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The emerging Internet-of-Things (IoT) are vulnerable to Sybil attacks where attackers can manipulate fake identities or abuse pseudoidentities to compromise the effectiveness of the IoT and even disseminate spam. In this paper, we survey Sybil attacks and defense schemes in IoT. Specifically, we first define three types Sybil attacks: SA-1, SA-2, and SA-3 according to the Sybil attacker's capabilities. We then present some Sybil defense schemes, including social graph-based Sybil detection (SGSD), behavior classification-based Sybil detection (BCSD), and mobile Sybil detection with the comprehensive comparisons. Finally, we discuss the challenging research issues and future directions for Sybil defense in IoT.
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372 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
Sybil Attacks and Their Defenses
in the Internet of Things
Kuan Zhang, Student Member, IEEE, Xiaohui Liang, Member, IEEE ,
Rongxing Lu, Member, IEEE, and Xuemin Shen, Fellow, IEEE
Abstract—The emerging Internet-of-Things (IoT) are vulnera-
ble to Sybil attacks where attackers can manipulate fake identities
or abuse pseudoidentities to compromise the effectiveness of the
IoT and even disseminate spam. In this paper, we survey Sybil
attacks and defense schemes in IoT. Specifically, we first define
three types Sybil attacks: SA-1, SA-2, and SA-3 according to the
Sybil attacker’s capabilities. We then present some Sybil defense
schemes, including social graph-based Sybil detection (SGSD),
behavior classification-based Sybil detection (BCSD), and mobile
Sybil detection with the comprehensive comparisons. Finally, we
discuss the challenging research issues and future directions for
Sybil defense in IoT.
Index Terms—Behavior classification, Internet of Things (IoT),
mobile social network, social network, Sybil attack.
I. INTRODUCTION
I
NTERNET-OF-THINGS (IoT), which can expand the
traditional Internet to a ubiquitous network connecting
objects in the physical world, starts an evolution to enhance the
interaction among people and the objects. With the embedded
sensors on objects, IoT can sense the information from the
environments, the objects and our body (via sensor network,
radio-frequency identification (RFID) technique, wearable de-
vices, etc.) [1]–[3]. With the emerging wireless communication
techniques, such as short-range wireless communications and
WiFi, IoT can enable users to share information with others
[4], [5] in social network and the Internet of connected
vehicles [6], [7]. Furthermore, by integrating the sensing,
communication, and computation capabilities [8], [9], IoT can
offer diverse intelligent services [10] to form smart home [11],
smart grid [12]–[14], smart community [15], and smart city
[16], [17], as shown in Fig. 1. Therefore, as the advancement
of IoT technology, these value-added applications flourish to
facilitate people to interact with objects, people, and the world,
and change the way we communicate with each other.
However, the emerging IoT is vulnerable to Sybil attacks
where attackers can manipulate fake identities [18]–[20] or
Manuscript received March 01, 2014; revised June 29, 2014; accepted July
11, 2014. Date of publication July 30, 2014; date of current version October
21, 2014. This work was supported by the Natural Science and Engineering
Research Council (NSERC) of Canada, under a research grant.
K. Zhang and X. Shen are with the Department of Electrical and Computer
Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada (e-mail:
k52zhang@bbcr.uwaterloo.ca; xshen@bbcr.uwaterloo.ca).
X. Liang is with the Department of Computer Science, Dartmouth College,
Hanover, NH 03755-3510 USA (e-mail: xiaohui.liang@dartmouth.edu).
R. Lu is with the School of Electrical and Electronics Engineering, Nanyang
Technological University, 639798 Singapore (e-mail: rxlu@ntu.edu.sg).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JIOT.2014.2344013
Fig. 1. Overview of IoT.
abuse pseudoidentities to compromise the effectiveness of the
systems. In the presence of Sybil attacks, the IoT systems may
generate wrong reports, and users might receive spam and lose
their privacy. From a recent report [ 21] in 2012, a substantial
number of user accounts are confirmed as fake or Sybil
accounts in online social networks (OSNs), totally 76 million
(7.2%) in Facebook, and 20 million fake accounts created in
Twitter per week. These Sybil accounts not only spread spam
and advertisements, but also disseminate malware and fishing
websites to others to steal other users’ private information. In
addition, in a distributed vehicular communication system [22]
and mobile social systems [ 23], Sybil attackers generate biased
options with “legible” accounts. Without an effective detection
mechanism, the collective results will be easily manipulated
by the attackers. Since most Sybil attackers behave similarly to
normal users, to find out whether an account is Sybil or not is
extremely difficult, which makes Sybil defense of paramount
importance in the IoT.
Recent research efforts [24], [25] have been focused on
studying Sybil attacks and how to detect and defend them.
SybilGuard [24], a social graph (network)-based Sybil de-
tection scheme, explores random walk to partition the whole
social graph into honest regions and Sybil one which contains
Sybil nodes within it. SybilGuard relies on the assumption
that Sybil nodes can only build a limited number of social
connections with the honest nodes. Alternatively, according
to different behaviors, such as clickstream, of normal and
Sybil users, a behavior classification-based Sybil detection
(BCSD) scheme is proposed in [26]. From the observation
2327-4662
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ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 373
Fig. 2. IoT domains: sensing domain, social domain, and mobile domain.
of the clickstream, Sybil attackers have some specific click
patterns and purposely repeat them. Thus, it is effective to
detect Sybil attackers via machine learning on the clickstream.
Besides these solutions for online Sybil attacks, the Sybil
defense also plays the crucial role in mobile networks. In
[27], the mobile Sybil detection is exploited based on mobile
user’s friend and foe list. Mobile users can detect Sybil
attackers with the profile matching when they are encountered.
