Exploiting Vehicles’ Reputation to Mitigate DoS Attack
Gianpiero Costantino, Fabio Martinelli and Ilaria Matteucci
Istituto di Informatica e Telematica, CNR, Via G. Moruzzi, 1, Pisa, Italy
Security and Safety in Automotive, Connected Vehicles, Reputation, DoS Attack.
Recently the convergence of safety and security needs in automotive systems is one of the main challenges
of the research community. However, the different nature of safety and security metrics suggests that no
individual assessment technique is sufficient, in isolation, to validate large systems that are intended to be
both safe and secure. The introduction of new generation ICT systems into vehicles makes them potentially
vulnerable to security attacks that may impact on the safety of passengers, pedestrians, and vehicle itself.
Hence, entities involved in a communication have to be evaluated trustable by means of specific mechanisms
of the vehicle or infrastructure system. This work aims at proposing an algorithm for the calculation of
reputation of vehicles in a Vehicular Ad Hoc Network (VANET) based on the type and number of exchanged
messages. The ultimate goal is to mitigate the Denial of Service (DoS) attack in such kind of communication
by acting as a firewall with respect to not trustable vehicles. Indeed, the DoS is a security attack that affects
the availability of network bandwidth. This may have an impact on safety of drivers and vehicles since it may
prevent the communication and spread of important information for, e.g., human life.
Nowadays, the more and more pervasive usage of
ICT in automotive ecosystem is raising the need to
consider security issues related to cars, trucks, and
motorbikes. In the last 5 years, both car manufac-
turers and academics have performed studies in the
field of automotive cyber-security to identify a de-
tailed list of threats that can be exploited by attackers.
This research has provided a set of recommendations
to car manufacturers to overcome the found threats.
Based on inputs from the Automotive Cyber Security
Thought Leadership event (held in November 2014),
the Institution of Engineering and Technology (IET)
has been recently published a report (IET, The Insti-
tution of Engineering and Technology, 2014) on auto-
motive cyber-security issues. IET provides some rec-
ommendations to address such issues to ensure that
future connected vehicles be safe and more efficient.
New technologies make vehicles connected and
users able to interact with own vehicles through mo-
bile devices. These new opportunities represent an
advantage in terms of connectivity, revenue, and
safety but they have also some drawbacks in terms
of security. The main issues are mostly related to
security CIA triad, i.e., Confidentiality, Integrity, and
Availability. In automotive systems, Confidentiality
is intended related to exchange of information be-
tween the user mobile device and the vehicle. The
exchanged data may be confidential and must not be
stolen by an untrusted party. Integrity means that
shared data or information are not modified or altered
by another entity different from the source. In auto-
motive, this can be violated through applications able
to interact with the vehicle system. The same for the
information (requests, data, etc.) exchanged between
the mobile device and the connectivity system that
might be misinterpreted and cause failure in control
units. Thus, a hacker may use these vulnerabilities to
get internal data of the vehicle or try to acquire control
of parts of the car. Availability is a guarantee of reli-
able access to the information by authorized people.
In automotive systems, it is mostly related to com-
munication services that have to guarantee the correct
flow of information, such as traffic information, safe
alerts, and so on, among circulating vehicles.
In this paper we focus on vehicle-to-infrastructure
(V2X for short) and on vehicle-to-vehicle (V2V for
short) communications. Both of them are subjects
to several security attacks, and among others, one of
the most common is the attack on network availability
also referred as Denial of Service (DoS) attack (Raz-
zaque et al., 2013). The DoS attack is an attempt to
make a communication network resource unavailable
to its intended users, such as to temporarily or indef-
initely interrupt or suspend services of vehicles con-
Costantino, G., Martinelli, F. and Matteucci, I.
Exploiting Vehicles’ Reputation to Mitigate DoS Attack.
DOI: 10.5220/0005844500750082
In Proceedings of the International Workshop on domAin specific Model-based AppRoaches to vErificaTion and validaTiOn (AMARETTO 2016), pages 75-82
ISBN: 978-989-758-166-3
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
nected with other vehicles or with the infrastructure.
Moreover, DoS is a very common attack where the
attacker can overpower a vehicles resources or jam
the communication channel used by the Vehicular Ad
Hoc Networks (VANET) to bring down the VANET
itself or even cause an accident with a damage of
safety drivers.
