ArticlePDF Available

# Lightweight Mutual Authentication Protocol for V2G Using Physical Unclonable Function

Authors:
• Thapar Institute of Engineering and Technology (Deemed University), Patiala (Punjab), India

## Abstract

Electric vehicles (EVs) have been slowly replacing conventional fuel based vehicles since the last decade. EVs are not only environment-friendly but when used in conjunction with a smart grid, also open up new possibilities and a Vehicle-Smart Grid ecosystem, commonly called V2G can be achieved. This would not only encourage people to switch to environment-friendly EVs or Plug-in Hybrid Electric Vehicles (PHEVs), but also positively aid in load management on the power grid, and present new economic benefits to all the entities involved in such an ecosystem. Nonetheless, privacy and security remains a serious concern of smart grids. The devices used in V2G are tiny, inexpensive, and resource constrained, which renders them susceptible to multiple attacks. Any protocol designed for V2G systems must be secure, lightweight, and must protect the privacy of the vehicle owner. Since EVs and charging stations are generally not guarded by people, physical security is also a must. To tackle these issues, we propose Physical Unclonable Functions (PUF) based Secure User Key-Exchange Authentication (SUKA) protocol for V2G systems. The proposed protocol uses PUFs to achieve a two-step mutual authentication between an EV and the Grid Server. It is lightweight, secure, and privacy preserving. Simulations show that the proposed protocol performs better and provides more security features than state-of-the-art V2G authentication protocols. The security of the proposed protocol is shown using a formal security model and analysis.
1
Lightweight Mutual Authentication Protocol for
V2G Using Physical Unclonable Function
Gaurang Bansal, Member, IEEE, Naren, Vinay Chamola, Member, IEEE, Biplab Sikdar, Senior Member, IEEE,
Neeraj Kumar, Senior Member, IEEE and Mohsen Guizani, Fellow, IEEE
Abstract—Electric vehicles (EVs) have been slowly replacing
conventional fuel based vehicles since the last decade. EVs are
not only environment-friendly but when used in conjunction
with a smart grid, also open up new possibilities and a Vehicle-
Smart Grid ecosystem, commonly called V2G can be achieved.
This would not only encourage people to switch to environment-
friendly EVs or Plug-in Hybrid Electric Vehicles (PHEVs), but
also positively aid in load management on the power grid, and
present new economic beneﬁts to all the entities involved in
such an ecosystem. Nonetheless, privacy and security remains
a serious concern of smart grids. The devices used in V2G
are tiny, inexpensive, and resource constrained, which renders
them susceptible to multiple attacks. Any protocol designed for
V2G systems must be secure, lightweight, and must protect the
privacy of the vehicle owner. Since EVs and charging stations are
generally not guarded by people, physical security is also a must.
To tackle these issues, we propose Physical Unclonable Functions
(PUF) based Secure User Key-Exchange Authentication (SUKA)
protocol for V2G systems. The proposed protocol uses PUFs to
achieve a two-step mutual authentication between an EV and the
Grid Server. It is lightweight, secure, and privacy preserving.
Simulations show that the proposed protocol performs better
and provides more security features than state-of-the-art V2G
authentication protocols. The security of the proposed protocol
is shown using a formal security model and analysis.
Index Terms—authentication, security, smart grid, V2G, PUF
I. INTRODUCTION
Electric Vehicles’ batteries enable the functionality of V2G
networks. The purpose of V2G is to manage the energy trading
for battery-poweblack electric vehicles as well as the power
grid. This is necessary in order to use the grid’s energy more
efﬁciently [1]. The electrical energy stored in the EV batteries
can serve as a source for the power grid and other energy
deﬁcient EVs. When the load on the grid is high, the energy
stored in the EVs’ batteries could be used to pump power into
the grid. On the other hand, when the load on the grid is low,
the excess power in the grid could be used to charge the EV
batteries and avoid wastage [2]. V2G networks could also be
used for power regulation [3] or for storing power generated by
Gaurang Bansal, Naren and Vinay Chamola are with the Department of
Electrical and Electronics Engineering, BITS-Pilani, Pilani Campus, India
333031 (e-mail: h20140128@pilani.bits-pilani.ac.in, f2015547@pilani.bits-
pilani.ac.in, vinay.chamola@pilani.bits-pilani.ac.in).
Biplab Sikdar is with the Department of Electrical and Computer Engi-
neering, National University of Singapore, Singapore 119077 (e-mail: bsik-
dar@nus.edu.sg).
Neeraj Kumar is with the Department of Computer Science, Thapar
University, Patiala, India 147004 (e-mail: neeraj.kumar@thapar.edu).
Mohsen Guizani is with the Department of Computer Science, Qatar
University, Doha 2713, Qatar (e-mail: mguizani@ieee.org ).
Digital Object Identiﬁer: XXXXXXXXXXXX
CommunicationFlow
ElectricityFlow
ChargingEV
DischargingEV
Charging
PowerGrid
Discharging
Aggregator
Aggregator
Aggregator
Fig. 1: System model
renewable sources such as wind power [4]. Thus, nowadays,
V2G for smart grids presents great practical applications.
The global demand for electrical power is predicted to climb
82% by the year 2030. Therefore, Power grids are aiming
to reduce the number of additional generators required. They
employ demand-response techniques [5] to reduce power con-
sumption and increase efﬁciency. Although such techniques
offer many beneﬁts, security and privacy issues remain to be
signiﬁcant downsides [6, 7]. A lot of information is communi-
cated during energy exchange between a vehicle and a service
provider. However, an adversary could compromise this ﬂow
of information, either by tampering with it or capturing it
entirely [8]. This could lead to unfair or imbalanced energy
transactions between the two parties. Moreover, the victim’s
information (that could be captured) may be used in criminal
activities and targeted advertisements. The devices used in
V2G are inexpensive, small, and simple [9]. The EVs are
usually parked in locations which are easy to access. This
means that an adversary could easily capture the V2G devices
on these vehicles. Therefore, it is important to make V2G
entities/devices secure against physical attacks. For instance,
an adversary could access security keys stored in the device
memory and initiate various attacks. Physical Unclonable
Functions (PUFs) have emerged as a promising solution for
protection against physical attacks. PUFs eliminate the need
to store secret keys in the devices’ memory and rely on the
exchange of challenge-response pairs. The challenge-response
mechanism of PUFs exploits the inherent fabrication or man-
ufacturing process variabilities involved in making integrated
2
circuits (ICs) [10]. The response or output of a PUF depends
on both the input as well as the physical microstructure of
the device [11]. The physical randomness induced through the
fabrication process variations makes each PUF device unique,
i.e., two identical copies can never be made.
In V2G systems, an aggregator is a charging station which
acts as a mediator between the EVs and the power grid. Se-
cure communications in such scenarios require authentication
between the EVs and the aggregator, between aggregator and
the grid, and between EVs and the grid as well. The proposed
Secure User Key-Exchange Authentication (SUKA) protocol
achieves the authentications mentioned above using a two-
stage process. Using pseudonym IDs (PID), the identity of
the vehicle is masked to protect the vehicle owner’s identity
and location. Two different session keys are established in
SUKA, one between the aggregator and the grid, and another
between any EV and the aggregator. These session keys are a
function of the PUFs installed on the aggregator and the EV,
respectively. This ensures the secrecy of the communication
and eliminates the need to store any secret keys in the memo-
ries of the EVs and the aggregator. The proposed protocol uses
simple cryptographic operations, which makes it lightweight
and energy efﬁcient. The number of message exchanges is also
limited, which results in a lower communication overhead. The
major contributions of this paper are highlighted below:
We propose a security scheme, SUKA, for V2G appli-
cations where both the mobile EVs and the stationary
aggregators are provisioned with PUFs for secure com-
munication.
SUKA puts the safety of the EV and its owner as top
priority by ﬁrst achieving authenication of the aggregator
with the power grid server. Only if this is achieved,
the aggregator will be able to accommodate the EV.
Our scheme ensures security even in the case of a
compromised aggregator.
SUKA ensures mutual authentication, identity protection,
message integrity and is tolerant to man-in-the-middle
(MITM) attacks, impersonation attacks, replay attacks
and node tampering attacks without having to store any
secret keys in the EVs or the stationary aggregators. The
records of EVs cannot be tracked even if the EVs and/or
the stationary aggregators are compromised.
SUKA establishes different session keys between the EV
and the aggregator, and between aggregator and the power
grid server. These session keys change randomly in each
round of authentication and cannot be accessed by an
adversary even if it gains physical access to both the EV
and the aggregator.
The rest of this paper is organized as follows. Section II
discusses the related work in V2G systems. Section III presents
a brief introduction to PUFs, the network model, security
goals, assumptions for the V2G system, and the notations used
in our paper. Section IV presents our MA protocol (SUKA).
Section V presents a formal security analysis of the proposed
protocol. We analyze the performance of our protocol and
compare it with state-of-the protocols in Section VI and ﬁnally
conclude the paper in Section VII.
II. RE LATE D WORK
Kempton and Tomi´
c [12] ﬁrst conceived the idea of V2G
in 2004. Before the protocols for V2G networks could be
developed, the structure of a V2G network had to be well
deﬁned, and the impact of V2G on the power grid had to be
analyzed. This work was carried out by the authors in [13,
14, 15, 16, 17]. Privacy, secure communication, and efﬁciency
are among the most important aspects of a V2G protocol
[18]. Privacy preservation in V2G environments has received
considerable attention in the existing literature [19, 20, 21, 22].
Yang et al. have presented a protocol P2in [19] which
achieves privacy for individual electric vehicles (EVs) and the
rewarding scheme which is crucial for proper implementation
of V2G. Liu et al. present their scheme, AP 3A, which is
capable of identifying whether an EV is in its home or visiting
network [20]. AP 3Acommunicates the aggregated power
status of the vehicles connected to an aggregator instead of
revealing individual power status, thus achieving privacy for
each EV. Liu et al. have presented a scheme which identiﬁes
the different roles played by an individual EV, i.e., customer,
storage or generator [22]. In each role, their scheme ROP S
addresses different privacy concerns. Tsai and Lo achieve
mutual authentication and identity protection with the use
of one private key which is given by a third-party anchor.
This enables the smart-meters to quickly authenticate with the
service provider. Abdallah and Shen propose a computation-
ally less intensive privacy-preserving scheme in [24]. They
identify that the authentication of EVs in the V2G system is
speciﬁcally problematic. Therefore, the power grid takes the
responsibility of ensuring the conﬁdentiality and integrity of
the communication. By reducing the number of exchanged
messages, they achieve less overhead. Odelu et al. present
a secure authenticated key agreement scheme [25] under the
grids. Shen et al. propose a privacy-preserving key agreement
protocol for V2G networks in [26]. Their protocol ensures
security by the use of a session key and ensures privacy using
a self-synchronization mechanism.
