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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
DECEMBER 2020 545
An Attribute-Isolated Secure CommunicationArchitecture for
Intelligent Connected Vehicles
Mu Han , Member, IEEE, Ailan Wan, Fengwei Zhang , and Shidian
Ma
Abstract—The rapidly increasing connectedness of modern
ve-hicles leads to new security challenges for intelligent
connectedvehicles (ICVs), where some potential attackers can
achieve unau-thorized access to gain control of the vehicle by
injecting mali-cious information into in-vehicle electronic control
units (ECUs).Therefore, in this paper, a secure attribute-isolated
communicationarchitecture for an ICV, which introduces attributes
into the ECUsto achieve authorized access among the ECU nodes is
proposed.First, an analysis of the functional attributes of all of
the in-vehicleECUs in an intelligent connected environment and a
division of thefunctional attributes of the ECUs into five
classifications are per-formed. Second, based on the
above-classified attributes, a secureattribute-isolated
communication architecture is demonstrated.The ECUs have different
access rights, allowing only the ECUs withthe same functional
attributes in the internal network of the vehicleto communicate.
Then, it is proven that the proposed architecturecan resist forgery
and eavesdropping attacks under the randomoracle model. Finally,
the secure attribute-isolated communicationarchitecture is
constructed in a hardware environment and evalu-ated with an
in-vehicle network simulator (IVNS). The evaluationresults show
that the average memory usage with 120 ECUs and100 messages is
below 40 MB and the bus load can be reduced to18.96% using the
proposed security architecture compared to thebus load of existing
architectures. Therefore, the proposed secureattribute-isolated
communication architecture solves the problemof the tradeoff
between the security threat of unauthorized accessand the high bus
load of existing in-vehicle architectures.
Index Terms—ICV, in-vehicle network, security,
attribute-isolated architecture, ECU functional attributes.
Manuscript received October 16, 2018; revised January 16, 2019,
June 21,2019, and January 30, 2020; accepted September 14, 2020.
Date of publicationSeptember 29, 2020; date of current version
November 23, 2020. This workwas supported in part by the Six Talent
Peaks Project of Jiangsu Province(DZXX-012), in part by Natural
Science Fund for Colleges and Universitiesin Jiangsu Province
(12KJD580002), in part by Jiangsu Graduate InnovationFund
(KYLX_1057), and in part by Key Research and Development Plan
ofJiangsu province in 2017 (Industry Foresight and Generic Key
Technology)(BE2017035). (Corresponding author: Mu Han.)
Mu Han is with the School of Computer Science and Communication
En-gineering, Jiangsu University, Zhenjiang 212013, China, and also
with theCOMPASS Lab, Wayne State University, Detroit, MI 48202 USA
(e-mail:[email protected]).
Ailan Wan is with the School of Computer Science and
Communi-cation Engineering, Jiangsu University, Zhenjiang 212013,
China (e-mail:[email protected]).
Fengwei Zhang was with the COMPASS Lab, Department of
ComputerScience, Wayne State University, Detroit, MI 48282 USA and
is now with the De-partment of Computer Science and Engineering,
Southern University of Scienceand Technology, Shenzhen 518000,
China (e-mail: [email protected]).
Shidian Ma is with the Automotive Engineering Research
Institute, JiangsuUniversity, Zhenjiang 212013, China (e-mail:
[email protected]).
Color versions of one or more of the figures in this article are
available onlineat https://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIV.2020.3027717
I. INTRODUCTION
R ECENTLY, the rapid convergence of vehicles and infor-mation
technology has resulted in a rapid increase inmodern vehicles
connected to the Internet [1]. Such a connectioncan make vehicles
become rich sources of data, including bothpersonal and vehicular
information. However, it also meansthat vehicles inevitably become
lucrative targets for hackers.In 2013, it was reported that a mass
production hybrid vehiclehad been cracked by hackers [2], who
illegally manipulated thebrake system through the on-board
diagnostics (OBD) interface,allowing the hackers to cause traffic
accidents and threaten thelives of the occupants. In 2015, the
details of security attackson a SUV have been unveiled [3]. The
attackers were ableto remotely invade the electronic control units
(ECUs) throughin-vehicle entertainment systems, and achieve remote
controlof the vehicle’s speed, air conditioning and windshield
wipers.Subsequently, in 2017, other researchers hacked some
othercars. They showed that they could remotely control the
vehi-cle including critical vehicle controls. They showed that
theycould remotely control the vehicle including critical
vehiclecontrols [4].
The security loopholes mentioned above originated from
thelimitations of the traditional in-vehicle network
architecture:1) The traditional in-vehicle network architecture is
a closedenvironment, that is insufficiently adapted to the open
envi-ronment of modern intelligent connected vehicles (ICVs)
[5].Any devices connected to the vehicle can obtain access to
thein-vehicle information via Wi-Fi, Bluetooth or OBD
interfaces.This increase in interconnections expands the attack
surface ofthe vehicle [6], [7]; 2) The communication framework of
thein-vehicle network has broadcast characteristics, in which
theECUs (nodes) exchange information in the form of plaintext
[8].Each ECU can communicate with other ECUs without
requiringsource or destination addresses. Hence, an attacker who
infil-trates an ECU can easily impersonate any other ECU and
finallyachieve remote control of the vehicle.