We consider that both V2X and V2V communi-
cations mainly happen through a VANET, that is a
specific type of Mobile Ad Hoc Network (MANET)
providing communications between nearby vehicles
and roadside equipment. A MANET is a structure
composed only by wireless nodes, without any fixed
devices, that is set up instantly, as soon as devices,
which do not even know each other, are available. A
MANET survives until the participants remain linked
together. Similarly, vehicles of a VANET are consid-
ered communication nodes able to belong to a self-
organized network without any prior knowledge of
the presence or identity of the others.
Starting from these considerations, we propose an
algorithm to calculate the reputation of vehicles while
they are circulating on road. Our algorithm is based
on the direct observation of the messages generated
and forwarded by vehicles. These pieces of informa-
tion are collected and sent to a central entity that is
able to establish the reputation of each vehicle. So,
established reputation values are sent back to the ve-
hicles to notify them about their current behaviours.
This approach helps vehicles to detect the occurrence
of an anomalous behaviour with respect to a possible
DoS attack, and intrinsically the presence of a ma-
licious entity in the network. Finally, our algorithm
tries to validate the communications by providing a
way for isolating the attacker.
The paper is organized as follows: Section 2 pro-
vides some background notions about Vehicle Ad
Hoc Network. Section 3 describes the reference sce-
nario we use in the paper to exemplify the proposed
approach. Section 4 introduces the technique used
to calculate the reputation of vehicles, to verify the
behaviour of a vehicle and, eventually, to isolate it
whether considered as malicious. Section 5 discusses
the related work on security issues in automotive
systems with a particular eye to security aspects of
VANET. Finally, Section 6 draws the conclusion and
proposes some possible for future work.
A modern vehicle can be considered as a network
of sensors/actuators on wheels. VANET is a special
kind of Mobile Ad-hoc Network (MANET) where
vehicles equipped with the technologies are the key
constituents. According to (Razzaque et al., 2013) a
VANET differs from a MANET in several aspects: i)
Large scale, potentially billion of vehicles, ii) Fleet-
ing contact with other vehicles, iii) Nodes not as con-
strained in terms of energy, storage, and computation,
iv) Higher mobility, and v) Privacy requirements.
One of the main objective of a VANET is to al-
low communication between vehicles on the roads
and their environments with the aim of improving the
safety of drivers. To this aim, each vehicle belong-
ing to a VANET needs to have an OBU (On-Board
Unit), which is a communication device mounted on
vehicles, and a Wireless Sensor Network (WSN) sup-
ported roadside unit (RSU).
By using OBUs, vehicles can be connected with
other vehicles as well as with the roadside unit. The
RSU is also connected with the backbone network,
e.g., the roadside infrastructure, in such a way that
it can communicate with many other network appli-
cations and services, including the Internet access,
which can be provided to the vehicles (Qian and
Moayeri, 2008).
To improve the functionalities and capabilities
of VANET, multiple ad-hoc networking technologies
have been integrated on them, such as WiFi IEEE
802.11p, WAVE IEEE 1609, WiMAX IEEE 802.16,
Bluetooth, IRA, and ZigBee. These technologies
make more easy, accurate, effective, and simple all
communication between vehicles. Furthermore, the
IEEE 1609 (P1609.2) explicitly defines security as-
pects such as secure message formatting, processing,
and message exchange to put in evidence their impact
on vehicle safety aspects.
VANETs are expected to implement a variety of
wireless technologies such as Dedicated Short Range
Communications (DSRC). Other candidate wireless
technologies are Cellular, Satellite, and WiMAX.
Thus, VANETs are envisioned as the most important
entity of the Intelligent Transportation Systems (ITS).
The main security issues of V2X communica-
tion mainly concerns security issues peculiar of WiFi
connection. Indeed, in (Chou et al., 2009) the au-
thors state that, even though V2V and V2X are
both wireless mobile networks, they are different and
participate in vehicular network with different com-
munication modalities. For that reason, they pro-
pose WiMAX instead of WiFi for V2X infrastruc-
ture. They focus on a static setting in urban environ-
ment and describe some measurements by showing
that WiMAX technology provides a longer communi-
cation range and, for applications like file download-
ing, one might want to use a larger frame size to have
AMARETTO 2016 - International Workshop on domAin specific Model-based AppRoaches to vErificaTion and validaTiOn
a better throughput. The setting of frame size has a
strong impact on performance of WiMAX.