Protocols for authentication in V2G environments have been
proposed in [27, 28, 29]. Saxena and Choi have presented an
authentication scheme for large V2G networks where vehicles
move from their home network to other networks as visitors
[30]. They propose a mutual authentication scheme which pro-
tects against impersonation, key-based and data-based attacks.
Tao et al. have presented capacity-aware protocol AccessAuth
in [31] which takes into consideration the capacity limitations
of each V2G network domain, of the EVs, and the mobility of
the EVs for admission control. Based on prior information of
trust between V2G network domains, they present a high-level
authentication model and procedure to ensure that only autho-
rized entities conduct the sessions. Gope and Sikdar have used
one-way noncollision hash functions to propose a lightweight
mutual authentication protocol [32]. Fouda et al. have pro-
posed a lightweight message authentication scheme in [33].
In their scheme, smart meters at different levels in the smart-
grid achieve mutual authentication among themselves, and a
shared session key is established. They achieve lightweight
3
message authentication using this shared session key along
with a hash-based authentication code mechanism. Although
this scheme was presented for smart grid communications, it
can very well be extended to V2G networks.
While many privacy-preserving, lightweight mutual au-
thentication, and key establishment protocols exist for V2G
systems, none of them provide all the required security and
privacy features along with protection against all types of
attacks, especially protection against physical attacks. If a
protocol does provide perfect security, then it either requires
resource-heavy hardware or is computationally complex.
III. NOTATIO NS
Table I lists the notations used in this paper and their
descriptions.
TABLE I: Notations
Notation Description
V, IDVVehicle and its ID
M, IDMAggregator(mediator) and its ID
GGrid Server
kConcatenation operator
XOR operation
FA public non-linear function
{Msg}k
Message Msg is encrypted
using key k
MsgP2Q
Message Msg is sent from
V2G entity Pto Q
MAC(X)Message authentication code
(MAC) of X
NA, NB, NC
NI, NO, NV
Nonces generated
at different stages
(C, K),(C0, K0)
(C00, K 00),(C#, K#)Challenge-response pairs of PUF
IV. PRELIMINARY BACKG ROU ND
A PUF is based on a unique physical property of a device
which is unique as the biometrics of a human. The distin-
guishing attribute of a PUF is that it relies on a physical basis,
making it impossible to reproduce a PUF using cryptographic
primitives. Additionally, the term “physical unclonable” in-
dicates that it is computationally infeasible or difﬁcult to
produce a physically identical PUF [34]. By using PUFs in
an interconnected system such as IoT or V2G systems, every
single device can have its own unique “ﬁngerprint” which
cannot be cloned or reproduced [35]. A PUF behaves like
a mathematical function whose input (challenge) and output
(response) are both in the form of a string of bits. A PUF
function can be represented as:
K=P U F (C)(1)
where the challenge Cis given as input and response, Kis
the corresponding output to that challenge.
PUFs are designed deliberately so that the response to a par-
ticular challenge depends on the individual physical disorder
present in the PUF. Therefore, each PUF response is not only
a function of the challenge applied, but also a function of its
physical disorder. While it is clear that different challenges to
the same PUF will give different responses, PUFs also show
the following unique characteristics with respect to their input
Cand output K:
1) If an input Cis given to the same PUF many times,
it produces the same response Kwith a very high
likelihood.
2) If the same input Cis given to different PUFs, the
responses obtained from each PUF differ greatly from
each other with a very high likelihood.
The characteristics mentioned above apply to non-ideal PUFs.
Due to environmental and circuit noises, a non-ideal PUF
cannot guarantee the exact same response Kfor an input C,
hence the word ‘high likelihood’ was used above. There will
always be some bit-errors depending on the type of PUF used.
Although error correcting techniques such as fuzzy extractors
could be used in order to combat this problem, that would
result in unnecessary overhead for the MA process. Therefore,
the PUFs employed in the proposed protocol have to be ideal
in nature, i.e., without any bit errors. This would ensure 100%
availability of the V2G system.
In the past few years, several types of ideal PUFs have
been developed, which ensure 0% Bit-Error-Rate (BER) over
a wide range of temperature and voltage ﬂuctuations. The
authors of [36] have been able to achieve 0% BER in SRAM
PUFs, the authors of [37] achieved 0% Bit-Error-Rate (BER)
design with their VIA-PUF. Several other works [38, 39, 40]
have also been able to achieve 0% Bit-Error-Rate (BER).
These ICs are very small, measuring just a few millimeters in
dimensions and require just a few Volts (1-5 V) to operate,
which makes them ideal for the V2G scenario. By using
Ideal PUFs with on-board computers in V2G systems, stable
key generation can be achieved without the need for any
dedicated error correction hardware or software components
and thus promise lightweight and high-security performance
when used in V2G systems. However, ideal PUFs have been
developed very recently and have only been fabricated for
research purposes. Techniques to incorporate these PUFs on
the on-board computers of V2G systems, or System-on-Chip
designs (SoCs) with built-in PUFs do not exist at present.
Research and development of such ideal-PUF based solutions
for V2G systems to properly implant PUFs in the on-board
computers of EVs or aggregators can be considered as future
work, but further discussion on this topic is beyond the scope
of this paper.
V. SYSTEM MODEL
A. Network Model
Figure 1 depicts the system model. This model consists of
three entities: EVs, aggregators (or mediators), and the grid.
An aggregator is a charging/discharging station where many
vehicles can come to charge/discharge their batteries. It acts as
a mediator between the EVs and the grid. EVs and aggregators
have limited resources, while the grid has signiﬁcantly larger
resources. Aggregators and EVs have similar capabilities, but
4
aggregators have slightly larger memory and computation
power. As can be seen in Figure 1, multiple vehicles are
connected to an aggregator, and multiple aggregators connect
to the power grid. Our objective is to develop a mutual
authentication (MA) protocol between EVs and the grid. The
device on every vehicle and aggregator is equipped with a PUF.
Since a vehicle does not communicate directly with the grid,
to achieve MA between these two non-communicating parties,
all the intermediary nodes must be authenticated. Thus, MA
between the grid and a vehicle can be divided as MA between
the aggregator and the grid along with MA between the vehicle
and the aggregator. We assume here that there is no shared
key between a vehicle and its corresponding aggregator or
between an aggregator and the grid. Whenever a new vehicle
wants to register on the network, its challenge-response pair is
stored in the grid server. The grid is the only trusted authority,
and therefore, challenge-response pairs for all vehicles are
stored only in the grid. Nothing else is assumed in further
communication.
The server on the power grid starts with a set of initial
challenge-response pairs, (C, K)for each aggregator and
(C00, K 00)for each EV. The aggregator acquires this initial
set (C00, K 00)for the EV once the aggregator itself mutually
authenticated with the grid server. To set up a new aggregator
in a location or to deploy a new vehicle or on the roads,
initialization involves the initial set (C00, K 00)/(C, K)to be
sent to the power grid server using a secure channel. This
initialization can be done using a time-based one-time pass-
word algorithm (TOTP) [41] and an operator using a password.
After this exchange, the aggregator or vehicle can function
on its own without needing any operator or secure channel.
The grid server stores the actual identity IDM/I DV, and their
corresponding challenge response pairs, (C, K)and (C, K”),
for each aggregator and vehicle, while the aggregators or
vehicles themselves do not store anything. At the end of the
protocol, the ID of the EV, IDV, is replaced with pseudo-
identities for further exchanges.
We assume that an adversary can get hold of any commu-
nication that is happening between the EV and the aggregator
or the aggregator and the grid. An adversary has the power
to change, manipulate, and hide the data. It can inject new
packets, store the old messages, initiate a session, or pretend
to be a valid device. The objective of an attacker or adversary
is to gain access to the grid without being noticed. Adversaries
may be EV owners who want to exploit the V2G system in
order to cheat the service provider to charge their vehicles
for free or to get more money from the service provider when
they supply power to the grid from their EV. They may also be
rogue or unauthorized aggregators set up to cheat EV owners
by charging very high prices or not paying the EV owner
for the power they acquire. Such aggregators may also be
selling personal information of the EV owner to third-party
be criminals who may want to track the location/behaviour of
an EV owner by recording the aggregators visited by the EV
or criminals who may want to authenticate with the aggregator
with some other EV’s identity in order to escape the payment.
If an unauthorized or potentially dangerous entity manages
to authenticate with the grid server, it may disrupt energy
transactions and cause economic damage. Therefore, this paper
proposes a MA protocol that is resistant to various attacks such
as replay attacks, man-in-the-middle attacks, impersonation
attacks, etc.
B. Security Goals
1) Conﬁdentiality: The energy transaction data must not
be visible to any unauthorized entity. For this, commu-
nication must be secret throughout, i.e., end-to-end. If
an unauthorized entity from either within the system
such as vehicles authenticated with other aggregators
or the currently connected aggregator gains access to
the messages which contain energy transaction details,
it must be impossible to make sense of it.
2) Message Integrity: It must be possible for the smart
grid server to verify if the message it receives from
the aggregator has been tampered with or compromised.
Since EVs and the grid server do not communicate
directly, the aggregator must also be able to do the same
for the messages received from the EVs.
3) Identity privacy: It must be impossible for an unautho-
rized entity to get hold of any personal information of the
vehicle owner of an EV. Even if an unauthorized entity
eavesdrops on the data exchanged within the V2G system,
it must not be able to ﬁgure out that the data is from a
particular vehicle or that two transactions are from the
same vehicle.
4) Authentication: Before any energy transaction can be
made, the aggregator must be authenticated with the grid
server. The aggregator must also be authenticated with
the vehicle, thus preventing any false energy exchanges.
C. Assumptions
The assumptions made in this paper are as follows:
PUF is a small hardware component that is present with
each participating device and is unique.
The communication between a device and its PUF is
secure and tamper-proof.
The grid is considered as a trusted authority and has
sufﬁcient resources. On the other hand, EVs have limited
resources in terms of memory and computation power.
VI. PRO PO SE D MUT UAL AUTHENTICATION PROTOCOL
This section presents the proposed mutual authentication
protocol between the vehicle and the grid. Mutual authentica-
tion between the vehicle and the grid can be divided as mutual
authentication between:
Aggregator and grid.
Vehicle and aggregator.
A. Mutual Authentication Between Aggregator and Grid
Server
1) When a vehicle wants to make a transaction, the aggre-
gator must authenticate the vehicle. The vehicle sends its
ID (IDV) along with a randomly generated nonce (NV)
to the aggregator with MsgV2M={IDV, NV}.
5
EV Aggregator
.
IDV,NV
.
.
Grid
Server
IDM,NI
SelectIDM
GeneratenonceNI
Checkif(IDM)exists?
Checkif(NI)isfresh?
Corresponding(C,K)frommemory
GeneratenonceNB
M1=NIXORF(K0,NB)
M2=NBXORF(K1,M1)
M3=M1XORF(K2,M2)
......