In this paper, an ECU access control mechanism for an ICVis
designed, which achieves attribute-isolated communicationamong all
of the ECUs. The ECUs’ access control mechanismsolves the security
threat of unauthorized access. Additionally,it reduces the high bus
load of existing in-vehicle architectures.The main contributions of
this paper are as follows:
1) An analysis of the functional attributes of in-vehicle ECUsis
performed. According to the impact of the passenger’s func-tional
requirements and the traffic environment on vehicles un-der
intelligent connected environment, we divide the functional
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https://orcid.org/0000-0003-2982-5578https://orcid.org/0000-0003-3365-2526https://orcid.org/0000-0002-0634-9938mailto:[email protected]:[email protected]:[email protected]:[email protected]://ieeexplore.ieee.org
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546 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
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attributes of ECUs into five classifications: perception,
decision,control, execution, and service.
2) Based on the above classified ECU functional attributes,we
propose an innovative in-vehicle secure
attribute-isolatedcommunication architecture. The architecture
allows the ECUswith the same functional attributes in the internal
network of thevehicle to communicate and isolates the ECUs with
differentfunctional attributes, achieving the purpose of access
controland reducing the bus load. Then we prove that the
proposedarchitecture can resist a forgery attack and an
eavesdroppingattack under the random oracle model.
3) We construct the attribute-isolated communication
archi-tecture in a hardware environment and evaluate the
architecturewith an in-vehicle network simulator (IVNS). The
evaluationresults indicate that the architecture is more efficient
than ex-isting schemes in terms of computation time, average
storageconsumption and bus load.
The rest of this paper is organized as follows: In Section II,we
review more related works. In Section III, ECU functionalattributes
are classified. In Section IV, a novel
attribute-isolatedcommunication for the in-vehicle network is
proposed. InSection V, we present a theoretical analysis of the
security forthe novel architecture. In Section VI, an evaluating
experimentfor the novel architecture is conducted and discussed.
Finally,Section VII presents the conclusions and future work
resultingfrom this study.
II. RELATED WORK
Researchers have been moving forward to design a
securein-vehicle network architecture to solve the information
securityproblem of the in-vehicle network under the ICV
environment.The first method to ensure in-vehicle network security
was pre-sented by Wolf et al. [9], who constructed a broadcast
communi-cation architecture based on the technology of encryption.
In thisarchitecture, all of the ECUs are connected to a gateway
elec-tronic control unit (GECU), and encrypted secret information
istransmitted between the GECU and the others. Another approachto
secure in-vehicle communication was proposed by Nilssonet al. [10],
who first introduced message authentication codes(MACs) to
authenticate the ECUs, overcoming the shortcomingthat an ECU
identity is easily impersonated. Nevertheless, theauthentication
process increases the load of the controller areanetwork (CAN) bus,
making the approach unsuitable for anin-vehicle real-time
communication environment.
Groza et al. proposed a series of lightweight broadcast
authen-tication communication solutions for an in-vehicle CAN
suchas EPSB (efficient protocols for a secure broadcast in
controllerarea networks) and Libra-CAN (a lightweight broadcast
authen-tication protocol for controller area networks) [11]–[13].
In theirscheme, ECUs implement broadcast authentication
protocolsbased on key-chains and time synchronization, meanwhile
thelimited data payload of the CAN data frame in the
authenticationprocess is considered. However, the total number of
data framesin the vehicle network doubles at minimum when the
datapayload is used for MAC in these schemes, since it requiresone
data frame containing the original data and at least one data
frame containing the MAC. Hence, these schemes (includingthe
full-length MAC) rapidly increase the load of the CAN busand are
not suitable for deployment in the vehicle environment.Jackson et
al. went a step further, using a truncated MACcode (Mini-MAC code)
to reduce the consumption of in-vehiclelimited resource [14], but
this approach weakened the securityof the scheme and interactive
information among the ECUs canbe leaked easily.
In 2012, Robert Bosch GmbH developed a new communi-cation
protocol [15], known as CAN with flexible data rate(CAN-FD) to
solve the problem of the existing security archi-tectures are
inapplicable of directly assisting in-vehicle CANbecause of the
limited data payload [16]. The CAN-FD designis based on CAN, with
the following advantages: First, it has ahigher bandwidth and a
larger data payload. Second, its physicallayer and topologies can
be maintained. Soon afterward, Samuelet al. proposed a practical
security architecture (PSAC) forin-vehicle CAN-FD [17], [18]. In
PSAC, ECUs derive sessionkeys with a GECU in a fixed order and
perform authenticationand encryption based on the Keyed-Hash MAC
and advancedencryption standard (AES). Patsakis et al. proposed a
distributedsecure in-vehicle communication architecture (DSCA) for
mod-ern vehicles under a CAN-FD [19]. In the DSCA, the ECUs
par-ticipate in a secure multi-party computation scheme to
performauthentication and encryption. However, all of the ECUs
needto perform decryption, which rapidly increases the bus
load,limiting the applicability of this approach in real-time
vehiclesystems.
The above architectures under a CAN-FD do not fully con-sider
the access control mechanism, and unauthorized attackerscan also
receive the in-vehicle private information. Meanwhile,the bus load
of these architectures is high. In this paper, wepropose an
isolated architecture based on ECU functional at-tributes under
CAN-FD. The proposed architecture not onlyhas an access structure
but also can reduce the bus load whencompared with [18], [19].
III. ECU FUNCTIONAL ATTRIBUTE CLASSIFICATION
A. Attribute Clustering
The traditional vehicle is a typical driver-centered system.As
shown in Fig. 1, a driver perceives changes of the
trafficenvironment through visual and auditory senses.
Meanwhile,the driver judges the current environment through their
brainand makes driving decisions to control the movement of
theirhands and feet, completing the manipulation of the
vehicle.
With the rapid development of artificial intelligence, inter-net
technology, communication technology and computer tech-nology, ICV
based on electrification, intellectualization andnetworking has
become a significant trend in the automotiveindustry. An ICV is
mainly embodied by the replacement ofmanual operation with
automatic driving, which can compensatefor the shortcomings of the
human sensory ability and reducethe driving manipulation intensity.
