A Decentralized Authentication Model for Internet of Vehicles Using SSI

Victor Emanuel F. C. Borges

, Danilo F. S. Santos

and Dalton C. G. Valadares

Future Connected Systems, Embedded Systems and Pervasive Computing Laboratory, Brazil

Keywords:

Internet of Vehicles, Vehicular Networks, Authentication, Self-Sovereign Identity.

Abstract:

The Internet of Vehicles (IoV) ecosystem is well-regarded for its overall security, yet authentication remains

a critical concern due to existing vulnerabilities that expose users to potential malicious attacks. Although

researchers have devised authentication mechanisms and protocols to address these issues, there are two sig-

niﬁcant risk factors often overlooked by prevalent solutions. The ﬁrst is trust in out-of-coverage mode, which

can leave vehicles vulnerable to receiving forged messages. The second is the centralization of the standard

authentication mechanism, where reliance on a centralized third-party service introduces authentication vul-

nerabilities that can result in access loss. In this article, we propose an innovative solution that incorporates

the Self-Sovereign Identity (SSI) decentralized identity model within the Trust Over IP architecture to pro-

vide vehicular authentication. This integration establishes decentralized identiﬁcation mechanisms suitable

for various contexts within the IoV ecosystem. Our primary focus is enhancing security in the Advanced

Driver-Assistance System (ADAS) context. We leverage the SSI model to design a specialized authentica-

tion scheme, aiming to effectively mitigate associated security risks through decentralization. This approach

strengthens authentication security within the IoV ecosystem, addressing the mentioned vulnerabilities.

1 INTRODUCTION

In today’s interconnected world, the Internet of

Things (IoT) has permeated nearly every facet of our

lives, from smart homes and wearable devices to in-

dustrial automation. One of the main areas of study

in IoT environment today is the Internet of Vehicles

(IoV). IoV represents a paradigm shift in the auto-

motive industry, as vehicles become increasingly con-

nected, intelligent, and capable of sharing critical in-

formation in real-time.

The major focus of the Internet of Vehicles ﬁeld

of study is on safety-related applications, which con-

sider the physical safety aspects of the driver, passen-

gers, pedestrians, and other entities involved. These

scenarios include collision avoidance applications,

post-collision notiﬁcations, lane change alerts, and

blind spot alerts. However, this newfound connectiv-

ity also introduces a host of security challenges, with

authentication standing as a paramount concern.

For securing data in these applications, their mes-

sages must be protected and encrypted, and they must

not leak information about the user. Otherwise, it

https://orcid.org/0000-0001-5346-8051

https://orcid.org/0000-0002-8162-715X

https://orcid.org/0000-0003-1709-0404

would violate the privacy rights. Furthermore, it is

essential to ensure that whoever sent the message is a

genuine user and not a malicious one.

In summary, IoV needs to guarantee the authen-

tication and anonymization of its involved entities,

something challenging for a volatile and restricted

technology such as the vehicular networks that imple-

ment the IoV ecosystem. To mitigate authentication-

related threats, some researchers have proposed au-

thentication mechanisms in various ways (Vasudev

and Das, 2018) (Gayathri et al., 2018) (Islam et al.,

2018) (Vijayakumar et al., 2017) (Cui et al., 2018).

However, none of them solve critical cases related to

the volatility of vehicles out of coverage and the de-

centralization of third-party entities. Therefore, con-

sidering a threat model based on these critical cases,

this article presents a new authentication approach

based on the Self-Sovereign Identity concept and the

Trust Over IP architecture to mitigate these threats.

The remainder of this article is organized as fol-

lows. Section 2 overviews the current authentication

issues and threats in IoV. Section 3 presents the state

of the art of authentication mechanisms in IoV. Sec-

tion 4 presents the considered threat model for this

work. Section 5 introduces Self-Sovereign Identity

model and Trust Over IP architecture for decentral-

Borges, V., Santos, D. and Valadares, D.

A Decentralized Authentication Model for Internet of Vehicles Using SSI.

DOI: 10.5220/0012259800003584

In Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023), pages 545-552

ISBN: 978-989-758-672-9; ISSN: 2184-3252

545

ized authentication. Section 6 presents our proposed

SSI-based vehicular authentication solution. Finally,

Section 7 concludes this paper.