Mm-1=Mm-3XORF(Km-2,Mm-2)
Mm=Mm-2XORF(Km-1,Mm-1)
M=(Mm||Mm-1)XORKm
N=mXORK0
C,M,N,MAC(IDM||M||m||NB)
Evaluate K = PUF(C)
m=NXORK0
CalculateMmandMm-1usingKmXORM
Mm-2=MmXORF(Km-1,Mm-1)
......
CalculateNBandverifyMAC
Generate(C',K')pairandnonceNC
M'i=K'iXORKi
M''=M'0||M'1||.......M'm
N'=NCXORK0
SessionKey(Sk)=F(K0,NB)XORF(K0,NC)
C',M'',N,N',MAC(IDM||M''||m||NC||Sk)
Calculate:
NCusingN'andK0
K'iusingM'iandKi
VerifyMAC
Store(C',K')
SessionKey(Sk)=F(K0,NB)XORF(K0,NC)
EncodeusingSessionKey
Fig. 2: Mutual authentication between aggregator and the power grid server.
2) The aggregator generates another random number, i.e.,
nonce (NI), and sends it along with its ID (IDM) to the
grid server with MsgM2G={IDM, NI}.
3) The ﬁrst stage of our protocol begins with the aggre-
gator authenticating with the power grid server. This is
shown in Figure 2. The grid server receives a message
(MsgM2G={IDM, NI}) from the aggregator. It checks
if IDMexists in its memory and whether NIis fresh. If
either of the conditions fails, the authentication request
initiated by aggregator is terminated. Using IDM, it ﬁnds
the corresponding challenge-response pair (C, K)(Kcan
be split into m+ 1 sub-strings) in its memory:
K= (K0, K1, K2,· · · , Km)
It also generates a nonce (NB). To encrypt the message,
the server uses a block-based encryption mechanism with
mrounds. Let Fbe any non-linear function which is
public to everyone. Thus, even an adversary can know
what Fis. It can be veriﬁed that the security of the
protocol does not depend on F. The grid server then
computes the following:
M1=NIF(K0, NB)
M2=NBF(K1, M1)
Mi=Mi2F(Ki1, Mi1),3i<m
Mm=Mm1F(Km1, Mm1)
M= (Mm1||Mm)Km
N=mK0
4) The grid server sends C,M,Nalong with a MAC
(message authentication code) to the aggregator IDM,
as shown just after the ﬁrst block under grid server in
6
c
EV Aggregator
IDV,NV
.
Grid
Server
IDM,NI
SelectIDM
GeneratenonceNI
DecryptwithSk
GeneratenonceNA
D1=NVXORF(K''0,NA)
D2=NAXORF(K''1,D1)
D3=D1XORF(K''2,D2)
......
Dm-1=Dm-3XORF(K''m-2,Dm-2)
Dm=Dm-2XORF(K''m-1,Dm-1)
D=(Dm||Dm-1)XORK''m
P=mXORK''0
E([C'',K''],Sk)
EvaluateK''=PUF(C'')
m=PXORK''0
CalculateDmandDm-1usingK''mXORD
Dm-2=DmXORF(K''m-1,Dm-1)
......
CalculateNAandverifyMAC
Generate(C#,K#)pairandnonceNO
P'=NOXORK''0
SessionKey(Sk2)=F(K''0,NA)XORF(K''0,NO)
PIDV=IDVXORK''0
P',E([C#,K#,PIDV],Sk2)
MAC(IDV||m||NO||Sk2)
Calculate:
NOusingP'andK''0
SessionKey(Sk2)=F(K''0,NA)XORF(K''0,NO))
VerifyMAC
C'',D,P,MAC(IDV||D||m||NA)
Mutual Authentication Established
Session Key (Sk)
IDV,NV
SessionKeyEstablished
DecryptwithSkfollowedbySk2
toobtainandstore(C#,K#)andPIDV
E([E([C#,K#,PIDV],Sk2),Sk2],Sk)
Checkif(IDV)exists?
Checkif(NV)isfresh?
EncryptCorresponding(C'',K'')withSk
Fig. 3: Mutual authentication between electric vehicle and the aggregator.
Figure 2. The MAC is used to verify a few security
essentials. The ﬁrst parameter in the MAC is to identify
the correct aggregator. Data integrity is ensured by the
second and third parameters. The freshness of the source
(grid server in this case) is identiﬁed by NB, which is
the last parameter. We use the same approach in the later
stages of the protocol as well.
5) On receiving the message from the grid server, aggregator
IDMgenerates the key Kas given in (1) using received
challenge Cas the input to its PUF. Then, the aggregator
calculates m, as shown below:
m=NK0.(2)
6) Using mand K, it then ﬁnds NBas shown in the
following equations by applying the same transformations
used in encryption operations of step 3.
7
Mm1||Mm=MKm
Mi2=MiF(Ki1, Mi1),3i<m
NB=M2F(K1, M1)
NI=M1F(K0, NB).
The aggregator uses the MAC to verify the source of the
message, checks if its integrity has been compromised,
and determines whether the message is fresh or not.
If it fails to verify these security traits, authentication
is terminated by the aggregator. Else, a nonce NCis
generated. For future authentication, it generates a new
random challenge-response pair (C0, K0)using its PUF
and split K0into m+ 1 sub-strings:
K0= (K0
0, K0
1, K0
2,· · · , K0
m).
It then calculates M0
i,M00,N0and session key Skas
follows:
M0
i=K0
iKi
M00 =M0
0||M0
1||.....||M0
m
N0=NCK0
Sk=F(K0, NB)F(K0, NC).
7) Then, the aggregator sends C0,M00,N,N0, as well as the
MAC to the grid server. Next, it erases interim variables
from its memory. This time the MAC includes a ﬁfth
parameter which is the session key, Sk. This ensures that
both aggregator and grid server have the same session
key.
8) On receiving the message from the aggregator, the grid
server calculates NCusing N0and K0, obtains K0using
Mand K, and stores (C0, K0)in its memory. Then,
it calculates the session key, Skand veriﬁes the MAC.
With the session key now established, MA between an
aggregator and the grid server is complete.
NC=N0K0
K0
i=M0
iKi
Sk=F(K0, NB)F(K0, NC)
B. Mutual Authentication between Vehicle and Aggregator
Mutual Authentication between Vehicle and Aggregator of
the proposed protocol is quite similar to Mutual Authentication
between Aggregator and Grid. Therefore, we only discuss the
key differences here, while the entire protocol is presented in
Fig. 3. The protocol for aggregator and grid server establishing
a session key Skbetween themselves, is shown as a small
box in Figure 3. The key difference of this stage is that, in
the ﬁrst block under the EV, as shown in Figure 3, the EV
then calculates its new pseudonym or pseudo-ID, P IDV, to
be used the next time it wants to authenticate. This ensures
identity protection because an adversary will not be able to
ﬁgure out whether a previous transaction belonged to the same
EV or not. The pseudo-ID of the EV is also sent to the grid
server in message MsgM2G.
P I DV=IDVK00
0
MsgM2G=E([E([C#, K#, P I DV], Sk2), Sk2], Sk)
The grid server decrypts message MsgM2Gwith Skto ob-
tain E([C#, K#, P I DV], Sk2)and Sk2. Next, it decrypts
E([C#, K#, P I DV], Sk2)with Sk2to obtain and store in
its memory the new challenge-response pair of the vehicle
(C#, K#), and the new pseudo-ID P I DV. If any hijacker
tries to tamper with the aggregator device, its PUF will be
destroyed and the protocol will not proceed to this stage.
Therefore, the adversary will not be able to access the new
pseudo-ID.
VII. SECURITY ANALYSIS
In this section, we formally show that our MA protocol is
secure. We use Mao and Boyd logic [42] which is extensively
used for security analysis of protocols. In our analysis, we
denote vehicle IDV, aggregator I DM, and the grid server by
V,M, and G, respectively.
A. Mao-Boyd Logic
The basic building blocks of Mao-Boyd Logic listed below
are necessary to understand the protocol veriﬁcation.
1) A B:Abelieves Bis legitimate and that it may
function correspondingly.
2) A
K
|B:Aencrypted Busing key K.
3) AK
/ B:Asees Busing decipherment key K.
4) AK
B:Kis a valid shared key between entities Aand
B.
5) #(N): Nonce Nis new and fresh.
6) sup(P):Pis a credible and reliable entity.
7) A/ kM: Entity Adoes not have access to message M.
In our proof we use several inference rules of Mao and Boyd
logic which are listed in Table II. In the rules, ‘V’ represents
the logical AND of two statements. If P,Qare statements,
and the inference of their logical AND is statement R, it is
written in Mao and Boyd logic as R
PVQ.
B. Security Analysis
First, let us consider the MA between an aggregator and
the power grid server. We ﬁrst prove the statement “Mis
convinced NBis a valid shared key between Mand G”. The
following proof is summarized in Mao and Boyd logic in Fig.
4a. The challenge-response pair of M,(C, K)is stored in
G, therefore it can be said that “Mand Ghave a well-kept
secret K”. In Mao and Boyd logic, this is written as shown in
equation (i). Using K, the aggregator is able to decipher the
variable Min message 3 of the protocol and obtain NBand
NI. Therefore, “Msees NIwith decipherment key K” which
is (ii) and “Msees NBwith decipherment key K” which is
(vi).
M M K
G(i)
MK
/ NI(ii)
8
M M
NB
G
M{M,G}c/kNB
M G {M,G}c/kNB
M G MK
G
M#(NI)V
M G
K
|NI
M M K
GVMK
/NI
VM G {M}c/kNBV
M G
K
|NB
M M K
GVMK
/NB
VM sup(G)
VM sup(G)V
M#(NB)
M#(NI)VM/NIRNB
MK
/NIRNB
(a) Proof for: “Mis convinced that NBas a valid shared key between Mand G”.
M M
NC
G
M{M,G}c/kNC
M M K
GVM Gc/kNCVM
K
|NC
VM#(NC)
(b) Proof for: “Mis convinced that NCis
a valid shared key between Mand G”.
G M
NC
G
G{M,G}c/kNC
G M {M ,G}c/kNC
G M M K
G
G#(NB)V
G M
K
|NB
G M K
GVGK
/ NB
VG M {G}c/kNCV
G M
K
|NC
G M K
GVGK
/ NC
VG sup(M)
V
G#(NC)
G#(NB)VG/NBRNC
GK
/ NBRNC
(c) Proof for: “Gis convinced that NCis a valid shared key between Mand G”.
G M
NB
G
G{M,G}c/kNB
G M K
GVG Mc/kNBVG
K
|NB
VG#(NB)
(d) Proof for: “Gis convinced that NBis
a valid shared key between Mand G”.
G MK0
G
G{M,G}c/kK0
G M {M ,G}c/kK0
G M M K
G
G#(NB)V
G M
K
|NB
G M K
GVGK
/ NB
VG M {G}c/kK0V
G M
K
|K0
G M K
GVGK
/ K0
VG sup(M)
V
G#(K0)
G#(NB)VG/NBRK0
GK
/ NBRK0
(e) Proof for: “Gis convinced that K0is a valid shared key between Mand G”.