Liang et al. [23] explore local mobile user’s contact history
and trustworthiness to resist Sybil attackers when they are
uploading review comments. Particularly, in a mobile network,
mobile users cannot effectively detect Sybil attackers without
sufficient knowledge. Therefore, more research efforts are
necessary for the development of both online and mobile Sybil
detection and defense schemes in IoT.
In this paper, we survey the Sybil attacks and the corre-
sponding defense schemes in IoT. Specifically, we first define
three types of Sybil attacks: SA-1, SA-2, and SA-3 to cover a
broad range of the existing Sybil attacks. SA-1 is considered
to have a limited number of connections with normal users
in the social graph, whereas SA-2 is considered to build
many such social connections. Therefore, SA-2 is difficult
to be distinguished by using social graph partition. SA-3 is
considered in mobile networks, where the social graph infor-
mation is not available, and cannot be easily detected. We then
present three types of Sybil defense schemes: 1) social graph-
based Sybil detection (SGSD); 2) behavior classification-based
Sybil defense; and 3) mobile Sybil defense (MSD). We also
discuss some challenging issues and potential solutions on
Sybil defense in IoT.
This paper is organized as follows. We introduce the IoT
applications and domains in Section II. Section III defines and
explains Sybil attacks in different categories. We then present
SGSD, BCSD, and MSD in Sections IV–VI, respectively.
Some future research directions are discussed in Section VII.
Finally, Section VIII concludes the paper.
II. I
OTDOMAINS AND APPLICATIONS
In this section, we present three domains of IoT according
to different IoT applications as follows.
A. Sensing Domain
One of the most value-added functionalities of IoT is to
sense the environments including the living environment [28]
and human body [29]–[31], enabling users to interact with the
physical world [32]. Thus, a large volume of embedded sensors
are deployed in the target area to monitor the environmental
conditions or human biologic information [33]. Collecting
these sensing data, the sink node (e.g., users or control center)
can analyze and dig out some inherent or latent information
as shown in Fig. 2. For example, smart meters are used to
measure the appliance usage or power condition of the building
or home area, and periodically send the power usage of
individual unit to the control center. According to the metering
data, the control center can effectively schedule the power
distribution to save the unnecessary energy consumption.
Wearable devices [34], [35] are taken by people to measure
the biological parameters, such as heart rate, blood pressure,
body temperature, oxygen saturation, blood volume index,
antiarrhythmic index, quality of sleep in the real-time pattern.
A sink node or controller (e.g., smartphones, control center)
collects all the sensing data and reports them for the system
decision and user’s control.
B. Social Domain
Different from sensing domain aiming at environmental
monitoring, social domain provides the IoT applications to
facilitate the social interaction among users [36]–[38]. Driven
by the similar interests shown in Fig. 2, users could form
virtual online community or society to exchange information
and share multimedia resources. Generally, users in social
domain have the Internet access and can interact with both
the online servers and other users. Users in social domain
can search the desirable content, catch the breaking news, and
share information or content with their social friends.
C. Mobile Domain
In mobile domain, users may not always have the Internet
access due to the constraints of the Internet coverage and
user mobility. However, users can take the advantage of their
mobility to interact with others in the physical proximity
and share their interests in a device-to-device pattern by
using short-range wireless communications, bluetooth, WiFi,
etc. [39], [40]. These features can also provide ubiquitous
IoT applications, such as mobile social network (MSN)
[41], vehicular ad hoc network (VANET), and delay tolerant
network.
374 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
Fig. 3. Three types of Sybil attacks: SA-1, SA-2, and SA-3.
III. SYBIL ATTACKS
Sybil attacks exist in the IoT to maliciously manipulate the
systems. In this section, we define three types of Sybil attacks.
At the beginning, we present the social graph model. Suppose
an undirect social graph denoted as G with n honest nodes H
and totally m edges. Sybil nodes are denoted as S. In the social
graph, we use node to represent user, identity, or account in
the real network. The edge between every pair of two nodes is
weighted by their social relationships. An attack edge AG is
the edge connecting an honest node and a Sybil one, as shown
in Fig. 3. Note that in some literatures [ 24] and [25], social
network refers to the undirect social graph G.
A. SA-1 Sybil Attacks
The SA-1 attackers usually build connections within the
Sybil community as shown in Fig. 3, i.e., Sybil nodes tightly
connect with other Sybil nodes. However, the SA-1’s capa-
bility of building social connections with honest nodes is
not strong. In other words, the number of social connections
between Sybil nodes and honest ones is limited, i.e., in Fig. 3,
the number of SA-1 attack edges is limited.
The SA-1 attackers usually exist in sensing domain and
social domain, i.e., OSN, voting [42], or mobile sensing
systems [43]. The main goal is to manipulate the overall option
or popularity. For example, in an online voting system, SA-1
can illegally forge a massive number of identities to act as
normal users and submit the votes with the biased options.
The final voting result might be manipulated by the SA-1
attackers, since a considerable portion of votes are from the
SA-1 attackers. Similarly, in mobile sensing system, SA-1
can forge the false sensing data and indirectly change the
aggregated data. Therefore, in some cases, the behaviors of
Sybil attackers are indistinguishable from the normal users.
B. SA-2 Sybil Attacks
SA-2 attackers usually exist in social domain. Unlike SA-1,
SA-2 is able to build the social connections not only among
Sybil identities but also with the normal users. In other
Fig. 4. Online social networking behaviors and transition probabilities of
Sybil attackers and normal users. (a) State transitions for a Sybil user.
(b) State transitions for a normal user.
words, the capability of SA-2 is strong to mimic the normal
user’s social structures from the perspective of social graph.
Therefore, the number of attack edges is large.
