify the physical presence of legitimate users in a protected indoor area. CLA can play an important role in contexts where access to electrical or physical resources must be limited to physically present legitimate users. In this paper, we present WearRF-CLA, a novel CLA scheme built upon increasingly popular wrist wearables and UHF RFID systems. WearRF-CLA explores the observation that human daily routines in a protected indoor area comprise a sequence of human-states (e.g., walking and sitting) that follow predictable state transitions. Each legitimate WearRF-CLA user registers his/her RFID tag and also wrist wearable during system enrollment. After the user enters a protected area, WearRF-CLA continuously collects and processes the gyroscope data of the wrist wearable and the phase data of the RFID tag signals to verify three factors to determine the user's physical presence/absence without explicit user involvement: (1) the tag ID as in a traditional RFID authentication system, (2) the validity of the human-state chain, and (3) the continuous coexistence of the paired wrist wearable and RFID tag with the user. The user passes CLA if and only if all three factors can be validated. Extensive user experiments onmore »commodity smartwatches and UHF RFID devices confirm the very high security and low authentication latency of WearRF-CLA.« less

Tag cloning and spoofing pose great challenges to RFID applications. This paper presents the design and evaluation of RCID, a novel system to fingerprint RFID tags based on the unique reflection coefficient of each tag circuit. Based on a novel OFDM-based fingerprint collector, our system can quickly acquire and verify each tag’s RCID fingerprint which are independent of the RFID reader and measurement environment. Our system applies to COTS RFID tags and readers after a firmware update at the reader. Extensive prototyped experiments on 600 tags confirm that RCID is highly secure with the authentication accuracy up to 97.15% and the median authentication error rate equal to 1.49%. RCID is also highly usable because it only takes about 8 s to enroll a tag and 2 ms to verify an RCID fingerprint with a fully connected multi-class neural network. Finally, empirical studies demonstrate that the entropy of an RCID fingerprint is about 202 bits over a bandwidth of 20 MHz in contrast to the best prior result of 17 bits, thus offering strong theoretical resilience to RFID cloning and spoofing.

Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.

Mobile two-factor authentication (2FA) has become commonplace along with the popularity of mobile devices. Current mobile 2FA solutions all require some form of user effort which may seriously affect the experience of mobile users, especially senior citizens or those with disability such as visually impaired users. In this paper, we propose Proximity-Proof, a secure and usable mobile 2FA system without involving user interactions. Proximity-Proof automatically transmits a user's 2FA response via inaudible OFDM-modulated acoustic signals to the login browser. We propose a novel technique to extract individual speaker and microphone fingerprints of a mobile device to defend against the powerful man-in-the-middle (MiM) attack. In addition, Proximity-Proof explores two-way acoustic ranging to thwart the co-located attack. To the best of our knowledge, Proximity-Proof is the first mobile 2FA scheme resilient to the MiM and co-located attacks. We empirically analyze that Proximity-Proof is at least as secure as existing mobile 2FA solutions while being highly usable. We also prototype Proximity-Proof and confirm its high security, usability, and efficiency through comprehensive user experiments.