M MK0
G
M{M,G}c/kK0
M M K
GVM Gc/kK0VM
K
|K0
VM#(K0)
(f) Proof for: “Mis convinced that K0is
a valid shared key between Mand G”.
Fig. 4: Proof for authentication between aggregator and power grid server
V V
NA
M
V{V,M }c/kNA
V M {V ,M}c/kNA
V M V K00
M
V#(NV)V
V M
K00
|∼NV
V V K00
MVVK00
/ NV
VV M {V}c/kNAV
V M
K00
|∼NA
V V K00
MVVK00
/ NA
VV sup(M)
VV sup(M)V
V#(NA)
V#(NV)VV /NVRNA
VK00
/ NVRNA
(a) Proof for: “Vis convinced that NAas a valid shared key between Vand M”.
V V
NO
M
V{V,M }c/kNO
V V K00
MVV Mc/kNOVV
K00
|∼NO
VV#(NO)
(b) Proof for: “Vis convinced that NOis
a valid shared key between Vand M”.
M V
NO
M
M{V,M }c/kNO
M V {V ,M}c/kNO
M V V K00
M
M#(NA)V
M V
K00
|∼NA
M V K00
MVMK00
/ NA
VM V {M}c/kNOV
M V
K00
|∼NO
M V K00
MVMK00
/ NO
VM sup(V)
V
M#(NO)
M#(NA)VM/NARNO
MK00
/ NARNO
(c) Proof for: “Mis convinced that NOis a valid shared key between Vand M”.
M V
NA
M
M{V,M }c/kNA
M V K00
MVM V c/kNAVM
K00
|∼NA
VM#(NA)
(d) Proof for: “Mis convinced that NAis
a valid shared key between Vand M”.
M V K#
M
M{V,M }c/kK#
M V {V ,M}c/kK#
M V V K00
M
M#(NA)V
M V
K00
|∼NA
M V K00
MVMK00
/ NA
VM V {M}c/kK#V
M V
K00
|∼K#
M V K00
MVMK00
/ K#
VM sup(V)
V
M#(K#)
M#(NA)VM/NARK#
MK00
/ NARK#
(e) Proof for: “Mis convinced that K#is a valid shared key between Vand M”.
V V K#
M
V{V,M }c/kK#
V V K00
MVV Mc/kK#VV
K00
|∼K#
VV#(K#)
(f) Proof for: “Vis convinced that K#is
a valid shared key between Vand M”.
Fig. 5: Proof for authentication between EV and aggregator
9
TABLE II
Name Inference Rule
Authentication rule
P Q
K
|M
P P K
QVPK
/ M
Nonce-veriﬁcation rule
P Q P K
Q
P#(M)VP Q
K
|M
Conﬁdentiality rule
P(S∪{Q})c/kM
P P K
QVP Sc/kMVP
K
|M
Super-principal rule
P X
P Q X VP sup(Q)
Intuitive rule P /M
PK
/ M
Good Key rule
P P K
Q
P{P,Q}c/kKVP#(K)
Fresh rule
P#(N)
P#(M)VP /NRM
Applying the authentication rule to statements (i) and (ii),
we obtain “Mbelieves Gencrypted NIusing key K” which
is (iii). Since Mgenerates a new nonce NIeach time, we
can say “Mbelieves NIis new and fresh” which is (iv). On
applying the nonce-veriﬁcation rule to (iii) and (iv) we obtain
(v) which is “Mis convinced that Gis convinced that Kis
a well-kept secret between Mand G”.
M G
K
|NI(iii)
M#(NI)(iv)
M G M K
G(v)
We then apply authentication rule to (i) and (vi), to obtain
(vii) which is “Mis convinced that Gencrypted NBusing
K”. Since Ggenerates a new nonce NBeach time, Mis
knows that no one apart from Gcould have seen NB. Thus,
we have the statement “Mis convinced that Gis convinced
that no one other than Mhas access to NB” which is (viii).
Applying the conﬁdentiality rule to (v), (vii) and (viii) we get
(ix) which states “Mis convinced that Gis convinced that no
one other than Mand Bhas access to NB”.
MK
/ NB(vi)
M G
K
|NB(vii)
M G {M}c/kNB(viii)
M G {M, G}c/kNB(ix)
It is assumed in the protocol that Gis a credible and reliable
entity and Mbelieves this as fact. Hence, the statement
Mbelieves that Gis a credible and reliable entity (super-
principal)” which is (x). Next, we apply the super-principal
rule to statements (ix) and (x), to obtain (xi) which is “Mis
convinced that no one other than Mand Ghas access to NB”.
To proceed further we need to understand a few deﬁnitions and
rules of message idealization from [42] which are discussed
in the Appendix.
In message 2 of the Fig. 2 Msends NIto G. As a response,
Gsends NBin message 3 by encrypting it inside variable
M. By deciphering variable M,Gobtains nonces NIand
NB. Therefore, according to the message idealization rules
presented in the appendix, NIcan be considered as a challenge
and NBcan be considered its response. Note that these are not
the same challenge-response pair (C, K)of the PUF. Thus we
arrive at the statement “Mcan see the replied challenge NI
and the response NBwith decipherment key K” which is (xii).
On applying the intuitive rule to (xii), we get (xiii) which is
Mcan see the replied challenge NIand the response NB”.
M sup(G)(x)
M{M, G}c/kNB(xi)
MK
/ NIRNB(xii)
M / NIRNB(xiii)
We then apply the fresh rule to (iv) and (xiii), we obtain
statement (xiv) which is “Mbelieves NBis new and fresh”.
M#(NB)(xiv)
M M NB
G(xv)
Finally, we apply the good-key rule to statements (x), (xi)
and (xiv) to prove the statement “Mis convinced that NBis
a valid shared key between Mand G”.
In a similar manner the proof for “Gis convinced that
NBis a valid shared key Mand G” is shown in Fig. 4d.
The statements “Mis convinced that NCis a valid shared
key between Mand G” and “Gis convinced that NCis
a valid shared key between Mand G” are shown in Fig.
4b and Fig. 4c respectively. The statements “Mis convinced
that K0is a valid secret key between Mand G” and “Gis
convinced that K0is a valid secret key between Mand G” are
shown in Fig. 4f and Fig. 4e respectively. In these ﬁgures, the
logical AND operation between two statements is represented
by a ‘V’. Thus, we have shown that an adversary cannot see
NB,NCor K0. The three variables NB,NCand K0are
critical because without them an adversary cannot decipher the
communicated data. The Mao Boyd formal proof discussed
above has proven the secrecy of NB,NCand K0which is
10
regardless of the kind of attack used by the adversary such
as man-in-the-middle (MITM) attack, masquerade attack, or
replay attack. In a very similar manner, the Mao and Boyd
logic proofs for the MA between the EV and the aggregator
can be obtained to establish that the critical variables of this
stage, i.e, NA,NOand K00 cannot be obtained by an adversary,
the proofs of which are shown in Fig. 5a - 5f. Note that
even if an attacker physically hijacks the aggregator or the
EV, by virtue of the property of PUF discussed in section I,
it is ensured that the adversary cannot obtain the legitimate
challenge-response pairs. Additionally, there are no secrets
stored on the aggregator or the EV itself. Thus, physical
security and protection against node tampering attack are also
guaranteed. Untraceablity is ensured by using a pseudo-ID,
P I DVas shown in Fig. 3.
The parameters used in generating the session key Skare
K0,NBand NCand the parameters used in generating session
key Sk2are K00
0,NAand NO. The nonces NB,NC,NAand
NOare cryptographic nonces which are randomly generated in
every session. As already discussed in section VI, the freshness
of the nonce is a necessary condition which is checked at
several stages of the MA protocol. Unless freshness is veriﬁed,
MA does not take place. In addition, the challenge response
pairs used in the protocol (C, K),(C0, K0),(C, K ”) and
(C#, K#)are all randomly generated. First a challenge is
randomly generated and its corresponding PUF response is
obtained. The output of a PUF depends both on the physical
disorder as well as the applied challenge, hence the response
obtained for each challenge will not only be random, but also
very different from each other. Therefore K0and K00
0will
change randomly in each authentication round. The combined
effect of the randomness of these variables and the non-
linearity of the function Fguarantee that a unique session
key is obtained in each round for both stages of the protocol.
VIII. COMPARISON AND ANALYSI S
A. Security Goals And Protection Against Various Attacks
A comparison of the security features of our protocol with
a different state of the art protocols currently in use in V2G
systems is presented in Table III. “3” indicates that the
protocol possesses a feature or is secure against an attack. A
blank indicates that the protocol lacks a feature or is insecure
against an attack. All the mentioned protocols provide MA
except [24]. Without MA, a participating entity can neither
verify if it is sending a message to a trusted entity, nor can
it verify if the message it received is from a trusted entity.
With MA, both the sending and receiving parties can be
sure of each other’s authenticity. Identity protection is not
provided by the protocol in [23]. Consequently, an attacker
may easily ﬁgure out the real identity of the EV by looking
at the usage data. The protocols in [20] and [22] do not
provide message integrity. Our protocol uses MAC to ensure
this. All the entities (EVs, aggregators and grid server) can
easily detect any tampering in the messages they receive. The
protocol in [20] is vulnerable to man-in-the-middle attacks. An
adversary may insert itself between the communication of an
EV and the aggregator, or between the aggregator and the grid
server and gain control of the communication between them.
The protocols in [19], [20] and [22] are vulnerable against
impersonation attacks. The protocols in [20] and [22] are not
secure against replay attacks. The protocols in [20] and [23] do
not provide session key security. Physical security is provided
only by the proposed protocol (SUKA). As mentioned in
Section V-B, an attacker that captures an EV device must
not be able to gather any secrets. As also mentioned in
Section I, almost all authentication protocols proposed in the
literature require that the EVs store at least one secret in their
memory, if not more. Such storing of secrets on any device
renders the protocols vulnerable to physical attacks. The MA
protocol proposed in this paper has two features which make it
resistant to any physical attacks: (i) EVs and aggregators need
not store any secrets in their memory; (ii) there is a secure
communication between the EV’s microcontroller and its PUF
since they are both on the same chip [43]. Thus, even though
an attacker may physically capture the device, it would be
impossible for them to extract any secrets. Therefore, SUKA
is resilient against physical attacks. The papers in [19], [20]
[22], [24] and [31] do not provide a formal security proof for
their proposed protocols.
In Table IV, we present a comparison of the computation
costs of our protocol with some state-of-the-art protocols
which have a similar system model as ours. The comparison
is for the case where one EV is authenticating with the grid.