The goal of SA-2 is to disseminate spam, advertisements,
and malware; steal and violate user’s privacy; and maliciously
manipulate the reputation system. For example, in OSNs,
SA-2 can forge the profiles and friend list as normal users,
but purposely spread spam, advertisements, and malware.
In addition, SA-2 could generate plenty of positive review
comments in a service evaluation system to exaggerate the
advantages of service, or generate many negative comments
to underestimate services. Obviously, SA-2 would focus
on some specific behaviors and repeat them in the high
frequency. In Fig. 4, the behaviors of SA-2 and normal ones
can be modeled as a Markov chain [44].
C. SA-3 Sybil Attacks
There are SA-3 Sybil attackers in mobile networks (i.e.,
mobile domain). The primary goal of SA-3 is similar to that of
SA-2. However, the impact of SA-3 may be in a local area or
within a short period. Due to the dynamics of mobile networks,
mobile users cannot keep connections with others f or the long
time, or the connections are intermittent. Furthermore, the
centralized authority cannot exist in mobile networks at all
the time. Thus, unlike that in the online system, the social
relationships, global social structure, topology, and historical
behavior patterns in mobile networks are not easy to obtain for
ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 375
TABLE I
S
YBIL ATTACKS
Sybil defense toward SA-3. The mobility and lack of global
information result in difficulties in SA-3 defense compared
with the defense on SA-1 and SA-2. In Table I, we compare
different types of Sybil attacks.
IV. S
OCIAL GRAPH-BASED SYBIL DETECTION
In this section, we present the SGSD schemes. The goal of
SGSD is to enable the known honest node H to either label any
other node S as “Sybil” or “honest, or detect SA-1 according
to community detection. Consequently, there are basically two
types of SGSD: social network-based Sybil detection (SNSD)
and social community-based detection (SCSD), respectively.
A. Social Network-Based Sybil Defense
SNSD is a kind of Sybil defenses based on “social network,
which is a social structure linking social relationships among
nodes. Sociology theory [45] is a useful tool to investigate
the social relationships among users. In this section, the
term “social network” indicates the user’s social graph and
structure, which can reflect user’s social relationships and the
social trustworthiness [46], [47] among users. Leveraging the
“social network” structure, Yu et al. [24] propose a famous
SNSD scheme, SybilGuard, based on random walk [48], [49].
Before the explanation of the detailed SybilGuard, we give an
assumption as follows.
Assumption 1: Although the Sybil nodes can tightly connect
with other Sybil ones, the number of social connections among
Sybil nodes and honest ones is limited.
SybilGuard relies on Assumption 1, and each node detects
the Sybil one in a distributed manner. Specifically, a node with
degree R generates totally R random routes starting from itself
along its edges with a fixed length L. If a route reaches a
known honest node, it is verified by this known honest node.
Particularly, a Sybil node S may be accepted as a verified
one (i.e., the route from S to H is called verifier) if one of
the routes from S reaches the known honest node V . Then,
given a threshold T R, S could be accepted as an honest
node when more than T routes from S are verified. With
Assumption 1, the limited number of attack edges makes the
number of verifiers greater than T if T is properly selected.
For example, if there are totally X attack edges, the number
of Sybil groups is bounded by X.From[50],itisproved
that T (
n log n) could be sufficiently large for the
honest nodes passing the random walk detection. In addition,
security schemes are adopted to ensure the authenticity of
the nodes and routes. Every pair of directly connected two
nodes (i.e., one-hop neighbors) negotiates a shared key on the
edge. Message authentication code (MAC) is used for each
node to verify the other one. Furthermore, every generated
random route should be registered with an unforgeable token
(witness table) including all L nodes on the route so that
the attackers cannot deny the connections and forge the route
information.
The correctness of SybilGuard relies on the fast-mixing
property of the social graph. The mixing time t of a social
graph indicates how fast the ending point of a random walk
algorithm achieves the stationary distribution. Here, in a social
graph, if the ending point distribution is independent on the
starting point as L →∞, it is the stationary distribution [24].
If the mixing time is Θ(t), the graph is fast mixing. When a
random walk with the length of L (
n log n), there are
Θ(
n) samples that are independent on the starting point. The
probability that a Sybil node is accepted by the known honest
node [i.e., both the Sybil node and the honest one select the
same edge (e.g., attack edge) in the random route] follows the
Birthday Paradox [51]. This collision probability is
Prob(Collision)=1
1
1
m
m
.
(1)
Therefore, SybilGuard has a high probability to detect SA-1
according to random walk.
To enhance SybilGuard, Yu et al. [25] propose another
defense scheme, called SybilLimit, with the near-optimal guar-
antees. In SybilLimit, each node generates R (
m) ran-
dom routes with length L (log n). Using the random walk
algorithm [52], the Sybil or honest nodes can be determined,
which is similar to SybilGuard. Different from SybilGuard,
SybilLimit leverages the intersections on edges instead of
vertex (node), and performs short random routes with multiple
independent instances of random walk. SybilLimit accepts
O(log n) Sybil nodes per attack edge, while this number in
SybilGuard is O(
n log n) [25], [53]. Both SybilGuard and
SybilLimit are based on Assumption 1.
To understand the properties of s ocial structures, Alvisi
et al. [54] investigate several structural properties of social
graphs including popularity distribution [55], small world
property [45], clustering coefficient [56], and conductance
[57], and observe that the conductance which is related to
the mixing time of a random walk is more resilient in
376 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
Sybil defense compared with the other properties. Note that
popularity distribution among the nodes follows a power-
law or log-normal distribution. Small world property indicates
that the distance between any two nodes is small. Clustering
coefficient is a parameter that reflects the closeness of nodes
within a social network. The conductance C(S) reflects the
mixing time, which indicates the minimum length of a random
walk. C(S)=
S
out
S
in
, where S
out
denotes the number of edges
that are out from S and S
in
denotes the number of edges
within S. If the conductance is low, the mixing time is high.