2 AUTHENTICATION ISSUES

AND THREATS

In the context of IoV safety-related applications, most

messages are transmitted via broadcast and need to

be delivered in a short time. These messages must

also be properly secured and encrypted to prevent data

leakage.

To safeguard privacy, messages in the IoV sys-

tem should never reveal their origin. Protecting the

sender’s identity ensures system anonymity. Addi-

tionally, for time-critical safety messages, the system

must prioritize security without exceeding a 1200-

byte limit, according to (3rd Generation Partner-

ship Project, 2017).

Therefore, authentication mechanisms in the IoV

ecosystem must be able to authenticate an entity to

ensure that it really is who it claims to be, guarantee

the anonymity of this entity, and ensure that the over-

head of this entire process does not rise to the point

of spending a signiﬁcant amount of time and compu-

tational resources.

Common authentication mechanisms can be at-

tacked through various techniques, such as network

interference, eavesdropping, and intrusion. These at-

tacks can compromise the IoV system, affecting the

stability and robustness of the system or, in the worst

case, crash the system and cause accidents.

One of the most signiﬁcant vulnerabilities in an

IoV system concerns user authenticity attacks, which

occur through security gaps in the authentication

mechanisms on the network. (Bagga et al., 2020) con-

siders the most recent authentication mechanisms in

the literature and categorizes them based on their al-

gorithms into four types:

• Sybil Attack: The attacker creates some dummy

vehicles around a target vehicle to generate a traf-

ﬁc jam signal while the path is clear enough,

which forces the user to take a different route.

This fake blocking is done using enumerable fake

IDs for a single node, providing a gist of more

than one node.

• Wormhole or tunneling attack: A malicious node

fakes wrong information about its distance from

the target node to make all messages coming from

the sender ﬂow through it. This creates a deadlock

and exposes all messages to the attacker’s node

before ﬂowing over a network.

• Replay attack: The adversary iterates over mes-

sages already transmitted within the network to

reuse them and illegally access services and re-

sources.

• Masquerade attack: The adversary uses the iden-

tity of some authenticated user, evacuating him

from the network to mislead the innocent vehicles

present and spoof them using false and dangerous

messages. The adversary can spoof the receiver

by creating two different senders with the same

identity.

• Message tampering: An attacker modiﬁes the

content of messages to undermine the receiving

entity’s decisions by paralyzing the overall sys-

tem.

In summary, two main factors enable authentica-

tion failures in IoV and open loopholes for security

vulnerabilities and malicious attacks, which are listed

below.

• In vehicular networks, nodes move swiftly and

might enter out-of-coverage areas. In such cases

without Trusted Authority, a vehicle trusts a mes-

sage sender based on hardware compatibility, by-

passing authentication. However, this can result

in message tampering, where a fraudulent node

sends fake messages to a real vehicle. Later,

upon returning to coverage, the legitimate vehicle

might relay this compromised data to applications

(Twardokus and Rahbari, 2022).

• The reliance on a third-party service (Trusted Au-

thority) for the system’s centralized authentication

mechanism, which veriﬁes all entities, poses a risk

to the IoV system. In case of a connection loss to

a Road Side Unit (RSU) or Radio Access Network

(RAN), authentication failure within the network

can occur due to the centralized mechanism.

3 PROTOCOLS AND

AUTHENTICATION

MECHANISMS IN IoV

To address vehicular authentication challenges high-

lighted earlier, some authors have devised secure

techniques, protocols, and authentication methods for

IoV entities. They prioritize both security and mini-

mizing the time and resource overhead in the authen-

tication process.

(Bagga et al., 2020) considers the most recent au-

thentication mechanisms in the literature and catego-

rizes them based on their algorithms into ﬁve types:

WEBIST 2023 - 19th International Conference on Web Information Systems and Technologies

546

Table 1: Notations of times required for unit operations in

authentication.

Notation Unit Operation Time

Tecm Elliptic curve point mult. 17.10 ms

Teca Elliptic curve point add. 4.40 ms

Tmtp Map-to-point operation 44.06 ms

Tbp Bilinear pairing 42.11 ms

Th One-way hash function 0.32 ms

Texp Modular exponentiation 19.20 ms

Tenc/dec Synmetric encr./decryption 0.32 ms

• Lightweight Authentication: These are the sim-

plest and lightest authentication methods, ideal

for the IoT context in general, but require aux-

iliary mechanisms for security. It guarantees se-

curity mainly against sybil attacks and replay at-

tacks.