The behaviour and runningstate of the vehicle are controllable and
predictable. Therefore,traffic accidents caused by human factors
can be eliminated, andtravel paths can be planned according to
real-time road condition
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Fig. 1. The manipulation of the traditional vehicle [20].
Fig. 2. The manipulation of the ICV [23].
information. Ultimately, zero casualties and zero congestion
inthe road transport can be achieved [21].
As shown in Fig. 2, an ICV integrates multiple informationand
physical function modules such as an environmental aware-ness
module, intelligent decision module, collaborative controlmodule,
secure execution module and service module. TheICV can realize
safe, comfortable, energy-saving, and efficientdriving, and can
eventually replace a new generation of vehiclesoperated by human
beings [22].
These functional modules of the ICV are made possible bya range
of 50 to 70 in-vehicle computers networked together,called
electronic control units (ECUs) [24].The ECUs exchangeinformation
with remote access equipment through wirelesscommunication to sense
traffic information. Meanwhile, theECUs transmit operation data and
control instructions throughthe in-vehicle network (CAN), as shown
in Fig. 3.
Based on an analysis of the manipulation of the
traditionalvehicle, the manipulation of the ICV and the in-vehicle
network
Fig. 3. In-vehicle network system.
TABLE IATTRIBUTE CLUSTERING OF ECUS
system, we classify the functional attributes of ECUs into
fivefunctional attributes: AttP, AttI, AttC, AttS1 and AttS2, as
shownin Table I.
B. The Scalability of Attribute Clustering
Based on the above-classified ECU functional attributes,
aninnovative in-vehicle attribute-isolated broadcast communica-tion
architecture is constructed. We will show that the
attributeclustering of ECUs is scalable for the novel
architecture.
1) The scalability of the ECU function attributes. The
tra-ditional vehicle is a typical driver-centered system, which
per-ceives changes of the traffic environment, judges the
currentenvironment and forms driving decisions, completing the
manip-ulation of the traditional vehicle. Compared with the
traditionalvehicle, an ICV is mainly embodied by replacement of
manualoperation. In an ICV system, there are 50 to 70 ECUs
networkedtogether, which like the human brain, hands, eyes and
feet,achieve environmental awareness, intelligent
decision-making,collaborative control, secure execution, and
service. If a newECU is added to the ICV, its functional attribute
of it should bein the above classified functional attributes.
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548 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
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Fig. 4. The isolated communication based on ABS access
structure.
2) Attribute communication is scalable, that is, ECUs with
anyone of the same attributes can perform
isolated-communication.For example, ABS has three functional
attributes {AttS1, AttI,AttC }, where AttS1 is the main functional
attribute of the ABS.Setting the access structure for the ABS based
on its functionalattributes, and allowing other ECUs with one of
the abovethree functional attributes to exchange information. Fig.
4 is adiagram of the isolated communication based on the ABS
accessstructure, which shows that leaf nodes are composed of
ECUfunctional attributes and each non-leaf node consists of a pair
ofthreshold values (1, 2). Hence, the other ECUs with
functionalattributes {AttS1}, {AttI }, {AttC}, {AttS1, AttI},
{AttS1, AttC},{AttI, AttC}, {AttS1, AttI, AttC} can exchange
informationwith the ABS, e.g., the braking ECU with functional
attributes{AttS1, AttC} can communicate with the ABS. However,
theair conditioner ECU with different functional attributes
{AttP,AttS2} cannot communicate with the ABS.
IV. IN-VEHICLE ATTRIBUTE-ISOLATED COMMUNICATIONARCHUTECTURE
In this section, based on the above ECU attribute clustering,the
proposed in-vehicle attribute-isolated communication archi-tecture
is elaborated. The novel architecture consists of a gate-way
electronic control unit (GECU) and electronic control units(ECUs)
which are equipped in vehicles. The specific functionsare as
follows:
GECU: The GECU1 functions as the trust authority [28]
andverifies the identity of the ECUs. Meanwhile, the GECU
hassufficient computation power and storage capacity, typicallywell
above those of a general ECU.
ECU: Before being allowed to interact with information, theECUs
must register with the GECU. To function as a senderECU, it needs
to set an access structure for the receiver ECUsbased on its
functional attributes. Two ECUs can only commu-nicate when the
functional attributes of a receiver ECU satisfythe access
structure.
The proposed in-vehicle attribute-isolated communica-tion
architecture consists of five phases, namely
“systeminitialization”, “registration”, “setting the access
structure”,
1GECU is the trusted third party and is free from security
leakages.
TABLE IINOTATIONS AND DESCRIPTIONS
“attribute-isolated communication” and “updating the
ciphertextand attribute private key”.
P1. System initialization. The GECU publishes the
publicparameters and generates the master key.
P2. Registration. The ECUs register their identities
informa-tion to the GECU.
P3. Setting the matching strategy. We set the matching
strategyfor the access structure of the ciphertext and the
attribute privatekey.
P4. Attribute-isolated communication. The ECUs
performattribute-isolated communication based on the matching
strat-egy.
P5. Updating the ciphertext and attribute private key. Thisphase
prevents attackers from obtaining in-vehicle private data.
In the following, we present the details of each phase.
Thenotations used in the five phases are listed in Table II.
A. System Initialization
Step 1: The GECU inputs the secure parameter k and gen-erates
two additive groups G0 and a multiplicative group G1.Define a
bilinear mapping e : G0 ×G0 → G1, and two genera-tors p1, p2 of G0
and G1, respectively, where G0 and G1 haveprime order q.