In [54], it is proved that for the first three properties, the
number of edges that Sybil attackers need to generate to
launch Sybil attacks is 0 or 1, whereas this number for the
property of conductance is
C(S)m
log(C(S))
. Sybil attackers have to
consume more resources to compete with the conductance-
based Sybil detection schemes. Therefore, it validates the
effectiveness of SybilLimit [25], which utilizes conductance
to detect Sybil nodes. In addition, a concept of perfect attack
is introduced to explain an undetectable attack that draws
some honest nodes in the social network into Sybil region,
without impact on the whole social network. In other words,
when a Sybil node joins the social network and sets up many
connections with the honest nodes, it is not easy to detect
such an attacker as well. The attack edge is a metric to
evaluate the attacker’s capability to launch a perfect attack.
To resist the strong Sybil attacks, in [54], an SoK defense
scheme is proposed exploiting conductance to enable honest
users to build a white-list which contains a set of nodes ranked
associated with their trustworthiness. The SoK is more robust
compared with other SNSD schemes, such as SybilGuard and
SybilLimit.
Recently, there are many other research efforts on SNSD.
Cao et al. [58] propose SybilRank to help the centralized
OSN servers or operators to detect Sybil attacks through
ranking nodes according to their perceived likelihood of being
Sybils. SybilRank aims to reduce the computation overhead
and achieve the scalability of the Sybil detection i n a large
scale OSN. Danezis and Mittal [59] explore a probabilistic
model of honest node’s s ocial network and propose a Bayesian
inference approach to divide the whole social graph into Sybil
and honest regions. Another Sybil defense [60] adopts the
principle of privilege attenuation [61] for SNSD to prevent
malicious Sybil attackers adding or removing edges in the
social graph without employing social engineering, especially
for collusion attack. To further enhance SybilLimit, Tran
et al. [62] propose a Sybil detection scheme, Gatekeeper, to
achieve optimization for the case of O(1) attack edges and
guarantee only O(1) Sybil identities. A multisource ticket dis-
tribution algorithm facilitates Gatekeeper for node admission
control.
A state-of-the-art tendency for SNSD is to explore trustwor-
thiness to establish social graph and detect SA-1. SybilFence
[63] leverages users’ negative feedbacks on Sybil attackers
and adjusts the edge weight in the social graph. For example,
if a user u
i
receives negative comments from others, u
i
’s
edge weights are reduced correspondingly. With the directed
social graph, SA-1 can be better detected. SumUp [42], an
SNSD scheme for the vote aggregation problem in an online
content rating system, also relies on credit network [46],
[64] among nodes. SumUp leverages online user’s voting
history in order to restrict the attacker’s voting capability
if he continuously misbehaves. In SumUp, a trusted node
computes a set of max-flow paths on the trusted graph and
then aggregates the votes. It allows the votes from the trusted
users t o be effectively aggregated, whereas limits the votes
from untrusted users. Canal [65] is similar to SumUp. With
a credit payment mechanism in a large scale network, Canal
enhances the establishment of social graph and is compatible
to the existing SNSD. Delaviz et al. [66] propose a trust and
credit-based Sybil detection scheme, SybilRes, which adopts
a local subjective weighted directed graph to indicate user’s
data transfer activities. When a user u
i
uploads data, the edge
weight on the path from u
i
to the downloader is reduced. To
maintain the edge weight of honest users, after downloading,
the downloader increases the weights of the edges on the paths
from the uploader u
i
to itself. Then, Sybil users could be
detected by using the sophisticated SNSD. Mohaisen et al. [67]
also explore the trust to form t he social graph. They rely on the
observation that nodes trust themselves more than others, and
the trustworthiness on other nodes are not uniformly equal.
Then, they leverage differential trust in the social graph to
filter weak trust edges and model trustworthiness by biasing
random walks. Unlike the basic SNSD [24], [25], these trust-
based SNSD schemes [66], [67] leverage trustworthiness to
build a directed social graph rather than the original undirected
social graph for r andom walk Sybil detection. Since this
enhancement relies on a practical assumption that the honest
nodes would not provide high trust on the unknown (or Sybil)
nodes, the attack edges could be filtered to guarantee the
SNSD accuracy. In summary, the trustworthiness or credit can
enhance the establishment of the social graph and restrict Sybil
attackers to build connections with normal users so that the
detection accuracy can be improved.
B. Social Community-Based Sybil Detection
SCSD explores social community detection to facilitate
Sybil detection. The possibility of using social community
detection algorithms to detect SA-1 is validated in [68]. In
[68], Viswanath et al. first analyze the SNSD schemes and
summarizes them to a ranking problem. Since the SNSD
schemes usually partition Sybil nodes and honest ones into two
parts: Sybil region and honest one, these would be viewed as
a graph partitioning problem. For these SNSD schemes, each
unknown node is ranked according to its social connections
with the known trusted nodes. Then, different parameters (i.e.,
thresholds) are selected to divide the social graph into two
partitions. These parameters determine the boundary of the
partition, or “cutoff. The ranking of nodes is toward the
direction of reduced conductance. In other words, the nodes
tightly connected with the known trusted ones (e.g., lower
conductance) would score higher in the ranking. Furthermore,
the ranking algorithms significantly impact on the ranking
results and the Sybil partition. At the same time, another
problem comes out: if a node slightly connects with the
current known trusted nodes, it is more likely to be detected
ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 377
TABLE I I
C
OMPARISON ON SOCIAL GRAPH-BASED SYBIL DETECTION
as a Sybil node no matter how tightly it connects with
other unknown trusted nodes. In other words, when there are
multiple social communities in the graph, it is inefficient and
ineffective to detect Sybil nodes only through social network
partition. Therefore, leveraging community detection to detect
Sybil nodes becomes promising and could enhance the Sybil
detection accuracy.