Hereafter we focus on the DoS security attack
within V2V communications, how it can impact on
safety aspects, and we provide a possible solution for
mitigating the attack. As we will see in the next sec-
tion, our approach works at application level so it is
independent from the used physical technology.
As reference scenario, we consider a vehicular net-
work in which all vehicles are connected with the
roadside infrastructure exploiting the WiMAX tech-
nology. Vehicles communicate with each other by
exploiting the OBU technology. Like any other net-
works, also these communications are affected upon
the Denial of Service (DoS) attack. The DoS is an ac-
tive and malicious attack in nature. For instance, if a
malicious adversary wants to create a massive pileup
on the highway, he could exploit an accident and then
use a DoS attack to prevent the dissemination of warn-
ings to other drivers.
The reference scenario is the one graphically de-
picted in Figure 1. Let us consider two vehicles both
circulating on a sector of a roadway. Let us also as-
sume the presence of a traffic jam some miles ahead.
Vehicles belonging to the VANET of that sector of the
roadway start to communicate each other to spread the
information as much as possible. Receiving this kind
of information on time increases the safety of drivers.
Indeed, according to (Wang and Thompson, 1997),
about 60% roadway collisions could be avoided if the
driver was provided warning at least one-half second
prior to a collision.
3.1 Modelling the Vehicles’ Behaviour
Vehicles that travel in a distributed environment can
assume several behaviours that may depend on differ-
ent reasons. A vehicle may decide to be fully collab-
orative and to constantly participate in the V2V com-
munications. On the other side, a vehicle can decide
to assume a dishonest behaviour and to deny V2V
communications by dropping messages it receives,
Black hole attack (Deng et al., 2002).
According to the BUG threat model (Bella et al.,
2005), an attacker can assume three different states of
behaviour that we recast when the goal is reputation:
Bad: when the vehicle is essentially selfish, and
damages the network intentionally by neglecting
or limiting particular communications;
Ugly: when the vehicle essentially assumes an op-
portunist behaviour according to its cost/benefit
analysis of the context.
Good: when the vehicle uses its resources to pro-
vide V2V communications nodes without any di-
rect interest.
In this section we present our technique to calculate
the reputation of vehicles, and to isolate those one that
are suspected to be attackers. We consider the reputa-
tion as main information to understand the behaviour
of vehicles, and more specifically, it is used as param-
eter to identify malicious vehicles. Indeed, reputation
is calculate in accordance to a principle of collabora-
tion: vehicles in a VANET work in an open net and
their collaboration is the sole mean to allow commu-
nications and the survival of the VANET itself (Bella
et al., 2008).
In the algorithm we propose, vehicles are able to
observe the behaviour of other vehicles that they meet
during the journey. The algorithm is composed of
four steps: as a first step, each vehicle performs a di-
rect observation of neighbours and evaluates their be-
haviour by comparing the number of messages they
generate and forward. Indeed, a trustworthy vehicles
has a collaborative behaviour and forwards messages
as much as possible to allow the information is cir-
culated and distributed uniformly over the network.
Then (second step), at fixed time, each vehicle trans-
mits the collected values to a central server belonging
to the roadside infrastructure by using V2X commu-
nications. In particular, a central server of the road-
side infrastructure acts as a collector of all local ob-
servations done by vehicles. As third step, the central
server calculates a single value of reputation for each
vehicle that is travelling in the considered roadway.
Finally, in the fourth step, the complete set of repu-
tation values is sent to all vehicles that will receive
updated reputation values of other vehicles they are
able to communicate with.
The main goal of the proposed algorithm is to pro-
vide a method of vehicles to rapidly identify and even-
tually isolate malicious vehicle. It is designed to de-
tect and overcome DoS attacks. Consequently, it al-
lows drivers to improve traffic safety and road effi-
ciency. Furthermore, by guaranteeing the availabil-
ity of communications network and road efficiency, it
leads to a reduction of pollution, thus positively im-
pacting on the environment.
Exploiting Vehicles’ Reputation to Mitigate DoS Attack
Figure 1: A graphical representation of the reference scenario.
