Securing the insecure: a first-line-of-defense for body-centric nanoscale communication systems operating in THz band

Aman, W., Rahman, M. M. U., Abbas, H. T. , Khalid, M., Imran, M. A. , Alomainy, A. and Abbasi, Q. H. (2021) Securing the insecure: a first-line-of-defense for body-centric nanoscale communication systems operating in THz band. Sensors, 21(10), 3534. (doi: 10.3390/s21103534)

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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 0.2, 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 10% 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.

Item Type:Articles
Additional Information:This work was made possible by EPSRC Grant Number EP/T021063/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and aman, waqas and Abbas, Dr Hasan
Creator Roles:
aman, w.Conceptualization, Software, Writing – original draft
Abbasi, Q.Supervision, Project administration
Imran, M.Supervision, Project administration
Abbas, H.Validation, Writing – review and editing
Authors: Aman, W., Rahman, M. M. U., Abbas, H. T., Khalid, M., Imran, M. A., Alomainy, A., and Abbasi, Q. H.
College/School:College of Science and Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Sensors
Publisher:MDPI
ISSN:1424-8220
ISSN (Online):1424-8220
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Sensors 21(10):3534
Publisher Policy:Reproduced under a Creative Commons License

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