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
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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. 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. Interest in on-body communication channels is growing as the use of wireless devices increases in medical, consumer and military sensor applications. This paper presents an experimental investigation and analysis of the narrowband on-body propagation channel. This analysis considers each of the factors affecting the channel during a range of stationary and motion activities in different environments with actual wireless mote devices on the body. Use of such motes allows greater freedom in the subject's movements and the inclusion of real-world indoor and outdoor environments in a test sequence. This paper identifies and analyses the effect of the different components of the signal propagation (mean propagation path gain, large-scale fading and small-scale fading) and the cause of the losses and variation due to activities, positions or environmental factors. Our results show the effect on the received signal and the impact of voluntary and involuntary movements, which cause shadowing effects. The analysis also allows us to identify sensor positions on the body that are more reliable and those positions that may require a relay or those that may be suitable for acting as a relay. 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.