• Batch Veriﬁcation Based Authentication: A batch

veriﬁcation method optimizes an authentication

protocol by verifying the received signatures to-

gether in batches on the veriﬁer side. Ensures se-

curity against mascarade attacks.

• Privacy-preserving Authentication: These are

mechanisms that focus on preserving user privacy

along with providing authentication. Ensures se-

curity against replay attacks and message tamper-

ing.

• Dual Authentication: These are methods that

meet the demand for authentication by providing

a dual authentication mode. It guarantees security

mainly in sybil and message tampering attacks.

• Hashchain-based Authentication: Mechanisms

that perform authentication based on a hash func-

tion, which are the ones with the least computa-

tional overhead. Mainly solves attacks related to

data modiﬁcation such as message tampering.

We examined the top-performing authentication

mechanisms in each category, focusing on their time

and computational efﬁciency (Bagga et al., 2020). We

quantiﬁed time costs using unit operations and their

approximate durations, as outlined in Table 1.

3.1 Lightweight Authentication

(Vasudev and Das, 2018) presented a two-tier light-

weight authentication model featuring an upper layer

housing a trusted vehicle server equipped with re-

markable storage and computational resources, while

the lower layer consists of vehicles. The protocol

of this model encompasses a conﬁguration phase,

followed by registration and authentication phases

that are seamlessly interconnected, ultimately en-

abling vehicle-to-vehicle (V2V) communication. No-

tably, this protocol brings forth advantageous out-

comes such as reduced battery consumption, mini-

mized communication overhead, lowered implemen-

tation costs, and improved processing time.

• Estimated time = 4Th + 2Tenc/dec = 1.92 ms

• Transfer of message size = 800 bits

3.2 Batch Veriﬁcation Based

Authentication

(Gayathri et al., 2018) introduced an authentication

scheme based on batch veriﬁcation, which offers a

reduced computational overhead by eliminating the

need for certiﬁcates and pairing techniques. This

scheme ensures the security of authentication by ad-

dressing concerns related to tampering, traceability,

anonymity, and revocation.

• Estimated time = 5Th + 7Tecm + 3Teca = 134.5

• Transfer of message size = 960n bits (where n is

the batch scan of n messages)

3.3 Privacy-Preserving Authentication

(Islam et al., 2018) present a conditional privacy-

preserving authentication protocol and group key gen-

eration protocol based on passwords, designed to

minimize memory usage by the Trusted Authority.

This scheme ensures identity maintenance and pro-

vides authentication, forward and reverse secrecy.

Moreover, it offers robust security against replay,

impersonation, modiﬁcation, and ofﬂine password

guessing attacks.

• Estimated time = 10Th = 3.2 ms

• Transfer of message size = 1632 bits

3.4 Dual Authentication

(Vijayakumar et al., 2017) propose a scheme that in-

corporates double authentication to prevent the entry

of malicious nodes into the network. After authen-

tication, the TA message is multicast to all authen-

ticated vehicles. The message multicasting process

utilizes a key calculated through the utilization of the

Chinese Remainder Theorem (CRT). This scheme en-

sures security against various attacks, including Sybil

attacks, spooﬁng, replaying, fabrication, altering mes-

sage moderation, and collusion attacks. Additionally,

the proposal maintains secrecy in both directions, en-

suring conﬁdentiality of communication.

• Estimated time = 2Th + 4Texp + 6Tenc/dec =

79.36 ms

A Decentralized Authentication Model for Internet of Vehicles Using SSI

547

• Transfer of message size = 3168 bits

3.5 Hashchain-Based Authentication

(Cui et al., 2018) introduce a hashchain-based pro-

posal for conditional privacy protection, leveraging

the use of a hash function. While many protocols

rely on bilinear pairing or elliptic curves, this pro-

tocol stands out as the most cost-effective option by

employing simple hash functions. The scheme ex-

cels in privacy preservation, detection of malicious

nodes, ensuring message authenticity and integrity,

maintaining secrecy, and offering protection against

replay, impersonation, and modiﬁcation attacks.