The number of cryptographic operations, pairing operations,
encryption/decryption, hash operations, MAC computations
and PUF executions are listed for one round of authentication,
setting m= 3. Our protocol uses only 33 cryptographic
operations (which include XOR, addition, scalar multiplication
and exponential computation) compared to 37 in [30] and 36 in
[20]. Our protocol uses zero pairing operations. While [20] has
only 2 encryption/decryption operations and 4 MAC/HMAC
computations, it has 9 hash function computations while
ours has zero. Although [30] has no encryption/decryption
operations or MAC/HMAC computations, it has 16 hash
computations while ours has none. While there is no physical
security in [19], [20] and [30], our protocol is physically
secured by the use of PUFs, which requires 2 operations. We
argue that the overall performance of our protocol is much
better due to lesser computation overhead and far superior
security features.
C. Performance Comparison
We simulated the operations carried out by an EV in the
security schemes of [19], [30] and [20], all of which have a
similar system model as SUKA. The simulations were carried
out in Python 2.7 on a PC with Intel Core i5-5200U processor
with 8GB DDR3 RAM. Fig. 6, shows the time consumed by
the EV in every round of authentication in the considered
schemes. The EV consumes 3.682 ms, 3.072 ms, 2.022 ms
in the security schemes of [19], [30] and [20] respectively
whereas only 0.845 ms in SUKA. Therefore, SUKA is more
efﬁcient than state-of-the-art security schemes.
11
TABLE III: Comparison of Security Features
Features [18] [19] [20] [22] [23] [24] [31] SUKA
Mutual Authentication 33333 33
Identity Protection 3 3 3 3 3 3 3
Message Integrity 3 3 3 3 3 3
Man-In-The-Middle Attack 33 33333
Impersonation Attack 3 3 3 3 3
Replay Attack 3 3 3 3 3 3
Session Key Security 3 3 3 3 3 3
Physical Security 3
Formal Security Proof 3 3 3
TABLE IV: Comparison of computation overhead
Operations [19] [20] [30] SUKA
Cryptographic operations
(, +, scalar multiplication
and exponent)
81 36 37 33
Pairing 19 - - -
Encryption/Decryption - 2 - 6
Hash (H) 6 9 16 -
MAC/HMAC 7 4 - 8
PUF - - - 2
[19] [30] [20] SUKA
Schemes Considered
0
0.5
1
1.5
2
2.5
3
3.5
4
Time Consumed (in seconds)
10-3
Fig. 6: Comparison of time consumed by EV for MA in SUKA
and the security schemes of [19], [30] and [20].
IX. CONCLUSION
This paper proposed MA protocols for the two stages or
steps which arise in a V2G system: (i) For MA between
the aggregator and the grid server, and (ii) for MA between
EV and aggregator. The proposed protocol (SUKA) uses a
challenge-response architecture, which is enabled by PUFs.
This gives our proposed protocol the advantage of not having
to store any secret information in EVs and aggregators. Secrets
are stored only in the grid server. Only one challenge-response
pair is stored in the server for every EV. Two session keys are
established when an EV wants to authenticate with the grid
server: one session key between the aggregator and the grid
server, and another one between the EV and the aggregator. We
showed that SUKA achieves MA, identity protection, message
integrity, physical security, and session key security along
with protection against various attacks such as MITM attacks,
replay attacks and impersonation attacks. SUKA is proven
formally secure by Mao and Boyd logic and uses simple
computations, which makes it very efﬁcient and fast. Hence,
the proposed protocol is a viable solution for upcoming V2G
systems.
X. AC KN OWLEDGEMENT
This research was supported in part by Singapore Ministry
of Education Academic Research Fund Tier 1 (R-263-000-
D62-114).
APPENDIX
A. Rules of Message Idealization
A message without any symbols is called an atomic
message (AM).
If an AM is sent at one stage of the protocol by a node
and received by the same node in another stage of the
protocol, it is called a challenge.
Achallenge sent to its originating node is called a replied
challenge.
If an AM and a response are sent together by a single
node for the ﬁrst time, it is called a response.
AMs which are not challenges or responses are treated as
nonsense and are discarded.
In case an AM qualiﬁes as a challenge and response in a
single line, it is considered a response.
Areplied challenge and its response together is denoted
as Response RRC.
REFERENCES
[1] B. K. Sovacool and R. F. Hirsh, “Beyond batteries: An examination of
the beneﬁts and barriers to plug-in hybrid electric vehicles (phevs) and
a vehicle-to-grid (v2g) transition,” Energy Policy, vol. 37, no. 3, pp.
1095–1103, 2009.
[2] C. D. White and K. M. Zhang, “Using vehicle-to-grid technology
for frequency regulation and peak-load reduction,Journal of Power
Sources, vol. 196, no. 8, pp. 3972–3980, 2011.
[3] H. Liu, Z. Hu, Y. Song, and J. Lin, “Decentralized vehicle-to-grid control
for primary frequency regulation considering charging demands,IEEE
Transactions on Power Systems, vol. 28, no. 3, pp. 3480–3489, 2013.
[4] H. Lund and W. Kempton, “Integration of renewable energy into the
transport and electricity sectors through v2g,” Energy policy, vol. 36,
no. 9, pp. 3578–3587, 2008.
[5] L. Gelazanskas and K. A. Gamage, “Demand side management in smart
grid: A review and proposals for future direction,Sustainable Cities and
Society, vol. 11, pp. 22–30, 2014.
12
[6] H. Wang, B. Qin, Q. Wu, L. Xu, and J. Domingo-Ferrer, “Tpp: Traceable
privacy-preserving communication and precise reward for vehicle-to-grid
networks in smart grids,” IEEE Transactions on Information Forensics
and Security, vol. 10, no. 11, pp. 2340–2351, 2015.
[7] V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar,
“A Survey on IoT Security: Application Areas, Security Threats, and
Solution Architectures,” IEEE Access, vol. 7, pp. 82721–82 743, 2019.
[8] G. K. Verma, B. B. Singh, N. Kumar, and V. Chamola, “CB-CAS:
Certiﬁcate-Based Efﬁcient Signature Scheme with Compact Aggregation
for Industrial Internet of Things Environment,IEEE Internet of Things
Journal, vol. PP, no. c, pp. 1–1, 2019.
[9] A. Abdallah and X. Shen, “Lightweight Security and Privacy-Preserving
Scheme for V2G Connection,” in 2015 IEEE Global Communications
Conference (GLOBECOM). IEEE, dec 2015, pp. 1–7. [Online].
Available: http://ieeexplore.ieee.org/document/7417592/
[10] S. Devadas, E. Suh, S. Paral, R. Sowell, T. Ziola, and V. Khandelwal,
“Design and Implementation of PUF-Based ”Unclonable” RFID ICs
for Anti-Counterfeiting and Security Applications,” in 2008 IEEE
International Conference on RFID. IEEE, apr 2008, pp. 58–64.
[Online]. Available: https://ieeexplore.ieee.org/document/4519377/
[11] J. Guajardo, S. S. Kumar, G.-J. Schrijen, and P. Tuyls, “Brand
and IP protection with physical unclonable functions,” in 2008
IEEE International Symposium on Circuits and Systems. IEEE, may
2008, pp. 3186–3189. [Online]. Available: http://ieeexplore.ieee.org/
document/4542135/
[12] W. Kempton and J. T. Tomi´
c, “Vehicle-to-grid power fundamentals:
Calculating capacity and net revenue,Journal of Power Sources, vol.
144, pp. 268–279, 2005. [Online]. Available: http://www.udel.edu/V2G.
[13] Sekyung Han, Soohee Han, and K. Sezaki, “Development of an
Optimal Vehicle-to-Grid Aggregator for Frequency Regulation,” IEEE
Transactions on Smart Grid, vol. 1, no. 1, pp. 65–72, jun 2010.
[Online]. Available: http://ieeexplore.ieee.org/document/5446440/
[14] F. Kennel, D. Gorges, and S. Liu, “Energy management for smart grids
with electric vehicles based on hierarchical MPC,” IEEE Transactions
on Industrial Informatics, vol. 9, no. 3, pp. 1528–1537, 2013.
[15] C. Guille and G. Gross, “A conceptual framework for the
vehicle-to-grid (V2G) implementation,” Energy Policy, vol. 37,
no. 11, pp. 4379–4390, nov 2009. [Online]. Available: https:
//www.sciencedirect.com/science/article/pii/S0301421509003978
[16] B. K. Sovacool and R. F. Hirsh, “Beyond batteries: An examination of
the beneﬁts and barriers to plug-in hybrid electric vehicles (PHEVs)
and a vehicle-to-grid (V2G) transition,” 2008. [Online]. Available:
www.elsevier.com/locate/enpol
[17] L. Pieltain Fern´
andez, T. G´
omez San Rom´
an, R. Cossent, C. Mateo
Domingo, and P. Fr´
ıas, “Assessment of the impact of plug-in electric
vehicles on distribution networks,IEEE Transactions on Power Sys-
tems, vol. 26, no. 1, pp. 206–213, 2011.
[18] N. Saxena, S. Grijalva, V. Chukwuka, and A. V. Vasilakos, “Network Se-
curity and Privacy Challenges in Smart Vehicle-to-Grid,IEEE Wireless
Communications, vol. 24, no. 4, pp. 88–98, 2017.
[19] Z. Yang, S. Yu, W. Lou, and C. Liu, “$Pˆ{2}$: Privacy-
Preserving Communication and Precise Reward Architecture for
V2G Networks in Smart Grid,” IEEE Transactions on Smart
Grid, vol. 2, no. 4, pp. 697–706, dec 2011. [Online]. Available:
http://ieeexplore.ieee.org/document/5771586/
[20] H. Liu, H. Ning, Y. Zhang, and L. T. Yang, “Aggregated-proofs based
privacy-preserving authentication for V2G networks in the smart grid,
IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1722–1733, 2012.
[21] H.-R. Tseng, “A secure and privacy-preserving communication protocol
for V2G networks,” in 2012 IEEE Wireless Communications and
Networking Conference (WCNC). IEEE, apr 2012, pp. 2706–2711.
[Online]. Available: http://ieeexplore.ieee.org/document/6214259/
[22] H. Liu, H. Ning, Y. Zhang, Q. Xiong, and L. T. Yang, “Role-dependent
privacy preservation for secure v2g networks in the smart grid,” IEEE
Transactions on Information Forensics and Security, vol. 9, no. 2, pp.
208–220, feb 2014.
[23] J. L. Tsai and N. W. Lo, “Secure Anonymous Key Distribution Scheme
for Smart Grid,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp.
906–914, mar 2016.
[24] A. Abdallah and X. Shen, “Lightweight Authentication and Privacy-
Preserving Scheme for V2G Connections,” IEEE Transactions on Ve-
hicular Technology, vol. 66, no. 3, pp. 2615–2629, 2017.
[25] V. Odelu, A. K. Das, M. Wazid, and M. Conti, “Provably Secure Au-
thenticated Key Agreement Scheme for Smart Grid,IEEE Transactions
on Smart Grid, vol. 9, no. 3, pp. 1900–1910, may 2018.
[26] J. Shen, T. Zhou, F. Wei, X. Sun, and Y. Xiang, “Privacy-preserving
and lightweight key agreement protocol for V2G in the social internet
of things,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 2526–2536,
aug 2018.