4.1 Direct Observation
Figure 2: A graphical representation of V2V communica-
In Figure 2 we show an example of communications
among three vehicles. Vehicle A generates a message
and sends it to vehicle B. When, B receives the mes-
sage, it propagates the message to C and so on. Ba-
sically, according to this propagation mechanism, B
and C vehicles are able to locate the vehicle that gen-
erated the message by observing the packet structure.
In particular, B understands that A generated the mes-
sage, and C understands that the message was only
forwarded by B but not generated.
We call Direct Observation the phase in which a
vehicle calculates the number of packets generated
and forwarded by close vehicles. Vehicles are con-
sidered close when the distance between them is only
a single hop. So, when a vehicle receives a message, it
checks whether such a message has been generated or
forwarded by the close car. This information is stored
into a local table that we call Vehicles Local Observa-
tion (VLO).
Table 1: Example of Vehicles Local Observation Table.
B 19 5
G 5 16
... ... ...
P 12 40
An example of VLO calculated by C is illustrated
in the Table 1, where, V
says that the table belongs
to vehicle C, while G
and F
show the number of
Generated and Forwarded messages of met vehicles,
such us B, G, P, and others. Note that each vehicle
can be uniquely identified by the other, for instance,
through its license plate.
4.2 Collecting the VLO Tables
Once VLO tables are populated and updated, each
vehicle sends its VLO to the central server that be-
longs to the motorway infrastructure. Such commu-
nications exploit V2X connections. We assume that,
at fixed time, for instance every 5 minutes
, vehicles
send their VLO tables to the server, which collects
them. We point out that communications with the
server are not performed all at the same time, but they
depend on the moment when a vehicles entered in the
roadway sector under observation.
Figure 3: Sending VLOs to the central server.
In Figure 3 we pictorially show the phase in which
cars share their VLO with the server. When, the server
receives VLOs, it populates its table that contains all
and F
aggregated for each vehicle. We call this
table Vehicles Global Observation (VGO), and in Ta-
ble 2, we illustrate the VGO that contains the aggre-
gated values of G
and F
of each vehicle circulating
on the considered sector.
The strategy to populate the VGO table works in
the following way. If the server receives a VLO that
contains a new vehicle identity, i.e., information about
a vehicle that is not already present in the VGO, the
server just creates a new entry in the table and ap-
pends its values of G
and F
. On the contrary, if
the server has already values of G
and F
for certain
Note that this is an approximate value that should be
fixed only after performing accurate simulations.
AMARETTO 2016 - International Workshop on domAin specific Model-based AppRoaches to vErificaTion and validaTiOn
Table 2: Example of Vehicles Global Observation Table.
B 1509 150
G 501 1654
... ... ...
P 350 467
vehicles contained in the VLO, then it updates those
values in the following way:
= G
+ G
where G
represents the number of messages
generated stored in the VGO for the vehicle j, G
represents the number of messages generated stored
into the VLO for the vehicle j, and G
is the new
value for the number of messages generated by j.
The same approach is also applied to calculate the
aggregated values of messages for vehicle j to store
in the VGO. So, the formula is the following:
= F
+ F
where F
is the value of forwarded messages
stored in the VGO, F
is the number of forwarded
messages calculated locally, and F
represents the
aggregation of the previous two values.
4.3 Vehicles’ Reputation
As we presented above, the VGO contains an overall
observation of the behaviour of each vehicle travel-
ling in the roadway. Such a behaviour expresses the
reputation taken by the vehicle up to the moment it
has been established. According to the principle of
collaboration, the reputation of each vehicle is calcu-
lated as the ratio of F
and F
+ G
. The formula that
calculates the reputation of a generic vehicle j is:
+ G
Let F
and G
the aggregated values of forwarded
and generated messages of the vehicle j stored in the
VGO. It is worth noting that the result of Equation 3
is normalized to obtain value in the interval between 0
and 1. Thus, the structure of the VGO table is updated
as we show in Table 3:
The VGO table with the reputation value of each
car basically shows the behaviour taken by cars in the
motorway. More specifically, we say that a Rep
0.3 is an indication of an anomalous and bad be-
haviour, for instance a DoS attack. On the contrary,
Table 3: Vehicles Global Observation Table with Reputa-
tion Values.
B 1509 150 0.09
G 501 1654 0.77
... ... ... ...
