Abstract

This manuscript presents a novel mechanism (at the physical layer) for authentication and transmitter identification in a body-centric nanoscale communication system operating in the terahertz (THz) band. The unique characteristics of the propagation medium in the THz band renders the existing techniques (say for impersonation detection in cellular networks) not applicable. In this work, we considered a body-centric network with multiple on-body nano-senor nodes (of which some nano-sensors have been compromised) who communicate their sensed data to a nearby gateway node. We proposed to protect the transmissions on the link between the legitimate nano-sensor nodes and the gateway by exploiting the path loss of the THz propagation medium as the fingerprint/feature of the sender node to carry out authentication at the gateway. Specifically, we proposed a two-step hypothesis testing mechanism at the gateway to counter the impersonation (false data injection) attacks by malicious nano-sensors. To this end, we computed the path loss of the THz link under consideration using the high-resolution transmission molecular absorption (HITRAN) database. Furthermore, to refine the outcome of the two-step hypothesis testing device, we modeled the impersonation attack detection problem as a hidden Markov model (HMM), which was then solved by the classical Viterbi algorithm. As a bye-product of the authentication problem, we performed transmitter identification (when the two-step hypothesis testing device decides no impersonation) using (i) the maximum likelihood (ML) method and (ii) the Gaussian mixture model (GMM), whose parameters are learned via the expectation–maximization algorithm. Our simulation results showed that the two error probabilities (missed detection and false alarm) were decreasing functions of the signal-to-noise ratio (SNR). Specifically, at an SNR of 10 dB with a pre-specified false alarm rate of , the probability of correct detection was almost one. We further noticed that the HMM method outperformed the two-step hypothesis testing method at low SNRs (e.g., a increase in accuracy was recorded at SNR = −5 dB), as expected. Finally, it was observed that the GMM method was useful when the ground truths (the true path loss values for all the legitimate THz links) were noisy.

Highlights

  • Nanoscale communication systems have attracted researchers due to their promising applications in healthcare, manufacturing industries, environmental control, etc. [1]

  • We focused on the body-centric communication systems where nano sensors/devices operating in the THz band are deployed on the body of a human being

  • Was far better than HT, and at a high signal-to-noise ratio (SNR), HT was close to the hidden Markov model (HMM)

Read more

Summary

Introduction

Nanoscale communication systems have attracted researchers due to their promising applications in healthcare, manufacturing industries, environmental control, etc. [1]. We focused on the body-centric communication systems where nano sensors/devices operating in the THz band are deployed on the body of a human being. With recent advances in quantum computing, traditional encryption has become vulnerable to being decoded, and Sensors 2021, 21, 3534 existing crypto-based measures are not quantum secure unless the size of secret keys increases to impractical lengths [6] In this regard, physical layer (PL) security finds itself as a promising mechanism in future communication systems. Contributions: For the first time, this work studied authentication at a nano-to-micro interface device (wearable device) in an on-body-centric communication system where we exploited the high-resolution transmission molecular absorption (HITRAN) database [19]. Impersonation attack detection at the wearable device/receiver/Bob in multiple legitimate and malicious nano nodes operating in the THz band is performed via different mechanisms.

System Model
Authentication via Two-Step Hypothesis Testing
Hidden Markov Model-Based Approach
ML-Based Approach
Transmitter Identification Using Gaussian Mixture Modeling
Simulations
Results
Discussions
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call