Step 2: The GECU randomly choosesα, β, θ ∈ Z∗q and a
hashfunction H : {0, 1}∗ → Z∗q .
Step 3: The GECU publishes the public parameters:{G0, G1, e, H,
p1, p2, θp2, θ2p2, Y = e(p1, p2)α(β−1),
e(p1, p2)αβ}. Meanwhile, the public key is PKGECU =
SKGECUP2, and the master key is MK = SKGECU = αp1.
B. Registration
After completing the system initialization, the GECU per-forms
the registration phase to verify the ECU identity, prevent-ing
attackers from impersonating the ECU identity. Algorithm 1indicates
the registration process. The concrete steps are asfollows.
Step 1: ECU1 sends registration request information to theGECU.
The registration information is generated as follows.
1) ECUI chooses (PKECUI , SKECUI ) as its public andprivate key
pairs, where SKECUI = r1(r1 ∈ Z∗q), PKECUI =SKECUIp2.
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HAN et al.: ATTRIBUTE-ISOLATED SECURE COMMUNICATION ARCHITECTURE
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2) ECUI computes H(IDECUI ) by its identity IDECUIand selects a
secret random number r2 (r2 ∈ Z∗q) to generatethe request
information (VECUI ,WECUI ) where (VECUI =PKECUIH(IDECUI )), WECUI
= r2p2.
3) ECUI signs the request information (VECUI ,WECUI )through
SKECUI to obtain the signature information SigI =sigSKECUI
(VECUI‖WECUI ).
4) ECUI computes H(IDECUI ) and uses PKGECUto encrypt VECUI ,
WECUI and H(IDECUI ). ECUI sendsMsg1(EPKGECU (VECUI‖WECUI‖H(IDECUI
))‖SigI‖t) toGECU.
Step 2: After receiving Msg1, the GECU takes the
followingactions to verify the legitimacy of ECUI .
1) The GECU verifies the validity of the timestamp by for-mula
(1), where t′ is the current time. T is the maximum timedifference
allowed for the vehicle.
(Δt = t′ − t) < T (1)2) Once verified successfully, the GECU
decrypts EPKGECU
(VECUI‖WECUI‖H(IDECUI )) in Msg1 by SKGECU andobtains VECUI ,
WECUI and H(IDECUI ).
3) The GECU confirms the correctness of the requestinformation
by verifying SigI . The GECU uses formula(2) to verify SigI . If V
erPKECUI (VECUI‖WECUI , SigI) =V erPKECUI (VECUI‖WECUI , SigSKECUI
(VECUI‖WECUI ))= true, it indicates that Msg1 has not been forged.
Otherwise,the GECU discards Msg1.
V erPKECUI (x, y) =
{true y = sigSKECUI (x)
false y �= sigSKECUI (x)(2)
wherex is the request information (VECUI ,WECUI ) and y is
thesignature information SigI = sigSKECUI (VECUI‖WECUI ).
4) GECU randomly selects r3 ∈ Z∗q and computes R =WECUI + r3p2,
L = r3 + SKGECUVECUI . GECU verifiesthe legal identity of ECUI by
formula (3). If formula (3) isestablished, it indicates that the
identity of ECUI is legal.Otherwise, ECUI is forged.
LP +WECUI = R+ PKGECUVECUI (3)
The process of the verification is shown in formula (4).
Ifformula (4) is established, it indicates that the identity of
ECUIis legal. Otherwise, ECUI is forged,and the GECU refuses
therequest information and terminates the session.
LP +WECUI = (r3 + SKGECUVECUI )p2 + r2p2
= (r3 + SKGECUVECUI + r2)p2
= (r2 + r3)p2 + SKGECUVECUIp2
= R+ PKGECUVECUI (4)
5) After verifying the legal identity of ECUI , the GECUstores
the set of legal ECUs and returns the successful regis-tration
information Msg2(EPKGECU (H(IDECUI ))‖t′) to theECU.
Step 3: ECUI decrypts EPKGECU (H(IDECUI )) in Msg2through SKECUI
. This indicates that ECUI successfully reg-isters in the GECU.
Fig. 5. Matching strategy for the access structure and attribute
private key.
Algorithm 1: ECU Registration Protocol(ECU_REGISTRA-TION).
1: ECUI : Generate the registration request information2: ECUI →
GECU :
Msg1(EPKGECU (VECUI ‖WECUI ‖H(IDECUI ))‖SigI‖t)where VECUI =
PKECUIH(IDECUI ), WECUI = r2p2
3: GECU: Verify the legitimate identity of ECUI4: if (Δt = t′ −
t) < T is valid then
GECU: Decrypt EPKGECU (VECUI ‖WECUI ‖H(IDECUI ))in Msg1 by
SKGECU and verify Sig1elseGECU: Refuse the request
informationendif
5: if V erPKECUI (Sig1, VECUI ‖WECUI ) = ture, thenGECU: Compute
R = WECUI + r3p2,L = r3 + SKGECUVECUIelseGECU: Refuse the request
information
6: if Lp2 +WECUI = R+ PKGECUVECUI then GECU: Thelegal identity
of the ECU is successfully authenticated, and thenGECU → ECUI :
Msg2(EPKGECU (H(IDECUI ))‖t′)endif
7: ECUI : Successfully registeredendif
C. Setting the Matching Strategy
After the ECUs successfully register in the GECU, we setthe
matching strategy for the access structure of the ciphertextand the
attribute private key. As shown in Fig. 5, the in-vehicleECUs are
logically separated from their access rights. TheECUs are described
by functional attributes and obtain attributeprivate keys according
to their functional attributes. The ECUscan communicate only when
their attribute private keys can
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550 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
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Fig. 6. Access structure of ABS.