P 350 467 0.57
0.7 indicates a collaborative and also good be-
Note that, even thought the reputation is calcu-
lated considering both newer and older message, our
reputation algorithm does not imply that a malicious
vehicle will be always tagged as malicious. In fact,
by reverting to a collaborative behaviour, that vehicle
will improve its reputation.
Remark. The proposed calculation is intended to
not be optimal in general. Our aim is to mitigate
the DoS attack by monitoring the number of mes-
sages that are input on the considered network. The
proposed algorithm can be refined to better estimate
the reputation of a vehicle. So, it may exploit ad-
ditional parameters that may help to mitigate secu-
rity issues related to data-integrity and privacy of ex-
changed messages.
4.4 Broadcating the VGO Table
In this last step, the central server is in charge of noti-
fying the vehicles with the reputation values available
in the VGO. Here, vehicles receive the VGO and use
the reputation values as indication of the behaviour of
the cars.
Figure 4: Isolation strategy against DoS attack.
In Figure 4 we show an attempt of DoS attack per-
formed by vehicle B. This car continuously generates
messages that may flood the V2V communications.
Nevertheless, vehicles E and F already know the be-
haviour of B because they checked the VGO table.
So, when E and F receive messages from B, they may
decide to isolate B by dropping the messages gener-
ated by it. This approach is close to that one applied
Exploiting Vehicles’ Reputation to Mitigate DoS Attack
by firewalls when they detect a DoS or bruteforce at-
tack. In the same fashion, here vehicles may decide
to isolate the “spammer vehicle”.
However, it may happen that the vehicle, which
is considered an attacker, may also generate an alarm
message that should not be dropped by its neighbours.
In this situation, we foresee that vehicles close to the
attacker do not directly drop the packet, but before
they check the content, for instance a bit indicating
the particular type of message, i.e., alarm. However,
this way to manage alarm situations may suggest the
attacker to flood the communications with alarm bit
set to 1. So, to avoid this scenario, we suggest to
accept only a maximum number of alarm messages
within a time-window, for instance 5msg/hour
Concerning security aspects in automotive design, the
authors of (Sagstetter et al., 2013) outline the upcom-
ing security challenges in automotive architecture de-
sign and discuss the application of a model-based de-
sign approach. This analysis was done in combina-
tion with formalized verification methods that aimed
at checking and avoiding vulnerabilities already dur-
ing the design process of a future automotive archi-
tectures based on Ethernet/IP.
The work in (Studnia et al., 2013) provides an
overview of the protection mechanisms that could be
adopted as countermeasures to identified threats.
Some solutions for automotive have been also
developed. For instance, European projects such
as SeVeCOM (Leinm
uller et al., 2006; Wieder-
sheim et al., 2009; Papadimitratos et al., 2008),
PRESERVE (https://www.preserve-project.eu/), and
EVITA (http://www.evita-project.org/) aimed at de-
signing secure communication architectures for inter-
nal or inter-vehicular communications. In particular,
the SeVeCOM EU project (Leinm
uller et al., 2006;
Wiedersheim et al., 2009) developed a modular sys-
tem supporting security and privacy features in V2V
Ad Hoc Networks. This solution describes an archi-
tecture for integrating into the network stack a sys-
tem for generating and verifying signatures based on
PKI with short term key and certificates in order to
guarantee the authenticity of communication partners.
It is important to note that the SeVeCom system has
been integrated into the network stack by inserting In-
ter Layer Proxies into the network stack to intercept
communication messages.
As we have already state above, also this value can be
further improved after performing simulations.
Many protection mechanisms and frameworks
have been developed to enforce security properties in
the VANET. For instance, there are generic security
mechanisms not customized for automotive but that
work for any mobile ad hoc network. Some solutions
for automotive have been also developed. Indeed,
the US Dept. of Transport identified several applica-
tion scenario where VANETs can be useful (National
Highway Traffic Safety Administration and others,
2005). These applications can be categorized in safety
and non-safety. Basically, the non safety are related to
traffic, congestion and so on. This points out how se-
curing VANETs is important. For this reason in (Raz-
zaque et al., 2013), the authors provide a possible list
of all possible adversaries in any security system and
present also several security solutions able to cope
with different issues. It is worth noticing that the main
security issues of V2X communication mainly con-
cerns security issues peculiar of WiFi connection.