SybilDefender [69] is a typical SCSD scheme, which relies
on performing a limited number of random walks for Sybil
identification and community detection. Sybil identification
can detect whether a node is Sybil or not, similar to the exist-
ing SNSD schemes. After the Sybil identification, a commu-
nity detection algorithm is adopted to detect the neighboring
Sybil nodes around the detected Sybil one. Furthermore, an
efficient combination of Sybil identification and community
detection facilitates SybilDefender to further reduce the com-
putation overhead. In addition, due to the observation that
a portion of OSN relationships among users are untrusted
[55], SybilDefender also includes a mechanism to limit the
number of attack edges. This attack edge limiting mechanism
enables users to rate their friend’s relationships as “Friend”
or “Stranger”. The attack edges could be further removed
since Sybil attackers are probably “Stranger” from the view of
normal users. Note that SybilShield relies on Assumption 1.
Cai and Jermaine [70] leverage the latent community model
and machine learning to detect Sybil attacks, enabling that
the tightly interconnected communities are connected more
closely than the one loosely connected. Even though some
certain communities are compromised by Sybil attackers, the
attack communities can also be detected via the transitivity
of the latent community model. By using multicommunity
social network structure, Shi et al. [71] propose SybilShield,
an agent-aided SCSD scheme. SybilShield also leverages trust
relationships among users to form the social graph. However,
due to the fact that two honest nodes belonging to the two
different social communities may not tightly connect with each
other, SybilShield exploits the agents and ensures the honest
nodes tightly connect with other honest ones. In [71], the first
random walk is adopted as SybilGuard. Then, some agents
of a verifier are selected to run a second round of random
walk, called agent walk, where the agents traverse all of the
verifier’s edges to confirm the suspect nodes. SybilShield relies
on Assumption 2.
Assumption 2: Sybil nodes cannot tightly connect with
honest nodes in the multiple honest communities since honest
nodes would not trust Sybil ones. Honest nodes can tightly
inter-connect with others in the honest community.
With a friend invitation graph built according to user’s
befriending interactions (invite or accept friends), VoteTrust
[72], a novel SCSD scheme, leverages a trust-based vote
assignment, and global vote aggregation to estimate the proba-
bility of a Sybil attacker. VoteTrust combines the social graph
structure and user’s feedback (accept or reject friend requests)
to establish a directed graph. It bases on an assumption that
the Sybil users cannot receive more than a certain number of
friend requests from normal users. The global aggregation of
the votes for every node can be used to estimate its global
rating. With this two-way (voting and feedback) mechanism
(e.g., in a directed graph), Sybil detection would be more
effective compared others schemes.
In Table II, we compare the SGSD schemes with respect to
preliminary techniques, assumption, decentralized properties,
etc. A tendency is to explore trustworthiness to facilitate the
Sybil detection to SA-1.
V. B
EHAVIOR CLASSIFICATION-BASED SYBIL DETECTION
In this section, we present BCSD. From the recent studies
[26] and [44], Sybil users in RenRen, a popular OSN i n China,
can generate an exponential number of social connections with
the normal (or honest) users. In [73], it shows that the Sybil
users rarely establish social connections with other Sybil users
in RenRen. Therefore, only relying on the SGSD schemes
cannot effectively detect Sybil attacks since Assumptions 1
and 2 may not always hold. Therefore, some novel Sybil
detection schemes are desirable and should exploit some
promising features of Sybil attacks.
Recently, Wang et al. [44] investigate the OSN user’s
browsing and clicking habits, and differentiate the Sybil users
by comparing their abnormal behaviors with the normal users.
According to the data obtained from RenRen, the primary
OSN activities of users are selected as follows.
1) Befriending: Send, accept, or reject friend requests.
2) Photo: Upload photos, tag friends in the pictures, browse
photos, and comment on the photos.
3) Profile: Browse profiles of other users.
4) Share: Share multimedia contents, including video,
photo, audio, contents, and website links.
5) Messaging: Update status, wall posts, send or receive
instant messages.
6) Blog: Post blogs, browse blogs, and comment on the
blogs.
According to the statistics, the primary activities of Sybil users
are friending (especially, sending friend requests), viewing
photos and profiles of others, and sharing contents with others.
On the contrary, the normal users spend a large portion of
online time to view photo, and perform other activities, such
as viewing profiles, sending messages, sharing contents with a
378 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
similar frequency. Both Sybils and normal users share content
or send messages at similar frequencies. Note t hat sharing
content or sending messages are the common approaches
for Sybils to disseminate spam in OSNs. This observation
indicates that the traditional spam detection schemes cannot
simply leverage numeric thresholds to resist spam.
From Fig. 4, the click transitions could be modeled by
Markov chain with each state as a click pattern. Normal
users usually perform diverse OSN behaviors, and the tran-
sitions among states are really complicated. By contrast,
the Sybil users are involved in some specific activities in
a high frequency. To distinguish the SA-2, support vector
machine (SVM) [74], [75] can be adopted according to the
session features, such as average clicks per session, average
session length, average inter-arrival time between two clicks,
and average sessions every day, and the click features. The
preliminary results show that the Sybil detection accuracy
is high. In [44], three models (click sequence model, time-
based model, hybrid model), which can cluster similar click
patterns, are built for the behavior classification. According
to some specific similarity metrics, the sequence similarity
graph can be established. Through graph clustering, the Sybil
users can be detected. The SVM-based scheme i s supervised
learning tool, which requires a long-term training period.