[27] H. Guo, Y. Wu, F. Bao, H. Chen, and M. Ma, “UBAPV2G: A unique
batch authentication protocol for vehicle-to-grid communications,” IEEE
Transactions on Smart Grid, vol. 2, no. 4, pp. 707–714, 2011.
[28] H. Liu, H. Ning, Y. Zhang, and M. Guizani, “Battery status-aware au-
thentication scheme for V2G networks in smart grid,” IEEE Transactions
on Smart Grid, vol. 4, no. 1, pp. 99–110, 2013.
[29] J. Chen, Y. Zhang, and W. Su, “An anonymous authentication scheme
for plug-in electric vehicles joining to charging/discharging station in
vehicle-to-Grid (V2G) networks,China Communications, vol. 12, no. 3,
pp. 9–19, 2015.
[30] N. Saxena and B. J. Choi, “Authentication Scheme for Flexible Charging
and Discharging of Mobile Vehicles in the V2G Networks,” IEEE
Transactions on Information Forensics and Security, vol. 11, no. 7, pp.
1438–1452, jul 2016.
[31] M. Tao, K. Ota, M. Dong, and Z. Qian, “AccessAuth: Capacity-aware
security access authentication in federated-IoT-enabled V2G networks,”
J. Parallel Distrib. Comput., vol. 118, pp. 107–117, 2018. [Online].
Available: https://doi.org/10.1016/j.jpdc.2017.09.004
[32] P. Gope and B. Sikdar, “Lightweight and Privacy-Friendly Spatial Data
Aggregation for Secure Power Supply and Demand Management in
Smart Grids,” IEEE Transactions on Information Forensics and Security,
vol. 14, no. 6, pp. 1554–1566, jun 2019.
[33] M. M. Fouda, Z. M. Fadlullah, N. Kato, R. Lu, and X. S. Shen, “A
lightweight message authentication scheme for smart grid communica-
tions,” IEEE Transactions on Smart Grid, vol. 2, no. 4, pp. 675–685,
2011.
[34] Q. Chen, G. Csaba, P. Lugli, U. Schlichtmann, and U. Ruhrmair,
“The Bistable Ring PUF: A new architecture for strong Physical
Unclonable Functions,” in 2011 IEEE International Symposium on
Hardware-Oriented Security and Trust. IEEE, jun 2011, pp. 134–141.
[Online]. Available: http://ieeexplore.ieee.org/document/5955011/
[35] T. Alladi, V. Chamola, B. Sikdar, and K.-k. R. Choo, “Consumer IoT
: Security Vulnerability Case Studies and Solutions Consumer IoT :
Security Vulnerability Case Studies and Solutions,” no. October, 2019.
[36] S. Pandey, S. Deyati, A. Singh, and A. Chatterjee, “Noise-resilient sram
physically unclonable function design for security,” in 2016 IEEE 25th
Asian Test Symposium (ATS). IEEE, 2016, pp. 55–60.
[37] D. Jeon, J. H. Baek, D. K. Kim, and B.-D. Choi, “Towards zero bit-
error-rate physical unclonable function: Mismatch-based vs. physical-
based approaches in standard cmos technology,” in 2015 Euromicro
Conference on Digital System Design. IEEE, 2015, pp. 407–414.
[38] K.-H. Chuang, E. Bury, R. Degraeve, B. Kaczer, D. Linten, and
I. Verbauwhede, “A physically unclonable function using soft oxide
breakdown featuring 0% native ber and 51.8 fj/bit in 40-nm cmos,
IEEE Journal of Solid-State Circuits, vol. 54, no. 10, pp. 2765–2776,
2019.
[39] X. Lu, L. Hong, and K. Sengupta, “Cmos optical pufs using noise-
immune process-sensitive photonic crystals incorporating passive vari-
ations for robustness,” IEEE Journal of Solid-State Circuits, vol. 53,
no. 9, pp. 2709–2721, 2018.
[40] W.-C. Wang, Y. Yona, S. N. Diggavi, and P. Gupta, “Design and
analysis of stability-guaranteed pufs,” IEEE Transactions on Information
Forensics and Security, vol. 13, no. 4, pp. 978–992, 2017.
[41] D. M’Raihi, S. Machani, M. Pei, and J. Rydell, “Totp: Time-based one-
time password algorithm,” Internet Request for Comments, 2011.
[42] W. Mao and C. Boyd, “Towards formal analysis of security protocols,
in [1993] Proceedings Computer Security Foundations Workshop
VI. IEEE Comput. Soc. Press, pp. 147–158. [Online]. Available:
http://ieeexplore.ieee.org/document/246631/
[43] S. Sutar, A. Raha, and V. Raghunathan, “Memory-based combination
pufs for device authentication in embedded systems,IEEE Transactions
on Multi-Scale Computing Systems, vol. 4, no. 4, pp. 793–810, 2018.
13
Gaurang Bansal received the B.E. & M.E. de-
gree in Computer Science from Birla Institute of
Technology and Science, Pilani, India, in 2018
and 2020 respectively. He has authored more than
10 publications in top tier confences and Journals
like IEEE INFOCOM, IEEE Globecom, IEEE ICC,
IEEE Transaction on Vehicular Technology, IEEE
Systems Journal. His research interests include the
IoT security, network security and distributed com-
puting.
Naren is currently pursuing his B.E in Electri-
cal and Electronics Engineering, and M.Sc (Hons)
in Physics with the Birla Institute of Technology
and Science (Pilani). He has completed projects on
Quark-Gluon Plasma, Superconductivity, hardware
security techniques in IoT and electromagnetic radi-
ation pollution. His other research interests include
IoT, Industry 4.0, and security provisioning in V2G,
UAV and Medical IoT networks.
Vinay Chamola received the B.E. degree in elec-
trical and electronics engineering and master’s de-
gree in communication engineering from the Birla
Institute of Technology and Science, Pilani, India, in
2010 and 2013, respectively. He received his Ph.D.
degree in electrical and computer engineering from
the National University of Singapore, Singapore, in
2016. In 2015, he was a Visiting Researcher with the
Autonomous Networks Research Group (ANRG),
University of Southern California, Los Angeles, CA,
USA. He also worked as a post-doctoral research
fellow at the National University of Singapore, Singapore where he worked
in the area of Internet of Things. He is currently Assistant Professor with
the Department of Electrical and Electronics Engineering, BITS-Pilani, Pilani
Campus where he heads the Internet of Things Research Group / Lab. He
has over 45 publications in high ranked SCI Journals including more than
25 IEEE Transaction and Journal articles. His works have been published in
Journals like IEEE Transactions on Communications,IEEE Transactions on
Vehicular Technology,IEEE Journal on Selected Areas in Communications,
IEEE Communications Magazine etc. Furthermore, his works have been
accepted and presented in reputed conferences like IEEE INFOCOM, IEEE
GLOBECOM, IEEE ICC, IEEE PerCom to name a few. His research interests
include IoT Security, Blockchain, 5G network management and addressing
research issues in VANETs and UAV networks. He has served as a reviewer
for several IEEE/Elsevier Journals. He is a Guest Editor in Computer
Communication, Elsevier. He also serves as an Associate Editor for the IET
Quantum Communications and Frontiers in Communications and Networks.
Biplab Sikdar (S’98–M’02–SM’09) received the
B.Tech. degree in electronics and communication
engineering from North Eastern Hill University,
Shillong, India, in 1996, the M.Tech. degree in
electrical engineering from the Indian Institute of
Technology Kanpur, Kanpur, India, in 1998, and the
Ph.D. degree in electrical engineering from Rensse-
laer Polytechnic Institute, Troy, NY, USA, in 2001.
He is currently an Associate Professor with the
Department of Electrical and Computer Engineering,
National University of Singapore, Singapore. His
current research interests include wireless network, and security for Internet of
Things and cyberphysical systems. Dr. Sikdar served as an Associate Editor for
the IEEE TRANSACTIONS ON COMMUNICATIONS from 2007 to 2012.
He currently serves as an Associate Editor for the IEEE TRANSACTIONS
ON MOBILE COMPUTING.
Neeraj Kumar received the Ph.D. degree in com-
puter science and engineering from Shri Mata
Vaishno Devi University, Katra, India. He is cur-
rently with the Department of Computer Science and
Engineering, Thapar University, Patiala, India. He
has authored or coauthored more than 300 technical
research papers in leading journals such as the IEEE
TII, IEEE TIE, IEEE TDSC, the IEEE TWPS,
IEEE SYSTEMS JOURNAL, IEEE COMMUNI-
CATIONS MAGAZINE, the IEEE WIRELESS
COMMUNICATIONS MAGAZINE, the IEEE NET-
WORK MAGAZINE, and conferences. His research interests include mo-
bile computing, parallel/distributed computing, multiagent systems, service-
oriented computing, routing and security issues in mobile ad hoc, and sensor
and mesh networks.
Mohsen Guizani (S’85–M’89–SM’99–F’09) re-
ceived the B.S. (with distinction) and M.S. degrees
in electrical engineering, the M.S. and Ph.D. degrees
in computer engineering from Syracuse University,
Syracuse, NY, USA, in 1984, 1986, 1987, and
1990, respectively. He is currently a Professor at the
Computer Science and Engineering Department in
Qatar University, Qatar. Previously, he served in dif-
ferent academic and administrative positions at the
University of Idaho, Western Michigan University,
University of West Florida, University of Missouri-
Kansas City, University of Colorado-Boulder, and Syracuse University. His
research interests include wireless communications and mobile computing,
computer networks, mobile cloud computing, security, and smart grid. He
is currently the Editor-in-Chief of the IEEE Network Magazine, and the
Founder and Editor-in-Chief of Wireless Communications and Mobile Com-
puting journal (Wiley). He is the author of nine books and more than 500
publications in refereed journals and conferences. He received the 2017 IEEE
Communications Society WTC Recognition Award as well as the 2018 AdHoc
Technical Committee Recognition Award for his contribution to outstanding
research in wireless communications and Ad-Hoc Sensor networks. He was
the Chair of the IEEE Communications Society Wireless Technical Committee
and the Chair of the TAOS Technical Committee. He served as the IEEE
Computer Society Distinguished Speaker and is currently the IEEE ComSoc
Distinguished Lecturer. He is a Fellow of IEEE and a Senior Member of
ACM.
... [3]. Recently, physical unclonable functions (PUFs) have been widely used for authentication protocols [4]. PUF-based authentication protocols have been proven to provide better security features and computationally lightweight authentication in UAV scenarios. ...
... PUFs exploit the inherent randomness that is unique to a device and cannot be cloned or forged. This intrinsic randomness is generated during the the fabrication of chips used in the device [4]. A PUF can be modeled as a challengeresponse system, where the PUF uses its internal characteristics to map a challenge C to response R. Scalability is an important required feature of an authentication protocol when it comes to the task of authenticating a swarm of UAVs. ...