• Estimated time = 5Th = 1.6 ms

• Transfer of message size = 1312 bits

3.6 Summary of the Solutions

Of the studied solutions, the strategy of (Cui et al.,

2018) is the most efﬁcient in terms of security against

malicious attacks and preservation of privacy, at the

same time that it does not increase the overhead con-

siderably, nor does it need auxiliary security mecha-

nisms as in lightweight category protocols.

However, none of the mechanisms studied can

solve the fact that authentication in the IoV ecosys-

tem is centralized and dependent on a third-party ser-

vice in a Trusted Authority. In fact, this is an open

research question according to some studies (Bagga

et al., 2020) (Alladi et al., 2020) (Xi et al., 2022).

One of the potential solutions cited in the liter-

ature is the creation of a blockchain-based authen-

tication mechanism, one of the prominent technolo-

gies that can provide the solution for today’s cen-

tralized infrastructures. The advantages of using the

blockchain-based IoV system is to support decen-

tralization, immutability, transparency, conﬁdentiality

and trust, such as smart grid systems (Musleh et al.,

2019).

Considering this gap in current authentication

mechanisms, we’ve crafted a threat model to analyze

how a malicious entity might exploit out-of-coverage

vehicles and disrupt both centralized and third-party

authentication systems.

4 THREAT MODEL

Our threat model use the Cellular-V2X communi-

cation standard, proposed by 3GPP (3rd Generation

Partnership Project, 2017), which uses PC5 interface

for vehicle-to-vehicle (V2V) communication, and Uu

interface for vehicle-to-network (V2N) communica-

tion. In the proposed scenario, we wish to inves-

tigate potential authentication threats related to the

Advanced Driver-Assistance System (ADAS) appli-

cation, widely used in IoV ecosystem.

The threat model can be seen in Figure 1 and fol-

lows the steps below:

• When vehicles are out of coverage, that is, with-

out access to cellular network, they automatically

switch to using out-of-coverage communication

mode. The trust in this interface is established via

hardware (meaning having a compatible OBU is

sufﬁcient).

• If we introduce a fake node that possesses a com-

patible OBU, it will be capable of transmitting

false messages to a neighboring OBU through

vehicle-to-vehicle communication (V2V). The af-

fected OBU must, at some point, regain coverage

and be reallocated to in-coverage communication

mode.

• The affected OBU will transmit manipulated in-

formation to the eNb RAN through vehicle-to-

network communication (V2N).

• The ADAS application server will capture this

data and might make incorrect decisions based

on it, subsequently transmitting them to the eNb

RAN.

• The eNb RAN will broadcast data with erroneous

decisions to neighboring OBUs via V2N.

Considering the proposed threat model, in the

next section we will present a framework that can be

used to develop a decentralized authentication solu-

tion plan to address this issue.

5 SSI-BASED AUTHENTICATION

& TRUST OVER IP IDENTITY

ARCHITECTURE

Self Sovereign Identity (SSI) is a model of decentral-

ized identity that promotes the control and individual

ownership to entities of their digital identities without

relying on third parties (Soltani et al., 2021). In other

words, the principle of SSI is that each entity owns its

identity, and shares only what is necessary to authen-

ticate itself to the system.

The SSI model has the following assumptions:

• Users must have an independent existence;

• Entities must control their own identities;

• Users must have access to their own data;

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548

Figure 1: Diagram of the proposed threat model.

• Systems and algorithms must be transparent to the

system;

• Identities must be persistent;

• Users must have a consensus on the use of their

identities;

• Disclosure of claims for authentication should be

minimized;

• Users’ rights to privacy must be protected.

Creating an SSI identity model requires three key

elements: a distributed ledger, typically a blockchain,

serving as a decentralized database for recording per-

sistent data; decentralized identiﬁers (DIDs), unique

self-generated IDs ensuring immutability; and veriﬁ-

able credentials (VCs), cryptographically secure dig-

ital versions of credentials presented to veriﬁers.