To address this issue, an unsupervised learning scheme is
proposed, where only a small portion of click patterns of given
normal users as “seeds. They color the normal clusters that
contain a seed sequence; otherwise, the uncolored clusters are
Sybil ones.
With crowdsourcing and social Turing tests, Wang et al. [76]
propose a distributed Sybil detection scheme, which signifi-
cantly improves the detection accuracy. For a Sybil attacker, he
cannot pass “social Turing test” with different attack strategies.
Furthermore, crowdsourcing provides an adaptive platform for
normal users (e.g., “turker”) to complete the Sybil profile
detection with a reasonable cost. From the experiments in
[76], the accuracy of crowdsourcing Sybil detection under
the reasonable burden is almost as high as that performed
by “experts. Some key factors (e.g., demographic factors,
temporal factors and survey fatigue, turker selection, and
profile difficulty) that may impact on the crowdsourcing Sybil
detection are provided. Obviously, the cost of a crowdsourcing
workforce is significantly low, which poses a new direction
for Sybil detection. In addition, some other BCSD schemes
[77] are proposed based on behavior classification. DSybil
[77] exploits the heavy-tail distribution of the classical voting
behavior from the honest users to detect Sybil identities. In
summary, these BCSD schemes can detect SA-2 according to
the user’s behavior learning and classification.
Strong Sybil attackers would penetrate into the social graph
and generate many social connections with the normal users,
which opposes the assumption of the SGSD. If Sybil attackers
are familiar with the normal user’s click patterns or habits, i.e.,
Sybil attackers could truly mimic the normal users, the BCSD
cannot effectively detect them as well. However, it is obvious
that Sybil attackers have to consume a large portion of time
to mimic the normal users so that the attack behaviors are
partially limited.
VI. M
OBILE SYBIL DEFENSE
In this section, we present Sybil defense schemes in mobile
networks. Without the global social graph for Sybil detection,
MSD aims to either detect SA-3 or restrict Sybil attacker’s
behaviors.
A. Friend Relationship-Based Sybil Detection (FRSD)
In a mobile network, due to the mobility and the lack of
global social graph information, Sybil defense is quite different
and difficult compared with that in the online networks.
Quercia and Hailes [27] propose an MSD scheme to match
mobile user’s communities and label the users from the Sybil
community as Sybil attackers. In [27], one assumption is that
each mobile maintains two lists: friend list containing the
trusted mobile users, and foe list with the untrusted users
in it. When two users are encountered in the network, they
match their communities. If a user is not in the trusted
communities, this user would be considered as a Sybil user.
In [78], Chang et al. also propose a Sybil defense scheme in
MSNs, assuming that the Sybil users and normal users exist in
different communities, and rely on the community matching
to detect the Sybil users. Therefore, leveraging friendship is
an effective solution to detect Sybil attackers. However, this
type of FR-MSD schemes requires mobile users to maintain
the trusted community information in advance.
B. Cryptography-Based Mobile Sybil Detection
Cryptography is another useful tool to facilitate Sybil
defense, especially for MSD, and can restrict Sybil at-
tacker’s malicious behaviors. In this section, we present some
cryptography-based MSD (crypto-MSD) schemes based on
cryptography techniques to defend SA-3.
VANET is one kind of the internet of vehicles, characterized
by the high-speed mobility. When Sybil attacks are launched in
VANET, an added challenge in detecting SA-3 is the mobility
that makes it increasingly difficult t o tie an attacker to the
location. To address Sybil attack issues in VANETs, Lin [22]
proposes an LSR scheme to resist local Sybil attackers and
mitigate zero-day Sybil vulnerability in sparse and privacy-
preserving VANET. The local vehicle users are not capable
to effectively detect Sybil attackers before they are revoked
by the TA. To this end, every user u
i
should sign on the
event that u
i
posts. Using group signature [79], if a user
signs on the same event for multiple times (e.g., more than
one), these signatures may be invalid. Then, the user can be
simply linked by other users and detected as Sybil attackers. In
[22], the Sybil report delay has been analyzed, while two-layer
and multilayer reportings are introduced to track the Sybil
attacker’s real identity and for the revocation at the TAs side.
Since the pseudonym techniques are widely applied in wireless
and mobile networks, there are two sides to the pseudonyms:
on one hand, the pseudonym can protect legitimate user’s
real identity from being identified and linked; on the other
hand, the use of pseudonymous identities may hinder the Sybil
detection since it is quite difficult to trace the Sybil identities
from pseudonyms. Similarly, in [80] and [81], a malicious
ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 379
user pretending to be multiple (other) vehicles can be detected
in a distributed manner through passive overhearing by a
set of fixed nodes called road-side boxes. The detection of
Sybil attacks in this manner does not require any vehicle in
the network to disclose its identity; hence, privacy can be
preserved at all times. Triki et al. [82] explore the embedded
RFID tags on the vehicles and the short lifetime certificates
from roadside units (RSUs) to verify user’s authenticity. Some
observers (e.g., RSUs or vehicles) are involved in monitoring
the sensitive events to mitigate the false negative detection.
Furthermore, vehicles change their identities when they switch
to the communication region of another RSU instead of the
current one, achieving the unlinkability and privacy.
The advantages of IoT techniques ensure the smart shopping
applications available when users are in a commercial street
or outlet mall. Users can either use smartphones to query
the special offers or features of the surrounding stores with
smart shopping, or search in OSNs, such as eBay, Facebook,
and Twitter, to browse the reviews or service evaluation
from previous customers. Alternatively, local service providers
(LSPs) can gather the users’ comments and post them to the
nearby users via MSNs. Since no trusted authority is available
to establish trustworthiness between LSPs and users, Sybil
attackers in the local area could forge some positive reviews,
delete or modify the negative ones. From the perspective of
users, they could also act as an attacker to post fake reviews
as well. Therefore, Sybil attacks may maliciously manipulate
the system and degrade the quality of smart shopping.
