Approximation-based Threshold Optimization from Single Antenna to Massive SIMO Authentication

TL;DR

This paper develops an approximation-based method for optimizing authentication thresholds in wireless sensor networks, spanning from single antenna to massive SIMO systems, to maintain a consistent security level against adversaries.

Contribution

It introduces a theoretical framework using chi-square and Gaussian approximations for hypothesis testing, tailored for different system scales and channel conditions, including massive SIMO scenarios.

Findings

Gaussian approximation is effective for massive SIMO analysis.

Time-varying thresholds maintain constant security levels.

Constant thresholds result in fluctuating security levels.

Abstract

In a wireless sensor network, data from various sensors are gathered to estimate the system-state of the process system. However, adversaries aim at distorting the system-state estimate, for which they may infiltrate sensors or position additional devices in the environment. To authenticate the received process values, the integrity of the measurements from different sensors can be evaluated jointly with the temporal integrity of channel measurements from each sensor. For this purpose, we design a security protocol, in which Kalman filters are used to predict the system-state and the channel-state values, and the received data are authenticated by a hypothesis test. We theoretically analyze the adversarial success probability and the reliability rate obtained in the hypothesis test in two ways, based on a chi-square approximation and on a Gaussian approximation. The two approximations are exact for small and large data vectors, respectively. The Gaussian approximation is suitable for analyzing massive single-input multiple-output (SIMO) setups. To obtain additional insights, the approximation is further adapted for the case of channel hardening, which occurs in massive SIMO fading channels. As adversaries always look for the weakest point of a system, a time-constant security level is required. To provide such a service, the approximations are used to propose time-varying threshold values for the hypothesis test, which approximately attain a constant security level. Numerical results show that a constant security level can only be achieved by a time-varying threshold choice, while a constant threshold value leads to a time-varying security level.