... The base station and UAVs mutually verify each other's identity through an authentication protocol. The system model employs a PUF-based mutual authentication protocol inspired from [4]. The mutual authentication protocol is presented in Fig. 2. The UAV sends its ID, GPS location and nonce to the base station. ...
Article
Full-text available
Swarm-based Unmanned Aerial Vehicle (UAV) applications require a large number of UAVs to be deployed across a region to work cooperatively. To operate a large number of unattended UAVs in hostile environments , it is critical to secure UAV-BS (base station) communications. UAV authentication based on Physical Unclonable Functions (PUFs) has recently emerged as a potential solution for overcoming adversarial attacks. The performance of PUF-based authentication protocols is strongly influenced by various factors, including the time required to generate the topology, the number of bottleneck connections, and the network's traffic load. This article investigates how the authentication time for a UAV swarm is affected by various factors such as the type of topology, number of UAVs in the swarm and the number of parallel connections.
... However, it was not supported by hardware security. A mutual authentication scheme for V2G utilizing physical unclonable functions (PUFs) suggested by Bansal et al. [18] in 2020. As per best knowledge, the suggested scheme could prevent a physical attack. ...
... Our proposed protocol provides the following important security features required for smart grid energy trading. We have compared our researches with the latest proposed scheme [17], [18] and also demonstrated the features as defined below. ...
... We compared the different schemes based on different operations like XOR, Addition, Hash, MAC, PUF operation, etc. Our scheme uses only 4 cryptographic operations compared to 33 in [18] and 37 in [15]. Our scheme has 12 hash function computations while [15] has 16 and scheme [17] has 14. ...
... The combinatorial problems' computational complexity as presented by Diffie et al. [17] for maximum simultaneous requests for the computing maximum flow or edge-disjoint paths (EDP) within the network problem was proven to be NP-complete. The V2G lightweight mutual authentication protocol using a physical unclonable function was presented by Bansal et al. [14]. They presented a vehicle-to-grid-based mutual authentication by a challenge-response model consisting of MAC, nonce, and user key exchange operations for session key generation. ...
... Table 5 shows the comparison of the protocol objectives. Many recent protocols performing mutual authentication use session keys and hashes, such as Bansal et al. [14], Kumari et al. [26], Lopes et al. [27], Binu et al. [15], Wu et al. [29], and Madhusudhan et al. [30]. A higher cryptographic calculation was used in Bansal et al. [14], Kumari et al. [26], Lopes et al. [27], Binu et al. [15], Mbarek et al. [28], and Madhusudhan et al. [30]. ...
... Many recent protocols performing mutual authentication use session keys and hashes, such as Bansal et al. [14], Kumari et al. [26], Lopes et al. [27], Binu et al. [15], Wu et al. [29], and Madhusudhan et al. [30]. A higher cryptographic calculation was used in Bansal et al. [14], Kumari et al. [26], Lopes et al. [27], Binu et al. [15], Mbarek et al. [28], and Madhusudhan et al. [30]. Furthermore, active and advanced attacks were analyzed by all of them. ...
Article
Full-text available
Authentication is essential for the prevention of various types of attacks in fog/edge computing. Therefore, a novel mode-based hash chain for secure mutual authentication is necessary to address the Internet of Things (IoT) devices’ vulnerability, as there have been several years of growing concerns regarding their security. Therefore, a novel model is designed that is stronger and effective against any kind of unauthorized attack, as IoT devices’ vulnerability is on the rise due to the mass production of IoT devices (embedded processors, camera, sensors, etc.), which ignore the basic security requirements (passwords, secure communication), making them vulnerable and easily accessible. Furthermore, crackable passwords indicate that the security measures taken are insufficient. As per the recent studies, several applications regarding its requirements are the IoT distributed denial of service attack (IDDOS), micro-cloud, secure university, Secure Industry 4.0, secure government, secure country, etc. The problem statement is formulated as the “design and implementation of dynamically interconnecting fog servers and edge devices using the mode-based hash chain for secure mutual authentication protocol”, which is stated to be an NP-complete problem. The hash-chain fog/edge implementation using timestamps, mode-based hash chaining, the zero-knowledge proof property, a distributed database/blockchain, and cryptography techniques can be utilized to establish the connection of smart devices in large numbers securely. The hash-chain fog/edge uses blockchain for identity management only, which is used to store the public keys in distributed ledger form, and all these keys are immutable. In addition, it has no overhead and is highly secure as it performs fewer calculations and requires minimum infrastructure. Therefore, we designed the hash-chain fog/edge (HCFE) protocol, which provides a novel mutual authentication scheme for effective session key agreement (using ZKP properties) with secure protocol communications. The experiment outcomes proved that the hash-chain fog/edge is more efficient at interconnecting various devices and competed favorably in the benchmark comparison.
... The proposed protocol uses the PUF to perform mutual authentication between the electric vehicle (EV), charging stations (CSs) and grid stations (GSs) and is formally verified using AVISPA tool. A secure user key-exchange authentication (SUKA) protocol was proposed in [14] between electric vehicle and grid server using PUF, and an aggregator acts as a mediator between the EVs Fig. 3 Message flow of PKES systems where LF and UHF denote low-frequency and ultra-high-frequency band RF signals [1] Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
... Instead, (s)he just launches distance bounding attacks to make the car system believe that the key fob is nearby and subsequently unlocks the door. To the best of our knowledge, this attack was never covered in any of the above-mentioned works [10][11][12][13][14][15][16]. Very recently, in [17] and [18] two machine learning attack-resistant PUF designs have been proposed using re-configuration property and deception techniques to fake the PUF responses. ...
Article
Full-text available
In recent years, connected and intelligent vehicles have posed advanced risks to road safety and vehicle thefts. The keyless entry and immobiliser systems of luxury vehicles have been under extensive scrutiny and found to be vulnerable against lack of mutual authentication in challenge-response protocol, smaller key size for the cipher, amplification and relay attack, etc. This work presents an initial study to use an unconventional hardware security primitive named Physically Unclonable Functions (PUFs) to nullify such impacts and develop a novel mutual authentication protocol (coined as “PAKAMAC”) to provide an alternative to remote keyless entry (RKE) system and passive keyless entry and start (PKES) system. The proposed scheme generates a hardware fingerprint of the key fob using an embedded PUF instance for unique identification by the vehicle and also leverages two factors, namely time-to-live (TTL) and nonce, to provide secure utility for keyless entry. We have implemented the protocol in Scyther protocol verification tool, and it shows that PAKAMAC satisfies all the security features and can be conveniently applied to automotive systems with minimal hardware overhead and no additional message exchange between the key fob and the car.
... Recently, Physical Unclonable Functions (PUF) has been interested to many researchers by its unique physical characteristics. Several mutual authentication schemes [40][41][42][43] based on PUF have been proposed in the last year. Bansal et al. [40] presented a lightweight and privacypreserving authentication scheme for Vehicle-to-Grid ecosystem (V2G) systems. ...
... Several mutual authentication schemes [40][41][42][43] based on PUF have been proposed in the last year. Bansal et al. [40] presented a lightweight and privacypreserving authentication scheme for Vehicle-to-Grid ecosystem (V2G) systems. Their scheme uses PUFs to verify the identity of an electric vehicles and the power grid. ...
Article
Full-text available
With the development and practical application of 5G technology, the construction of smart cities has progressed into an entirely new level. Mobile wireless networks in smart cities provide people with ubiquitous network services, thereby making the entire city organic. However, the open character of such wireless networks results in network security issues. As a result, people suffer from potential network threats while enjoying the convenience of wireless networks. To solve this problem, various roaming authentication protocols for mobile network are proposed. We find that a contradiction exists between user anonymity and resistance to denial of service (DoS) attacks. Most current protocols attach importance to user privacy protection. Hence, they are vulnerable to DoS attacks, which cause network paralysis. We put forward an anonymous authentication protocol with DoS resistance for smart cities by overcoming the defects of the protocol of Xie et al. Then, two formal validation tools, namely, ProVerif and BAN logic, are introduced to verify the security of our scheme. Security analyses indicate that our protocol not only meets many known security properties but also shows higher efficiency compared with related works. In addition, the proposed protocol achieves a good balance between user anonymity and DoS attack resistance, while many other schemes failed to do so because they ignore this type of attack. Thus, it is more suitable for smart cities.
... Authentication and attestation are two security mechanisms required for proper functioning and ensuring security in UAV communications. Authentication is a mechanism used by communicating parties to establish that each of them is a valid device [3,4]. Authentication is a mechanism used by communicating parties to validate the identity or source of a message or information flow between them. ...
Article
Full-text available
Unmanned Aerial Vehicles (UAVs) have enabled a broad spectrum of applications serving social, commercial, and military purposes. However, since UAVs use wireless communication technologies, they are highly vulnerable to security threats. Establishing trust with the base station is the most fundamental security aspect in UAV networks to mitigate these threats. However, due to a UAV's constrained resources, deploying traditional trust establishment schemes in UAV networks becomes challenging. Further, this issue escalates as the number of UAVs increases. To address this issue, we propose an authentication cum attestation protocol for UAV swarms using an optimal communication trajectory, which can establish the required trust in a lightweight manner. Furthermore, our protocol uses Physical Unclonable Functions (PUFs) and thus guarantees physical security as well. We demonstrate that the proposed protocol is feasible, scalable, and secure using a formal Mao Boyd logic approach. Comparative analyses show that the proposed protocol outperforms the state-of-the-art.
... Physical Unclonable Functions (PUFs): PUFs assign a unique ID to an electronic device based on the process variations present in it. This ID can be used for implementing fast and secure authentication, and identification [181]. Javaid et al. used PUFs for establishing trust in their proposed blockchain-based framework for vehicular networks [98]. ...
Article
Full-text available
Vehicular networks promise features such as traffic management, route scheduling, data exchange, entertainment, and much more. With any large-scale technological integration comes the challenge of providing security. Blockchain technology has been a popular choice of many studies for making the vehicular network more secure. Its characteristics meet some of the essential security requirements such as decentralization, transparency, tamper-proof nature, and public audit. This study catalogues some of the notable efforts in this direction over the last few years. We analyze around 75 blockchain-based security schemes for vehicular networks from an application, security, and blockchain perspective. The application perspective focuses on various applications which use secure blockchain-based vehicular networks such as transportation, parking, data sharing/ trading, and resource sharing. The security perspective focuses on security requirements and attacks. The blockchain perspective focuses on blockchain platforms, blockchain types, and consensus mechanisms used in blockchain implementation. We also compile the popular simulation tools used for simulating blockchain and for simulating vehicular networks. Additionally, to give the readers a broader perspective of the research area, we discuss the role of various state-of-the-art emerging technologies in blockchain-based vehicular networks. Lastly, we summarize the survey by listing out some common challenges and the future research directions in this field.