Fig. 7. In-vehicle attribute-isolated communication.
decrypt the ciphertext. Therefore, the matching strategy
realizesthe isolated communication among the ECUs with the
samefunctional attributes.
For example, the antilock brake system (ABS), which canquickly
judge the lock state of a wheel according to the speedsignal from
each wheel speed ECU to ensure the vehicle safety,has three
functional attributes: AttI: the perception functionattribute,
AttC: the collaborative control function attribute, andAttS1: the
secure execution function attribute. We set the accessstructure for
ABS based on its functional attributes, as shown inFig. 6. In
addition,the ABS generates the attribute private keybased on its
functional attributes. Therefore, the attribute privatekey matches
the access structure based on the ABS functionalattributes.
D. Attribute-Isolated Communication
Based on the above matching strategy and
ciphertext-policyattribute-based encryption algorithm (CP-ABE), the
ECUs per-form attribute-isolated communication, thereby allowing
onlythe authorized ECUs to obtain the in-vehicle private
information.The specific design of our scheme is shown in Fig. 7.
We takeECUI and ECUJ to show how the ECUs can communicate,ECUI and
ECUJ have the same functional attributes Atti. Theconcrete steps
are as follows.
1) ECUI Generates the Ciphertext: ECUI performs thefollowing
steps to broadcast the ciphertext:
i) ECUI sets the access structure ACPI as shown in Fig. 1.Every
non-leaf node of the tree represents a threshold kr and thenumber
of a children node numx (1 ≤ numx ≤ 5). Each leafnode r of the tree
is described by its functional attributes.
ii)ECUI chooses s ∈ Z∗q and a polynomial qr of degree dr =kr − 1
inACPI , where kr is the threshold of the node r inACPI ,qr(0) = s.
Let Nr denote the set of all leaf nodes in ACPI .
Fig. 8. Access Structure.
ECUI computes the ciphertext by formula (6) as follows:
CTIJ = (ACPI , C̃, C, C′, ∀r ∈ Nr : C ′r, C ′′r ) (5)
Where C̃ = e(p1, p2)αβsM , C = sp1, C ′ = Y s, C′r =
qr(0)H(Atti)p2 + qr(0)θ2p2, andC
′′r = qr(0)θp2).
Meanwhile, ECUI broadcasts MsgB1(CTI ||T1) in the ve-hicle.
2) ECUJ ObtainsM From the Ciphertext: ECUJ performsthe following
steps to achieve M from the broadcast ciphertext.
i) ECUJ takes Atti,MK as an input, where Atti isthe functional
attribute of ECUI . ECUJ randomly selectskj , lj ∈ Z∗q for each
attribute Atti ∈ Sj and then computeshj = H(Atti). Subsequently,
ECUJ computes the attributeprivate key SKECUJCP−ABE as: SK
ECUJCP−ABE = (Dj , ∀Atti ∈ Sj :
D′j , D′′j ) where Dj = αp2 + ljkjp2, D
′j = kjp1 + hjp1, D
′′j =
ljkjθp1.ii) ECUJ uses the recursive algorithm DecryptNode(CTI
,
SKECUJCP−ABE , r) and inputs CTI , SKECUJCP−ABE and node r
of the access structure ACPI . SKECUJCP−ABE is related to
the
attribute set Sj and the attribute Atti. The definition
ofDecryptNode(CTI , SK
ECUJCP−ABE , r) is as follows:
DecryptNode(CTI , SKECUJCP−ABE , r) =
e(D′j , C
′r)
e(D′′j , C
′′r)
= e(ljkjp1, qr(0)p2)
= e(p1, p2)ljkjqr(0)
(6)
We denote:B = DecryptNode(CTI , SK
ECUJCP−ABE , r) = e(p1,
p2)ljkjs, where qr(0) = s.
After performing DecryptNode(CTI , SKECUJCP−ABE , r),
ECUJ can obtain M as follows:
Decrypt(CTI ,SKECUJCP−ABE)=
B · C̃e(C,Dj) · C ′
=e(p1,p2)
ljkjs·e(p1, p2)αβsMe(p1,p2)s(α+ljkj)·e(p1,p2)α(β−1)s
=M · e(p1, p2)s(α−α)
=M (7)
E. Updating the Ciphertext and Attribute Private Key
The proposed attribute-isolated communication
architectureintroduces a counter mechanism in the traditional
CP-ABE
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HAN et al.: ATTRIBUTE-ISOLATED SECURE COMMUNICATION ARCHITECTURE
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scheme to update the ciphertext and attribute private key of
eachECU. We specify that the counter is integrated into the
accessstructure when the sender ECU generates the ciphertext
eachtime, hence the receiver ECUs must synchronize the countersand
update their own attribute private key. This effectivelyprevents
attackers from obtaining in-vehicle private data. Theconcrete steps
are as follows.
1) ECUI Updates the Ciphertext: When a vehicle stalls,any
broadcast interaction ends, and ECUI will invalidate itsprevious
ciphertext CTI . The next time the vehicle is started,ECUI must
generate a new ciphertext as follows.
i)ECUI manages its own counter valueCTRI and computesH(Atti ⊕
CTRI), and then generates the new ciphertext CT ′Iby formula
(8).
CT ′I = ACPI , C̃, C, C′, ∀r ∈ Nr : C ′rnew, C ′′r ) (8)
where C̃ = e(p1, p2)αβsM , C = sp1, C ′ = Y s, C ′rnew
=qr(0)H(Atti ⊕ CTRI)p2 + qr(0)θ2p2, andC ′′r = qr(0)θp2).
ii) ECUI broadcasts the new ciphertext CT ′I in the vehicleand
increments CTRI .
