FS2M: fuzzy smart IoT device pairing protocol via speak to microphone

• Date: 20 November 2024
• Pages: 1-21
• DOI: https://doi.org/10.1108/IJWIS-06-2024-0169
• Published date: 20 November 2024
• Subject Matter: Information & knowledge management,Information & communications technology,Information systems,Library & information science,Information behaviour & retrieval,Metadata,Internet
• Author: Huaiyu Jia,Dajiang Chen,Zhidong Xie,Zhiguang Qin

FS2M: fuzzy smart IoT device

pairing protocol via speak to

microphone

Huaiyu Jia and Dajiang Chen

School of Information and Software Engineering,

University of Electronic Science and Technology of China, Chengdu, China

Zhidong Xie

Department of Innovative Research, Academy of Military Sciences PLA,

Beijing, China, and

Zhiguang Qin

School of Information and Software Engineering,

University of Electronic Science and Technology of China, Chengdu, China

Abstract

Purpose –This paper aimsto provide a secure and eff‌icient pairing protocol for twodevices. Due to the large

amount of data involving sensitive informationtransmitted in Internet of Things (IoT) devices, generating a

secure shared key betweensmart devices for secure data sharing becomes essential. However, existing smart

devices pairing schemesrequire longer pairing time and are diff‌icult to resist attacks caused by context, as the

secure channelis established based on restricted entropy from physical context.

Design/methodology/approach –This paper proposes a fuzzy smart IoT devicepairing protocol via speak

to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the

environment around the devices, which is easily implemented in the vast majority of Internet products.

Specif‌ically, to protect the privacy of secret keys and improveeff‌iciency, this paper presents a single-round

pairing protocolby adopting a recently published asymmetric fuzzy encapsulationmechanism (AFEM), which

allows devices with similar environmentalf‌ingerprints to successfully negotiate the shared key.To instantiate

AFEM, this paperpresents a construction algorithm, the AFEM-ECC, based on ellipticcurve cryptography.

Findings –This paper analyzes the security of the FS2M and its pairing eff‌iciency with extensive

experiments.The results show that the proposed protocol can achieve a secure device pairing betweentwo IoT

devices withhigh eff‌iciency.

Originality/value –In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT

device pairing by using a new context-environment (i.e., human voice) . Theexperimental results show that

FS2M has a goodperformance in both communication cost (i.e.,130 KB) and running time (i.e., 10 S).

Keywords Security, Device pairing, Internet of Things

Paper type Research paper

1. Introduction

With the development of artif‌icial intelligence (Aleet al., 2024), the next wireless communication

(Gopal and Velmurugan, 2024), edge computing (Xue et al., 2023;Moghaddasi et al., 2024),

This work is jointly supported by NSFC (No. 62002047), the National Defense Project “Research on

Digital Twin and Sensor Modeling Technology”(No.30601010704), and in part by the Demonstration of

Scientif‌ic and Technology Achievements Transformin Sichuan Province under Grant 2022ZHCG0036.

International

Journal of Web

Information

Systems

1

Received 17 June 2024

Revised2 September 2024

Accepted 4 Sep tember 2024

InternationalJournal of Web

InformationSystems

Vol.21 No. 1, 2025

pp. 1-21

© Emerald Publishing Limited

1744-0084

DOI 10.1108/IJWIS-06-2024-0169

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1744-0084.htm

Internet of Things (IoT) (Vazhuthi et al., 2023) and IoT security (Chen et al., 2024), as well as the

increasing popularity of consumer IoT products (Jin et al., 2022), there is growing concern abou t

the security of IoT (Lin et al., 2021). Large amounts of data are exchanged between smart IoT

devices on a daily basis, which often includes sensitive information (Han et al., 2017;Gao et al.,

2021b), e.g. users’property information, health records and family details. However, the

transmitted information is vulnerable to various adversary attacks such as man-in-the-middle

attacks, illegal eavesdropping and message tampering (Li et al., 2019). We need to conf‌irm the

data integrity during the transmission of the devices (Li et al., 2024). Therefore, it is es sential to

establish secure communication channels between smart IoT devices (Gao et al., 2021a). This not

only protects sensitive information but also enhances the reliability and trustworthiness of IoT

systems, which is vital for their widespread adoption in applications such as smart homes, health

care and industrial automation.

To ensure secure communication, devices must negotiate a secure key through device

pairing. In a typical pairing process, the IoT devices obtain the reliable context from

environment with high f‌ingerprinting similarity between them and then generate keys

through cryptography primitives with their f‌ingerprints. In recent years, there have been

many secure pairing schemes that are divided into two categories: zero-interaction pairing

(Fomichev et al., 2021;Schürmann and Sigg,2011) and user-assisted pairing. Traditionally,

user-assisted pairing schemes need human involvement, such as entering specif‌ic gestures

(Ahmed et al.,2015).However, most of the IoT devices designed at this stage do not provide

screens or keyboards for users to accomplish hands-on input. Moreover, carefully designed

tiny devices [i.e. implantable smart machines (Hassija et al., 2021)] are diff‌icult to operate

manually.Updating and deleting keys in these protocols are inconvenient due to theneed for

users’involvement.

Zero-interaction pairing does not require user participation as the devices extract valid

information from their similar context to generate the shared key. The context is f‌irst

obtained by IoT devices from the surrounding environment through one or more sensors.

Then, the context can be converted into a sequence of bits as the device’s environmental

f‌ingerprint to generate a shared key via a key agreementmethod. Acceleration and sound are

of great interest because they have higher entropycompared to other sources of entropy (Xie

et al., 2018). Acceleration-based pairing is commonly used for IoT devices, e.g. wearable

devices and vehicles (Fomichev et al., 2021;Xie et al.,2018), but the drawback is, it can

only be applied to devices which can move. Note that, most IoT devices equipped with

microphones, which makes it possible to participate in device pairing using sound. A

proposed pairing scheme in Schürmann and Sigg (2011) uses ambient audio as context.

However, natural ambient audio makesit challenging to acquire f‌ingerprints without human

intervention, and it takes a long time to pair with a low similarity. In Mei et al. (2019),a

personal computer is used to play extra sound to enhance ambient audio, which still brings

low pairing success rates.

On the other hand, to ensure the shared keys with security, advanced IoT device pairing

schemes require long f‌ingerprints (i.e. >256 bits) and high entropy (Jin et al., 2022). The

pairing schemes are usually based on a cryptographic primitive technique called fuzzy

commitments (Juels and Sudan, 2006), which will take minutes to carry out. In Fomichev

et al.(2021), the pairing time was shorted by reducing the number of f‌ingerprint bits while

resisting off‌line attacks. Moreover, this scheme incorporates the fuzzy password-

authenticated key exchange(fPAKE) protocol (Dupont et al., 2018) to share a secure key by

using low-entropy secrets (e.g. short passwords). However, this scheme requires running

password-authenticatedkey exchange (PAKE) several times in entropyamplif‌ication, which

consumes a great dealof communication resources during device pairing.

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