... Physical Unclonable Functions (PUFs): PUFs assign a unique ID to an electronic device based on the process variations present in it. This ID can be used for implementing fast and secure authentication, and identification [181]. Javaid et al. used PUFs for establishing trust in their proposed blockchain-based framework for vehicular networks [98]. ...
Preprint
Full-text available
Vehicular networks promise features such as traffic management, route scheduling, data exchange, entertainment, and much more. With any large-scale technological integration comes the challenge of providing security. Blockchain technology has been a popular choice of many studies for making the vehicular network more secure. Its characteristics meet some of the essential security requirements such as decentralization, transparency, tamper-proof nature, and public audit. This study catalogues some of the notable efforts in this direction over the last few years. We analyze around 75 blockchain-based security schemes for vehicular networks from an application, security, and blockchain perspective. The application perspective focuses on various applications which use secure blockchain-based vehicular networks such as transportation, parking, data sharing/ trading, and resource sharing. The security perspective focuses on security requirements and attacks. The blockchain perspective focuses on blockchain platforms, blockchain types, and consensus mechanisms used in blockchain implementation. We also compile the popular simulation tools used for simulating blockchain and for simulating vehicular networks. Additionally, to give the readers a broader perspective of the research area, we discuss the role of various state-of-the-art emerging technologies in blockchain-based vehicular networks. Lastly, we summarize the survey by listing out some common challenges and the future research directions in this field.
... Researchers [25,26] have also studied improving the microgrid stability in an EV charging station by using a vehicle-to-grid service (V2G). A V2G is defined as providing power and other necessary support from an EV to the grid [27][28][29][30]. Other researchers [25] proved that EV charging stations could be used to compensate for instantaneous grid voltage fluctuations and improve the power quality. ...
Article
Full-text available
This study proposes a grid-connected inverter for photovoltaic (PV)-powered electric vehicle (EV) charging stations. The significant function of the proposed inverter is to enhance the stability of a microgrid. The proposed inverter can stabilize its grid voltage and frequency by supplying or absorbing active or reactive power to or from a microgrid using EVs and PV generation. Moreover, the proposed inverter can automatically detect an abnormal condition of the grid, such as a blackout, and operate in the islanding mode, which can provide continuous power to local loads using EV vehicle-to-grid service and PV generation. These inverter functions can satisfy the requirements of the grid codes, such as IEEE Standard 1547–2018 and UL 1741 SA. In addition, the proposed inverter can not only enhance the microgrid stability but also charge EVs in an appropriate mode according to the condition of the PV array and EVs. The proposed inverter was verified through experimental results with four scenarios in a lab-scale testbed. These four scenarios include grid normal conditions, grid voltage fluctuations, grid frequency fluctuations, and a power blackout. The experimental results demonstrated that the proposed inverter could enhance the microgrid stability against grid abnormal conditions, fluctuations of grid frequency and voltage, and charge EVs in an appropriate mode.
Article
Full-text available
As consumer Internet of Things (IoT) devices become increasingly pervasive in our society, there is a need to understand the underpinning security risks. Therefore, in this paper, we describe the common attacks faced by consumer IoT devices and suggest potential mitigation strategies. We hope that the findings presented in this paper will inform the future design of IoT devices.
Article
Full-text available
The notion of aggregation of data in IIoT environment is a common practice. It shortens the data and associated signatures to reduce the bandwidth requirement. The compact aggregate signature (CAS) scheme creates a constant length aggregate signature (AS). Thus, the length of the CAS is independent of the number of messages or signatures to be aggregated. This article presents the first pairing free compact aggregate signature scheme in certificate-based settings. Due to the certificate-based approach, the proposed scheme is free from key escrow and key distribution problems inherited in identity-based cryptography (IDC) and certificate-less cryptography (CLC) respectively. Being compact and pairing free, it is the least bandwidth-consuming and the most efficient provably secure aggregation method. The length and computational cost analysis show that the scheme is the most appealing to use in the IIoT environment.
Article
Full-text available
Internet of things (IoT) is the next era of communication. Using IoT, physical objects can be empowered to create, receive and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in IoT applications are discussed. Four different technologies: Blockchain, fog computing, edge computing, and machine learning to increase the level of security in IoT are discussed.
Article
Full-text available
This paper introduces a new design methodology for incorporating process-sensitive optical nanostructures in standard CMOS processes to create robust optical physically unclonable functions (PUFs) realized through an electrical-photonic co-design approach. The passive lithographic variations of lower level metal interconnects are exploited to realize resonant photonic crystals on an array of photodetectors to include variations that are robust to noise processes. The chip is realized in a standard 65-nm CMOS process with no additional post-processing. The addition of the structures increases the coefficient of variation by a factor of 3.5x compared to only active device variations. This creates extremely robust PUF responses with a native inter-chip Hamming distance (HD) of 49.81% and intra-chip HD of 0.251% with an inter-HD/intra-HD ratio of 198x illustrating the reliability of the design. The native intra-HD can be reduced to 0.06% with 17 mV of thresholding with only 4% of the total combinations discarded. To the best our knowledge, this is also the first demonstration of photonic crystals and an optical PUF in CMOS.
Article
Full-text available
Smart vehicle-to-grid (V2G) involves intelligent charge and discharge decisions based on user operational energy requirements, such as desired levels of charging and waiting time. V2G is also supported by information management capabilities enabled by a secure network, such as a reliable privacy-preserving payment system. In this article, we describe the network security and privacy requirements and challenges of V2G applications. We present a new network security architecture to support V2G. We propose a scheme with the following security and privacy-preserving features: anonymous authentication, fine-grained access control, anonymous signatures, information confidentiality, message integrity, remote attestation, and a payment system. This article is oriented toward practitioners interested in designing and implementing secure and privacy-preserving networks for smart V2G applications.
Article
This paper presents a physically unclonable function (PUF) based on the randomness of soft gate oxide breakdown (BD) locations in MOSFETs, namely, soft-BD PUF . The proposed PUF circuit features a self-limiting mechanism that generates exactly one soft-BD spot in a pair of NMOS transistors. Highly stable “0” and “1” bits with an equal probability of 0.5 are extracted based on the locations of the generated BDs. A differential readout scheme is employed based on the proposed reference-free sense amplifier (SA), resulting in good current sensitivity and side-channel attack resilience. The soft-BD PUF, fabricated in a 40-nm CMOS process, comprises all essential periphery circuits. Measurements show that the soft-BD PUF has good data stability in a wide operating range. The native bit error rate is 0% for $V_{\text {DD}}=1\,\,\text {V}$ and above, shown by measuring 10k readout cycles among 10k PUF cells. Data stability degrades at lower supply voltage and higher temperature due to the conductivity of PUF cells and the offset of SAs. Under the nominal $V_{\textrm {DD}}$ of 0.9 V in this technology, the throughput is shown to be at least 40 Mb/s and the PUF readout consumes only 51.8 fJ/bit. The averaged hamming weight and hamming distance are 0.497 and 0.496, respectively, showing a good randomness and uniqueness. The resulting PUF data show good statistical properties by passing all the relevant tests in the NIST 800-22 suite.
Article
The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing. However, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities. Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on publickey cryptographic primitives such as homomorphic encryption, elliptic curve digital signature algorithms (ECDSA), etc. which are ill-suited for the resource constrained smart meters. On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed. However, these schemes cannot ensure some of the imperative security properties such as consumer’s privacy, sender authentication, etc. In this paper, we first propose a lightweight and privacyfriendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids. Our scheme only uses lightweight cryptographic primitives such as hash functions, exclusive-OR operations, etc. Subsequently, we propose a secure billing solution for smart grids. As compared to existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids.
Article
Embedded systems play a crucial role in fueling the growth of the Internet-of-Things (IoT) in application domains such as healthcare, home automation, transportation, etc. However, their increasingly network-connected nature, coupled with their ability to access potentially sensitive/confidential information, has given rise to many security and privacy concerns. An additional challenge is the growing number of counterfeit components in these devices, resulting in serious reliability and financial implications. Physically Unclonable Functions (PUFs) are a promising security primitive to help address these concerns. Memory-based PUFs are particularly attractive as they require minimal or no additional hardware for their operation. However, current memory-based PUFs utilize only a single memory technology for constructing the PUF, which has several disadvantages including making them vulnerable to security attacks. In this paper, we propose the design of a new memory-based combination PUF that intelligently combines two memory technologies, SRAM and DRAM, to overcome these shortcomings. The proposed combination PUF exhibits high entropy, supports a large number of challenge-response pairs, and is intrinsically reconfigurable. We have implemented the proposed combination PUF using a Terasic TR4-230 FPGA board and several off-the-shelf SRAMs and DRAMs. Experimental results demonstrate substantial improvements over current memory-based PUFs including the ability to resist various attacks. Extensive authentication tests across a wide temperature range (20 - 60 deg. Celsius) and accelerated aging (12 months) demonstrate the robustness of the proposed design, which achieves a 100% true-positive rate and 0% false-positive rate for authentication across these parameter ranges.
Article
The concept of the Social Internet of Things (SIoT) can be viewed as the integration of prevailing social networking and the Internet of Things (IoT), which is making inroads into the daily operation of many industries. Smart grids, which are cost-effective and environmentally-friendly applications, are a promising field of the SIoT. However, security and privacy concerns are the dark aspects of smart grids. The goal of this paper is to address the security and privacy issues in the vehicle-to-grid (V2G) networks with the intention of promoting a more extensive deployment of V2G networks for smart grids. Driven by this motivation, in this paper, we propose a robust key agreement protocol that can achieve mutual authentication without exposing the real identities of users. Efficiency is also a major concern in resource-constrained environments. By leveraging only hash functions and bitwise exclusive-OR (XOR) operations, the proposed protocol is highly efficient compared with pairing-based protocols. In addition, we define a formal security model for our privacy-preserving key agreement protocol for V2G networks. Using this model, a formal security analysis shows that the proposed protocol is secure. Moreover, an informal security analysis demonstrates that our protocol can withstand different types of attacks.
Article
Vehicle-to-Grid (V2G) systems promoted by the federated Internet of Things (IoT) technology will be ubiquitous in the future; therefore, it is crucial to provide trusted, flexible and efficient operations for V2G services using high-quality measures for security and privacy. These can be achieved by access and authority authentication. This paper presents a lightweight protocol for capacity-based security access authentication named AccessAuth. Considering the overload probability and system capacity constraints of the V2G network domain, as well as the mobility of electric vehicles, the ideal number of admissible access requests is first calculated adaptively for each V2G network domain to actively achieve capacity-based access admission control. Subsequently, to provide mutual authentication and maintain the data privacy of admitted sessions, by considering whether there is prior knowledge of the trust relationship between the relevant V2G network domains, a high-level authentication model with specific authentication procedures is presented to enforce strict access authentication such that the sessions are conducted only by authorized requesters. Additionally, efficient session revocation with forward security and session recovery with no extra authentication delay are also discussed. Finally, analytical and evaluation results are presented to demonstrate the performance of AccessAuth.