To resist these Sybil attacks, Liang et al. [23] study
trustorthy in service evaluation of MSNs, and propose a TSE
scheme to facilitate the service review submission and limit
the Sybil attackers’ capabilities. Specifically, in TSE, LSPs
generate plenty of tokens to synchronize mobile users’ review
submissions. A user u
i
can tie his reviews with signatures
to only one token after receiving a token from either LSPs
or other users having similar profiles or preferences with u
i
.
The similar profiles and preferences help users build their trust
relationships in a local area. Then, the tokens can be circulated
among users to enable cooperative review submissions from
users with similar profiles or preferences. The efficiency of
signature and verification can be achieved with aggregate au-
thentication techniques. The TSE also embeds the time stamp
into the review signature to prevent any user from modifying or
deleting the submitted reviews. In addition, every user adopts
pseudonym when submitting reviews. All pseudonyms for the
reviews in the same token are stored in a list for traceability on
a group Sybil attackers. If u
i
submits multiple reviews with
multiple pseudonyms, both LSP and other users can easily
verify it due to the group signature properties. Furthermore,
u
i
s real identity can be linked due to the revealed multiple
pseudonyms that u
i
uses. After publishing a token, the LSP
cannot omit this token once some reviews are negative to
the LSPs. In each token, the length of the review chain can
bound the LSP’s modification capability. For example, the LSP
has to be stronger to modify the existing review chain with
a longer review chain. With different token structures, such
as ring, chain and tree, it is difficult for SA-3 to modify
the posted reviews. I t is because the established structure
would be destroyed if any modification is made on this
structure. Besides the basic cryptography solutions, in [23], if
a user generates a massive number of reviews with the same
pseudonym in a short period, i.e., one time slot, other users
can easily detect his behavior. Therefore, Sybil attacks can be
effectively detected and the attacker’s behaviors are strictly
constrained.
C. Feature-Based Mobile Sybil Detection
Some specific features, such as channel characteristics [83],
[84] and mobility features, in mobile networks, could be
investigated to classify Sybil attackers and normal users. For
example, in a typical wireless network, channel features are
studied to effectively detect Sybil attackers [85]. An enhanced
physical-layer authentication is utilized, whereas the spatial
variability of radio channels is typical in indoor, and urban en-
vironments with rich scattering is exploited. The combination
of authentication and channel features detects Sybil attackers.
In practice, the proposed scheme is also feasible according to
the overhead of the sophisticated channel estimation schemes,
either independently or associated with other physical-layer
security schemes, like spoofing attack detection. In addition,
the received signal strength (RSS) is also used to detect Sybil
attackers in a static wireless network, such as wireless sensor
networks [86], [87]. If a node always receives the packets
with a similar RSS, the sender is probably a Sybil attacker.
Some other MSD schemes leverage mobile network features
to defend Sybil attacks. Geutte and Ducourthial [88] estimate
the amount of cheated nodes to measure the success rate of
Sybil attacks. They also evaluate the impact of transmission
power tuning from senders, whereas analyze the impact of
bidirectional antenna over omnidirectional antenna for the
receiver. Investigating the transmission signal difference, they
quantify the effects of different security assumptions on Sybil
attackers and the impact of antennas on the Sybil detection
accuracy. Yu et al. [89] also analyze the signal strength
distribution of vehicles, and adopt a statistical method to
cooperatively verify the location that a vehicle comes from.
Since the neighbors cooperatively measure the signal strength
of the specific vehicle, the location estimation accuracy can be
significantly improved. Abbas et al. [90] propose a lightweight
RSS-based Sybil detection scheme in mobile ad hoc network,
without centralized authority and dedicated hardware [e.g.,
directional antenna or global positioning system (GPS)]. This
lightweight detection scheme relies on the node mobility, and
sets the threshold to differentiate the node’s moving speed.
If any node moves much faster than the preset threshold, it
may be Sybil attackers. In summary, by investigating normal
user’s and Sybil attacker’s behaviors related to the mobility,
channel conditions, the SA-3 attackers can be differentiated.
The detection strategies would vary in different networks,
since the system features also significantly change.
Mobility is an important characteristic of mobile network,
and can be adopted to detect Sybil attackers in the mo-
bile environment. Piro et al. [91] observe that in mobile
ad hoc network, the Sybil identities related to a single Sybil
attacker are bound to a single physical node. In other words,
380 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
TABLE III
S
YBIL DETECTION:ACOMPARISON
a large number of Sybil identities move together. By mon-
itoring the user’s mobility, Sybil identities can be detected.
Mutaz et al. [92] leverage the features of platoon to detect
the Sybils in VANETs. Therefore, defending Sybil attackers
through the system features is a promising approach where the
challenge is how to obtain the sufficient knowledge or features.
Park et al. [93] investigate the mobility of vehicle and rely
on the fact that the two vehicles rarely pass multiple RSUs
always at the same time. Correlating the vehicles and RSUs
in the spatial and temporal domains, Sybil attackers can be
identified.
In addition, the secure hardware [94] i s used to validate
every user’s authenticity. Sybil attackers can only authenticate
themselves with the limited number of times, and the fake
identities cannot become legal. Although Sybil attacks can be
well resisted, the cost of this scheme is high. Therefore, it
would be used in the applications requiring the highest security
level. In [43], an identity fee-based Sybil defense is proposed,
relying on increasing the cost of identity maintenance. The
attackers have to spend more fees to launch a Sybil attack.