2) ECUJ Updates the Attribute Private Key: After receiv-ing the
ciphertext, ECUJ manages the counter of ECUJand computes h′j =
H(Atti ⊕ CTRI), and then generatesthe new attribute private key SK
′ECUJCP−ABE as SK
′ECUJCP−ABE =
(Dj , ∀Atti ∈ Sj : D′iknew, D′′ik) where Dik = αp2 +
ljkjp2,D′iknew = kjp1 + h
′jp1, and D
′′j = ljkjθp1. ECUJ increments
the counter CTRI of ECUI .
V. SECURITY ANALYSIS OF THE PROPOSED SCHEME
In this section, we theoretically prove that the proposed
archi-tecture can resist forgery attack and eavesdropping attack
underthe random oracle model.
Theorem 1: Assuming that the Discrete Logarithm (DL) as-sumption
is established, the ECU ID in the proposed attribute-isolated
architecture can resist a forgery attack.
Proof: Assume that the attacker A can fake the real iden-tity
information IDECUI of the legal ECU and generate avalidmessage
Msg1, that is, A can calculate the effective valueVECUI =
PKECUIH(IDECUI ) of Msg1. The advantage of Aattack success isAdvA.
We useA to construct an algorithmADLto solve the DL problem. �
ADL randomly chooses θ ∈ Z∗q , publishes the public param-eters:
{G0, G1, e,H, p1, p2, θp2, θ2p2, Y = e(p1, p2)a(b−1),e(p1, p2)
ab} and saves the master key MK = ap2 secretly. Acan make
queries about ADL to qDL times.
Query: A makes queries about IDECUI , and algorithm ADLreturns
VECUI = PKECUIH(IDECUI ) to A.
Challenge: After A receives VECUI , A uses (PKECUI ,VECUI ) to
call algorithm ADL and lets H(IDECUI ) = a. Thatis given PKECUI ,
aPKECUI , compute a.
The advantage of A challenge success in this process isAdvA =
qDL ·AdvDL. As can be seen from the difficult prob-lems described
in the preliminaries, the advantage AdvA ofthe algorithm in
successfully solving the DL problem in thepolynomial time is
negligible, hence the attacker A cannotcounterfeit the ECU ID.
Theorem 2: Provided that the DL assumption is established,the
ECU attributes in the proposed attribute-isolated
architecturecannot be obtained.
Proof: Assume that the attacker A can obtain the real
at-tributes Atti of the ECU, that is, A can calculate the
effectivevalue H(Atti)p1 in the ECU attribute private key SK
ECUJCP−ABE .
The advantage of A successful attack is AdvA. We use A
toconstruct algorithm ADL to solve DL problem. �ADL randomly
chooses θ ∈ Z∗q , publishes the pub-
lic parameters: {G0, G1, e,H, p1, p2, θp2, θ2p2, Y =
e(p1,p2)
a(b−1), e(p1, p2)ab} and saves the master key MK = ap2secretly.
A can make queries about ADLto qDL times.
Query: A makes queries about the real attributes Atti of theECU,
and ADL returns H(Atti)p1 to A.
Challenge: After A receives H(Atti)p1, A uses(p1, H(Atti)p1) to
call algorithm ADL and lets H(Atti) = a.That is given p1, ap1,
compute a. The advantage of A challengesuccess in this process
isAdvA = qDL ·AdvDL. The advantageAdvA of the algorithm in
successfully solving the DL problemin the polynomial time is
negligible. Hence the attackerA cannotobtain the ECU attributes and
generate the ECU attribute privatekey.
Theorem 3: Assuming the DBDH (Determine Bilinear Diffie-Hellman)
problem is difficult, then the scheme designed in thispaper is CCA
(Chosen-Ciphertext Attack) secure.
Proof: Challenger C randomly chooses θ ∈ Z∗q , publishesthe
public parameters: {G0, G1, e,H, p1, p2, θp2, θ2p2, Y =e(p1,
p2)
a(b−1), e(p1, p2)ab} and saves the master key MK =ap2. �
Phase 1: C generates the attribute private key accord-ing to the
attribute set Si of the ECU: SK
ECUICP−ABE(Di).
Then C generates CT and broadcasts it according to formula(5) in
the attribute-isolated communication process: CT =ACP , C̃, C,
C
′, C ′r, C′′r ).
The attacker A intercepts ciphertext CT from the
broadcastmessages by eavesdropping and sends CT to C. C uses
thedecryption algorithm to obtain M and transmits it to A.
Challenge:A chooses two equal messagesM0,M1 and accessstructure
A∗CP . A sends (M0,M1, A
∗CP ) to C. C sets r to be
the set of the root node in A∗CP after receiving (M0,M1, A∗CP
).
Then C randomly selects b′ ∈ {0, 1} and computes:
CT ∗ = (A∗CP , C̃ = Z ·Mb′ ,
C = cp1, C′ =
Z
e(ap1, cp2),
∀r ∈ Nr :C ′r = qr(0)H(atti)p2 + qr(0)θ
2p2,
C ′′r = qr(0)θp2)
Finally, C returns CT ∗ to A.Phase 2: All of the queries in
Phase 1 can be performed during
Phase 2. However, if A asks for the decryption algorithm inthe
attribute-isolated communication phase, C will abort
thesimulation.