Within these elements, the SSI model comprises

four entities: the issuer, responsible for issuing veri-

ﬁable credentials; the holder, possessing a decentral-

ized identiﬁer and receiving credentials; the veriﬁer,

tasked with verifying credentials’ validity based on

evidence; and the record of veriﬁable data, facilitat-

ing access to the distributed ledger.

The aim is for each authentication context to have

accredited issuers authorized by the record of veri-

ﬁable data. These issuers provide veriﬁable creden-

tials to holders, who, during authentication, interact

with veriﬁers. Veriﬁers request a proof of credential

and ensure it originates from an authorized veriﬁer,

thereby verifying the user’s authenticity.

The self-sovereign identity model offers secure,

decentralized authentication and privacy preservation.

It achieves this through two methods: selective disclo-

sure, allowing the model to choose which credential

data to reveal to veriﬁers while protecting unneces-

sary information, and Zero Knowledge Proof (ZKP),

a mechanism that allows holders to prove they meet a

requirement to veriﬁers without disclosing supporting

data.

To implement the SSI model in a system, it is

necessary to use an architecture. As such, Trust

Over IP (ToIP) is a layered set of technical protocols

and governance structures that function as a decen-

tralized identity architecture, implementing the Self

Sovereign Identity model to enable trust (Davie et al.,

2019). ToIP is usually used in conjunction with SSI,

as it is a faithful implementation of the model that

brings the advantages of fraud mitigation, process

simpliﬁcation and reduced infrastructure costs.

Trust Over IP has a protocol layer stack that is fol-

lowed in its architecture. It has four layers, which are

divided into two categories: technology and gover-

nance.

The technology stack is divided into the following

layers:

• Layer 1 - Public Utilities: This foundational

layer encompasses deﬁned utilities responsible for

maintaining veriﬁable data records for different

DID methods. These utilities store DIDs with

associated public keys, utilizing diverse decen-

tralized ledger technologies such as blockchains,

distributed ledgers, decentralized ﬁle systems, or

databases.

• Layer 2 - Peer-to-peer communication: Estab-

lishes wallet-to-wallet connections, which persist

until one of the parties gives up. Data wallets

interoperate to exchange DIDs and public keys

without intermediaries.

• Layer 3 - Data Exchange Protocols: This pivotal

layer in the ToIP architecture (depicted in Figure

2) focuses on issuer-veriﬁable credential transac-

tions with holders. Holders get credentials ver-

iﬁed by veriﬁers, leveraging issuer information

accessed through the veriﬁable data record. To

issue a veriﬁable credential, an issuer employs

a private key for digital signing. A data wallet

conducts this transaction and stores the credential

A Decentralized Authentication Model for Internet of Vehicles Using SSI

549

while keeping issuance private. Veriﬁcation oc-

curs using public key cryptography. Credentials

are acquired by holders through issuer requests,

stored in their data wallet. When a veriﬁer seeks

proof of a credential, the data wallet generates

speciﬁc data proof. This proof includes the is-

suer’s DID, enabling veriﬁers to verify the issuer’s

signature by referencing the veriﬁable data record

and the issuer’s public key. The Veriﬁable Data

Registry (VDR) stores issuer DIDs, public keys,

and cryptographic data. The VDR indexes issuer

keys alongside DIDs, allowing sharing of issuer

DIDs for correct public key retrieval. The VDR

does not store actual credential data.

• Layer 4 - Application Ecosystem: Applications

are built using DIDs and veriﬁable credentials to

establish an ecosystem of digital trust.

Figure 2: Entity ﬂow in VC-based identiﬁcation.

The governance stack deﬁnes the following layers:

• Layer 1 - Utility Frameworks: Governance en-

sures the security and integrity of veriﬁable data

records. If a registry is operated as an authorized

network, it will have a government entity respon-

sible for its oversight.

• Layer 2 - Agent Frameworks: Government au-

thorities set security, privacy, usability, accessibil-

ity, and data protection standards for data wallets.

Agent frameworks specify trust programs, mak-

ing it easier for holders to choose data wallets and

agents that meet standards.

• Layer 3 - Credential Frameworks: Credentials al-

low holders to build trust with veriﬁers, as long as

everyone knows the governance rules being fol-

lowed. The governing authority sets policies for

issuers that meet the needs, as well as rules for

registering issuers and veriﬁers.

