Using the physical layer for wireless authentication in time-variant channels

Abstract

Abstract-The wireless medium contains domain-specific information that can be used to complement and enhance traditional security mechanisms. In this paper we propose ways to exploit the spatial variability of the radio channel response in a rich scattering environment, as is typical of indoor environments. Specifically, we describe a physical-layer authentication algorithm that utilizes channel probing and hypothesis testing to determine whether current and prior communication attempts are made by the same transmit terminal. In this way, legitimate users can be reliably authenticated and false users can be reliably detected. We analyze the ability of a receiver to discriminate between transmitters (users) according to their channel frequency responses. This work is based on a generalized channel response with both spatial and temporal variability, and considers correlations among the time, frequency and spatial domains. Simulation results, using the ray-tracing tool WiSE to generate the timeaveraged response, verify the efficacy of the approach under realistic channel conditions, as well as its capability to work under unknown channel variations.

Channel-based spoofing detection in frequency-selective rayleigh channels

“…Then the impact of channel time variations and terminal mobility were studied in [11] and [13], respectively. We also explored the use of multiple-input multiple-output (MIMO)-techniques with the channel-based detector in [12].…”

“…Our previous work [10]- [13] has partly addressed these impairments and validated the efficacy of the channel-based authentication for various cases, notably, environmental changes [11], and terminal mobility [13]. However, these studies are limited by virtue of using different heuristic test statistics for each scenario.…”

Extrinsic Channel-Like Fingerprint Embedding for Authenticating MIMO Systems

“…We model the time-variant portion of the channel response gain for each element of [ ] corresponding as an independent first-order autoregressive (AR-1) model. The AR-1 model has been used to describe time-variant channels in previous works [11], and [20], [12]. Assuming an average AR-1 noise power 2 over all time-variant gain elements , [ ], the AR-1 model is given as…”

“…We now consider two methods for detecting the fingerprint signal given the sequence of channel state information in (12).…”

“…In [12] it was demonstrated that robust PHY-layer fingerprints can be obtained from intrinsic characteristics of 1536-1276/11$25.00 c ⃝ 2011 IEEE wireless channels, such as unique scattering environments and spatial variability, to authenticate the transmitter without additional signal superposition. This work demonstrated that transmitters can be validated when the multipath channel profiles for each transmitter are unique and sufficiently stationary.…”