The authors of (Malip et al., 2014), propose a pro-
tocol for securing communication in VANETs which
exploits “certificate-less” signature. This protocol
uses a reputation server, which is responsible for the
distribution and management of identities and crypto-
graphic credentials of vehicles. The main purpose is
to guarantee the reliability of messages. The solution
we propose is also based on a reputation system but
with the aim to deal with issues related to the avail-
ability of the VANET and its services for drivers.
Further approaches listed in (Chen et al., 2011) do
not require authentication to guarantee the authentic-
ity of a broadcast announcement. These approaches
take into account the number of received messages
with the same warning; if this number is greater than
a threshold, the announce is considered true. The se-
curity aspects they mainly consider are reliability of
messages, privacy, and auditability. They do not care
about availability of the VANET. Both solutions are
based on an algorithm that takes as input the num-
ber of exchanged messages but our algorithm, being
focused on DoS attack, considers also the source of
the messages as input information and calculates the
reputation of a vehicle.
In (Raya and Hubaux, 2005), the authors sur-
vey about security issues in VANET and sketches
some possible solutions for some of them. For in-
stance, in order to mitigate the DoS attacks, they
propose the capability of switching between different
channels or even communication technologies (e.g.,
DSRC, UTRA-TDD, or even Bluetooth for very short
ranges), if they are available, when one of them is
brought down. However, this could be not feasible
in case vehicles have not all the necessary technolo-
gies. The approach we propose overcomes this issue
AMARETTO 2016 - International Workshop on domAin specific Model-based AppRoaches to vErificaTion and validaTiOn
by identifying the possible attacker and dropping his
messages. In this way, the algorithm works as a fire-
wall by isolating the possible malicious vehicle.
The authors of (Leinm
uller et al., 2010) present
a solution to protect VANET communications from
roadside attackers. In this scenario, the attacker is
someone located next to a road and it is a vehicle that
generates messages that are not distinguishable from
the other cars. In this way, the attacker is able to send
fake messages on the net to influence the behaviour of
drivers on the road. Even though the problem we face
is similar, we focus on internal attackers, i.e., vehicles
that send too much messages to reduce the availability
of the VANET. Furthermore, the approach they pro-
posed in based on the distance of the attacker while
we propose an algorithm based on reputation of a ve-
Being more general, literature about reputation
system in peer-to-peer network can be also consid-
ered. As an example, the work in (Stakhanova et al.,
2004) presents a fully decentralized approach to com-
pute the reputation of peers based on the traffic be-
tween a node and its peers, independently of these
peers willingness to cooperate in calculation of their
reputation. A part form the different network commu-
nication, the main difference between this work and
the one we propose is the final goal: in (Stakhanova
et al., 2004) they want to find the optimal peers for the
communication, while we want to identify the possi-
ble attacker and prevent it to badly affect the commu-
nication among all the other nodes in the VANET.
Automotive systems present many security challenges
that depend on several factors such as, the heterogene-
ity of embedded systems and communication tech-
nologies to make vehicle connected. In this paper
we focus on Vehicle to Vehicle communication and,
in particular, we provide a reputation based algorithm
able to evaluate whether a vehicle behaves as an at-
tacker. As a result we are able to verify if a DoS attack
happens and mitigate it to preserve the availability of
vehicle communications for safety communication.
As ongoing work, we are simulating the perfor-
mance of the proposed algorithm to evaluate its effi-
ciency and feasibility in the verification and valida-
tion of both V2V and V2X communications. In the
future, we aim at enhancing our proposal to overcome
other security issues, such as data integrity and con-
fidentiality. We will refine our algorithm to prevent
malicious vehicles, for instance, from sharing wrong
identity information and creating messages that look
like forwarded messages but are actually newly cre-
ated ones. Furthermore, we would like to consider
the possibility that a malicious vehicle tries to attack
the infrastructure by, for instance, uploading fake ob-
servation tables to the server to arbitrarily change the
reputation scores The ultimate goal will be the deeply
analysis of the impact of the proposed solution on
safety aspects, such as, how much we are able to im-
prove the traffic on a road and to reduce traffic jam.
Work partially supported by the H2020-MSCA-ITN-
2015-NeCS 675320.
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