On-body devices are an intrinsic part of the Internet-of-Things (IoT) vision to provide human-centric services. These on-body IoT devices are largely embedded devices that lack a sophisticated user interface to facilitate traditional Pre-Shared Key based security protocols. Motivated by this real-world security vulnerability, this paper proposes SecureTag, a system designed to add defense in depth against active attacks by integrating physical layer (PHY) information with upper-layer protocols. The underpinning of SecureTag is a signal processing technique that extracts the peculiar propagation characteristics of creeping waves to discern on-body devices. Upon overhearing a suspicious transmission, SecureTag initiates a PHY-based challenge-response protocol to mitigate attacks. We implement our system on different commercial off-the-shelf (COTS) wearables and a smartphone. Extensive experiments are conducted in a lab, apartments, malls, and outdoor areas, involving 12 volunteer subjects of different age groups, to demonstrate the robustness of our system. Results show that our system can mitigate 96.13 of active attack attempts while triggering false alarms on merely 5.64 of legitimate traffic.
Many existing measurements have studied the propagation model for on-body channels @cite_33 @cite_14 @cite_8 @cite_9 @cite_1 @cite_27 . These studies suggest that there exists substantial differences in on-body and off-body propagations. Their measurement results indicate that it is feasible to use radio propagations to distinguish between on-body and off-body devices.
@cite_14 @cite_33 @cite_8 @cite_9 @cite_1 @cite_27
1993949615 2143824524 2098548444 2103002377 2161455567
A channel model for a wireless body area network at 400 MHz, 900 MHz and 2.4 GHz is derived. The electromagnetic wave propagation around the body is simulated with a finite-difference time-domain simulator. Creeping waves were identified as the propagation path around the body. Its impact on the delay spread in an indoor environment is discussed. The fading of an on-body transmission channel at 2.45 GHz is investigated. For the first time a large amount of data has been gathered in practical environments, for realistic activities, and has been subject to statistical analysis. It is clear that significant variations of the path gain and fading can occur due to the movements of body. Extensive statistical analysis has shown that fading in on-body channels is a non stationary process. This letter presents results of a number of significant statistical parameters, including level crossing rate and average fade duration. This letter presents the characterization of an on-body propagation channel with a specific activity of a body on the basis of the measurement results of a male subject in a radio anechoic chamber. In order to capture the dynamic behavior of the channel, a commercial real-time channel sounding system for the 4.5-GHz narrowband was used. The measurement scenarios included two repetitive actions of walking on the spot and standing up and sitting down at 10 different receive antenna locations where the fading caused by the action was observed and statistically modeled. The first analytical model of Body Area Network channels is presented. The formulation includes the body morphology and the characteristics of the human tissues. The studied transmission paths are along curved parts like the waist or the head. The model is derived from the diffraction theory describing the attenuation of creeping waves along a circular path on a lossy dielectric surface. The model is validated by measurements performed with Planar Inverted-F Antennas on human subjects. In wireless body area network (BAN), node authentication is essential for trustworthy and reliable gathering of patient's critical health information. Traditional authentication solutions depend on prior trust among nodes whose establishment would require either key pre-distribution or non-intuitive participation by inexperienced users. Most existing non-cryptographic authentication schemes require advanced hardware or significant modifications to the system software, which are impractical for BANs. In this paper, for the first time, we propose a lightweight body area network authentication scheme BANA. Different from previous work, BANA does not depend on prior-trust among nodes and can be efficiently realized on commercial off-the-shelf low-end sensors. We achieve this by exploiting a unique physical layer characteristic naturally arising from the multi-path environment surrounding a BAN, i.e., the distinct received signal strength (RSS) variation behaviors among on-body channels and between on-body and off-body communication channels. Based on distinct RSS variations, BANA adopts clustering analysis to differentiate the signals from an attacker and a legitimate node. We also make use of multi-hop on-body channel characteristics to enhance the robustness of our authentication mechanism. The effectiveness of BANA is validated through extensive real-world experiments under various scenarios. It is shown that BANA can accurately identify multiple attackers with minimal amount of overhead.