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552 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
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Guess: A outputs b′′ ∈ {0, 1} as a guess for b′. If b′′ = b′,
Awill win the game. Otherwise, A fails. If the attacker A canwin
the CCA game in polynomial time with non-negligibleadvantage ε,
then C can solve the DBDH enlarge 20% problemwith non-negligible
advantage ε′, where ε′ ≥ 12 (ε− δ) and δ isa negligible advantage.
The probability analysis is as follows.
If Z = e(p1, p2)abc, we can achieve thatPr[A(p1, p2, ap1, bp1,
cp1, ap2, bp2, cp2, e(p1, p2)
abc) = 1]= Pr[b′′ = b′] where |Pr[b′′ = b′]− 12 | ≥ ε.
Otherwise, if Z israndomly chosen from G1, then
Pr[A(p1, p2, ap1, bp1, cp1, ap2, bp2, cp2, Z) = 1] = Pr[b′′ =
b′]
Where |Pr[b′′ = b′]− 12 | ≤ δ and δ is the advantage of
break-ing the semantic security and can be ignored. Hence, we
canpresent that
|Pr[A(p1, p2, ap1, bp1, cp1, ap2, bp2, cp2, e(p1, p2)abc) = 1]−
Pr[A(p1, p2, ap1, bp1, cp1, ap2, bp2, cp2, Z) = 1]|≥ ε− δTherefore,
we can conclude that the proposed scheme satisfies
CCA secure.
VI. SIMULATION AND EVALUATION
In this section, we first constructed the attribute
isolatedarchitecture using STMicroelectronics’s automotive
microcon-trollers to extract the hardware parameters and then
evaluated thearchitecture using the software In-Vehicle Network
Simulator(IVNS). We compared the proposed architecture with the
archi-tecture suggested in [18] and [19] in terms of the
computationtime, average storage consumption, and bus load.
A. Simulation
1) The Construction of the Attribute Isolated Architecture inthe
Hardware Environment: To ensure that the software simu-lation
follows reality as closely as possible, the simulation canbe
parametrized with measurements from real hardware. In
thehardware-constructed experiment of the attribute isolated
archi-tecture, we used 12 STM32 microcontrollers with 12
addressnumbers of 0X0446 to 0X0451 as ECU nodes, and the
0X0449address number node was used as the GECU. We first
trans-planted the portable system contiki in the integrated
environmentof keil’s MDK5 and compiled the codes used in the
experiment.As shown in Fig. 9, we built an attribute isolated
architecture.The time parameters of the hardware were exported
through theserial port. The specifications of the tools used in the
hardwareexperiment and software simulation are shown in Table
III.
i) Extraction of the registration time parameters: We mea-sured
the registration time of 12 ECUs in the
attribute-isolatedarchitecture respectively, as shown in Table IV.
The averageregistration time for an ECU to perform one registration
isapproximately 0.36 ms. The average time that the GECU verifiesone
ECU is approximately 0.54 ms.
ii) Extraction of the encryption and decryption time
parame-ters: The average execution time for encryption, attribute
privatekey generation and decryption were measured by
implementing
Fig. 9. Construction environment of the attribute-isolated
architecture.
TABLE IIITOOLS USED FOR THE SIMULATION
TABLE IVREGISTRATION TIME EXTRACTED FROM THE HARDWARE
the CP-ABE algorithms on 11 ECUs. We measured the averagekey
generation time, the average encryption time, successfuldecryption
time and failed decryption time from 1 byte to 100bytes, as shown
in Fig. 10. The average time for an ECU togenerate the attribute
private key is approximately 3 ms. Theaverage encryption time on
hardware is 4.8 ms. The average timefor an ECU to fail to achieve
decryption is 1.6 ms. The averagetime for an ECU to successfully
achieve decryption is 7.5 ms.
2) IVNS Simulation: Then, to verify the performance of
theproposed attribute isolated communication among the ECUs,the
time parameters extracted from the hardware experimentare imported
into a python database. We built a 64-bit systemenvironment based
on Ubuntu under the PC, with 1.6 GHz CPU
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HAN et al.: ATTRIBUTE-ISOLATED SECURE COMMUNICATION ARCHITECTURE
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Fig. 10. CP-ABE performance for hardware implemention.
Fig. 11. Computation time.
and 8 GB RAM. Then, we built a simulator environment basedon
In-Vehicle Network Simulator (IVNS) which is developed byArtur
Mrowca et al. [29]. To export the communication perfor-mance
results, we created a monitor tag in the communicationlayer of the
IVNS. The monitor tag outputs the simulation resultsinto CSV files.
The analysis of the simulation results is shownin the next
subsection.
B. Evaluation and Comparison
In this subsection, we compare the proposed architecture withthe
architecture PSAC suggested in [18] and the architectureDSCA in
[19] in terms of the computation time, averagestorage consumption,
and bus load. For the convenience of thedescription, our proposed
architecture is denoted as Ours.
1) Computation Time: We measure the computation timefrom the
time that the ECUs process a message and transmitsthe encrypted
messages in our scheme. The simulation result isshown in Fig. 11,
if ECUs are added to the system for all of thearchitectures, the
measured computation time needed to executethe simulation increases
with an increasing number of ECUs. Inaddition, the DSCA performs
the slowest, while the computationtime for the PSAC with 100
messages is nearly equal to that ofour architecture with 200
messages. Furthermore, the slope ofthe curves is higher when ECUs
send more messages. Hence,for the DSCA, the computation time for 40
ECUs and 200messages is 849.78 ms and for 100 ECUs, the computation
timeis 4736.87 ms. For PSAC it is 513.47 ms and 1403.77 ms,
whilefor our architecture, it is 408.77 ms and 1174.88 ms. Hence,
thecomputation time of the proposed attribute-isolated
architectureis less than that of the PSAC and DSCA.