• Layer 4 - Ecosystem Frameworks: Governance

facilitates trust between members of a digital trust

ecosystem. The governing authority is responsi-

ble for the security, privacy, and accountability of

the system. Auditors can verify that members are

adhering to speciﬁed frameworks.

The set of all layers seen above forms the Trust

Over IP architecture, which implements the self-

sovereign identity model. This framework can be

used to authenticate an IoV system and to solve the

proposed threat model, as shown in the next section.

6 SSI-BASED VEHICULAR

AUTHENTICATION MODEL

As seen in the previous section, the use of a model

based on Self Sovereign Identity and based on the

Trust Over IP architecture can favor the decentral-

ization of a system, as well as avoid dependence on

third-party services, mainly due to the presence of the

distributed ledger, based on blockchain, in the process

of identifying entities.

The same concept can be applied to ADAS sce-

nario previously discussed in threat model, with re-

gard to the authentication of vehicles and other enti-

ties in the vehicular network. The proposed authenti-

cation scheme can be seen in Figure 3.

The ecosystem surrounding ADAS makes use of a

decentralized authentication based on Self-Sovereign

Identity (SSI).

A new vehicle, when in-coverage, upon register-

ing with the ADAS application, requests a Veriﬁable

Credential (VC) from the eNb RAN with access to the

ADAS application. The eNb RAN has a DID and is

coupled with a UE that acts as an issuer and veriﬁer of

credentials for the applications it has access to. The

eNb RAN creates a new VC, registers the key pair

with its DID in the Veriﬁable Data Registry (VDR),

which is a distributed ledger, and returns the VC to

the vehicle to be stored in its data wallet.

When this vehicle is out-of-coverage, it might re-

ceive a V2X message from a fake node with identi-

cal hardware characteristics (therefore, accepted by

C-V2X architecture). The vehicle stores the message

content with a ”pending” ﬂag along with the entity’s

VC.

Upon returning to in-coverage mode, the vehicle

must update the ADAS system through V2N mes-

sages. In one of these messages, the vehicle must send

the received information from the fake node, marked

as pending, along with the sender’s information and

their VC.

The eNb RAN must access the VDR to conﬁrm

the authenticity of the message. When it decrypts the

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550

VC with the public key, this will result in a nonexis-

tent DID. As a result, the eNb RAN rejects the mes-

sage content, and it does not reach the ADAS applica-

tion server, thus avoiding future decision-making er-

rors that the server could cause.

伀甀琀ⴀ漀昀ⴀ挀漀瘀攀爀愀最攀

䤀渀ⴀ挀漀瘀攀爀愀最攀

Figure 3: Authentication scheme in SSI-based IoV ecosys-

tem for ADAS.

Although this is an open study, some research on

the use of SSI in the IoV ecosystem has already been

carried out, even if not in the context of authentica-

tion. (Theodouli et al., 2020) proposes an identity and

trust management framework based on SSI to authen-

ticate a software vendor API with the vehicle, aiming

to improve the software update of IoT devices em-

bedded in the vehicle. Even though it is not a study

directly related to vehicular authentication in the IoV

ecosystem, the research proves the possibility of us-

ing all of this framework in the context of vehicular

networks.

7 CONCLUSION & FUTURE

WORK

In brief, while the Internet of Vehicles (IoV) ecosys-

tem is commonly regarded as secure, it contains au-

thentication vulnerabilities that expose users to poten-

tial malicious attacks. Although researchers have de-

veloped authentication mechanisms and protocol so-

lutions to address these concerns, none of the evalu-

ated solutions effectively tackle the issue of maintain-

ing vehicle trust when out of network coverage, nor

address the centralized nature of the default authen-

tication mechanism. These factors introduce risks of

authentication failures, as detailed in the threat model.

To confront this challenge, we proposed to apply

the Self-Sovereign Identity (SSI) decentralized iden-

tity model in conjunction with the Trust Over IP ar-

chitecture. This integration offers a compelling ap-

proach for implementing decentralized identiﬁcation

in diverse contexts. Additionally, we have discussed a

proposed solution that incorporates an SSI-based au-

thentication scheme within the IoV ecosystem, with a

particular focus on ADAS applications. This solution

provides a foundation for future research endeavors to

explore and enhance.

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