Fig. 12. Average memory storage.
Fig. 13. Bus load comparisons.
2) Average Storage Consumption: The average memory us-age
behaves differently for the three architectures, as shown inFig.
12. While for our architecture and PSAC, the average mem-ory usage
is nearly equal when the ECUs are added to the system,for the DSCA,
the average memory usage increases linearly.This is because the
DSCA needs to cache more authenticationand encryption messages. For
the DSCA, the average memoryusage for 120 ECUs and 200 messages is
159.98 MB and for 200ECUs, the average memory usage is 261.73 MB.
For the PSAC,the average memory usage is 81.56 MB and 139.78 MB,
whilefor our architecture, the average memory usage is 78.69 MB
and125.78 MB. Additionally, the DSCA requires more events thanother
architectures per new ECU, as a message exchange is morecostly than
our architecture or the PSAC and more ECUs meanmore receivers.
Hence the DSCA curve increases rapidly. For thePSAC, each ECU
performs authentication and encryption anddecryption to cache
messages. The additional monitor informa-tion that results from
more ECUs thus requires more receiversin the DSCA. For our
attribute-isolated architecture, the curveslowly increases. The
number of receivers is less than the numberof ECUs in the vehicle
and only specified ECUs can cachein-vehicle data. From this
perspective, our architecture attainsreasonable memory usage in
comparison with the memory usageof the DSCA and PSAC.
3) Bus Load: The bus load rate is the sum of the bus
percent-ages occupied by all data frames, and is an important
indicatorfor measuring the communication performance of an
in-vehiclenetwork. We fixed the baud rate of the CAN-FD at 500
Kbpsand evaluated the bus load for different cycles and numberof
messages. Fig. 13 shows the bus load of our architecture
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554 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 5, NO. 4,
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compared to that of the PSAC and DSCA in terms of the numberof
messages. In the case of the DSCA, the bus load increaseby
approximately 50% due to the new message exchange andencryption.
The bus load of the PSAC is slightly higher thanthat of our
architecture since the PSAAC additionally has totransmit as many
MAC messages as receivers. When there are100 messages in the
vehicle, the periods are 10 ms, 20 ms and50 ms, the bus load of
DSCA is 34.39%, that of PSAC is 22.34%,while the bus load of our
architecture is 18.96%. Hence, the busload of DSCA and PSAC are
higher than that of our attributeisolated communication
architecture. The proposed architecturecan be well applied to the
in-vehicle real-time environment.
VII. CONCLUSION
In this paper, a secure and efficient attribute-isolated
au-tomotive architecture was proposed. First, an analysis of
thefunctional attributes of all of the in-vehicle ECUs in an
intelligentconnected environment and a division of the functional
attributesof the ECUs into five classifications were performed.
Second,based on the above-classified attributes, we demonstrated
asecure attribute-isolated communication architecture. The ECUshave
different access rights, allowing only the ECUs with thesame
functional attributes in the internal network of the vehicleto
communicate. Then, it was proven that the proposed architec-ture
could resist both forgery and eavesdropping attacks underthe random
oracle model. Finally, the secure attribute-isolatedcommunication
architecture was constructed in a hardware en-vironment and
evaluated with the IVNS. The evaluation resultsshowed that the
average memory usage with 120 ECUs and100 messages is below 40 MB
and the bus load can be reducedto 18.96% using the proposed
security architecture comparedwith existing architectures. Our
results confirm that the proposedarchitecture is suitable for an
application to in-vehicle real-timeenvironments.
In the future work, ICV will face driverless environments.These
driverless cars can use nodes to perform edge computingand collect
information for decision-making [30]. We will usegateways as nodes
to enhance the collection capabilities of edgecomputing and ensure
the efficiency and real-time performanceof the automotive system.
Therefore, securing automotive archi-tecture based on edge
computing will be the focus of our futureresearch.
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HAN et al.: ATTRIBUTE-ISOLATED SECURE COMMUNICATION ARCHITECTURE
FOR INTELLIGENT CONNECTED VEHICLES 555
Mu Han (Member, IEEE) was born in 1980. Shereceived the Ph.D.
degree from the School of Com-puter Science and Technology, Nanjing
Universityof Science and Technology, China, in 2011. She
iscurrently an Associate Professor with the School ofComputer
Science and Communication Engineering,Jiangsu University. Her
research interests includecryptography, security and communication
in vehiclenetwork, the design of security protocols for smartcar
and Information Security, etc.
Ailan Wan was born in Jiangsu Province, China.She received the
B.S. degree from Jiangsu University,Zhenjiang, in 2016. She is
currently working towardthe M.S. degree with the Department of
ComputerScience and Communication Engineering, JiangsuUniversity.
Her research interests include informationsecurity, cryptography,
security of Electronic ControlUnit in vehicular network, controller
area networksecurity.
Fengwei Zhang received the Ph.D. degree in com-puter science
from George Mason University. He isan Associate Professor with
Department of ComputerScience and Engineering, Southern University
of Sci-ence and Technology. He was an Assistant Professorand the
Director of the COMPASS Lab with Depart-ment of Computer Science,
Wayne State University.His primary research interests include in
the areas ofsystems security, with a focus on trustworthy
execu-tion, hardware-supported security, transparent mal-ware
analysis, and plausible deniability encryption.
Shidian Ma received the master’s degree from theSchool of
mechanical and automotive engineering,Hefei University of
Technology, China, in 2005. Heis currently an Associate Professor
with School ofAutomotive Engineering Research Institute,
JiangsuUniversity. His research interests include automo-tive
electronic control technology, road traffic activesafety prevention
and control, security and commu-nication of electronic control
system.
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