Zhang et al. [95] propose a resource testing scheme to detect
the overloaded users, which are probably Sybils. The resource
testing relies on the observation that the each user or attacker
would work on a single or limited number of devices. If a
Sybil attacker exists in the network, it might consume the
dramatic amount of resources, such as computation, commu-
nication, storage, and network bandwidths, to maintain the
created fake identities. Meanwhile, Li et al. [96] propose
an admission and retainment control mechanism to enforce
nodes to periodically solve computational puzzles. When these
dedicated resources can support each node, Sybil attackers
would not have adequate recourses t o launch the attack. There-
fore, the attacker’s capabilities are limited to some extent.
Reputation systems [97] could also be adopted to mitigate
Sybil attacks in mobile network [98], [99]. These Sybil de-
tection schemes provide some challenges, such as hardware,
device resource, and reputation, to limit the Sybil attacker’s
behaviors.
In Table III, we summarize the existing Sybil defense
schemes with respect to some design principles. Toward Sybil
attackers i n Section III, Sybil defense should leverage different
features to classify, detect, and resist Sybil attacks toward
different scenarios and networks.
TABLE I V
C
OMPARISON OF SYBIL DEFENSE IN MSNS AND OSNS
VII. RESEARCH CHALLENGES
In this section, we present some research challenges and
potential solutions on Sybil defense.
A. Sybil Defense in MSNs
Although some off-the-shelf Sybil defense schemes could be
applied in MSNs, they cannot effectively detect Sybil attackers
due to the lack of global social graph or historical behaviors
for detection schemes to learn. Furthermore, the traceability
on the detected Sybil attackers may not be guaranteed due to
the dynamic mobility of MSN users. Unlike OSNs, the social
structure in MSNs is hard to obtain due to the dynamically-
changing network topology and privacy concerns, as depicted
in Table IV. The existing MSD schemes [22], [23] can either
partially differentiate Sybil attackers and normal users, or
design some cryptographic schemes, i.e., group signature, to
constrain Sybil attacker’s behaviors. One possible solution is
to explore the trust relationships among mobile users and
build a tightly connected local social structure. Furthermore,
the contact and location information should be taken into
account. Since the mobile Sybil attacker’s behaviors would
be related to their attack purposes, e.g., purposely generating
malicious reviews or spam but inactively participating in other
social activities, the mobile Sybil attacker’s mobility would
be differentiated from that of normal users. Furthermore,
the contact and location information of mobile users can be
obtained in MSNs. Therefore, more research efforts should be
put on the analysis of contact and location properties of mobile
users, which would benefit the Sybil defense in MSNs.
B. Privacy and Sybil Defense
Since many Sybil defenses, e.g., BCSD and MSD, tend
to study the user’s behaviors, such as clickstream, browse
ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 381
history, and contact, it is critical to address the privacy leakage
during Sybil defense, especially in a mobile environment.
For example, when the contact information is used to detect
SA-3, user’s contact history might be disclosed to others,
including mobile users, Sybil attackers or LSPs. Although it is
helpful to the Sybil defense, the leakage of user’s information
still violates their privacy. With the proper cryptographic
encryption, i.e., homomorphic encryption, it is possible to
hide the real information in the ciphertext and enable addition
or multiplication operations on the ciphertexts. However, the
computation and communication overheads have to be dra-
matically increased, especially in a mobile environment where
energy consumption is also a crucial issue for mobile users.
Alternatively, it is possible to explore user’s common profiles
and preferences, which reduce the privacy leakage, for Sybil
defense. The challenging issue is how to guarantee the Sybil
defense accuracy while preserving privacy.
C. Cooperative Sybil Defense
Due to the lack of sufficient knowledge or the capability
of users, Sybil defense may be ineffective and inefficient in
some scenarios. For example, in a mobile network, mobile
user’s capability is not as powerful as that on the server
side, or even weak compared with online users. One possible
and promising approach is the cooperation among servers and
mobile users for Sybil defense. The mobile users can detect
the suspicious Sybil users in the early stage via cryptographic
schemes, such as authentication of identities or user contacts,
event signatures, and local community structure. The mobile
users then report them to the servers with the corresponding
contact or other information. The centralized servers would be
an assistance to process the complicated operations, such as
user behavior learning, social graph or community detection.
The servers could take the advantages of the computation and
storage capability and confirm the Sybil detection from mobile
users. In addition, the cooperation among mobile users can
also facilitate the Sybil defense. With the cooperation, more
knowledge about Sybil attackers can be obtained for further
detection. Therefore, the cooperative Sybil defense should be
a promising tendency.
VIII. C
ONCLUSION
In this paper, we have provided a survey of Sybil attacks
and their defense schemes in IoT. Specifically, we have de-
fined three types of Sybil attacks in the distributed IoT and
presented some Sybil defense schemes with the comparison.
The differential characteristics, including social structures and
behaviors, between Sybil attackers and normal users could
facilitate the Sybil defense. In addition, MSD can leverage
mobile network features, wireless channel characteristics, and
cryptography to resist Sybil attackers. We have also suggested
some open research issues such as Sybil defense in MSNs,
tradeoff between privacy and learning in Sybil defense, and
cooperative Sybil defense. We hope this survey will be useful
for further research and development in the IoT.
R
EFERENCES
[1] A. Sehgal, V. Perelman, S. Kuryla, and J. Schonwalder, “Management of
resource constrained devices in the Internet of Things, IEEE Commun.
Mag., vol. 50, no. 12, pp. 144–149, Dec. 2012.
[2] K. Ren, W. Lou, K. Zeng, and P. Moran, “On broadcast authentication
in wireless sensor networks, IEEE Trans. Wireless Commun.,vol.6,
no. 11, pp. 4136–4144, Nov. 2007.
[3] Y. Liu, K. Liu, and M. Li, “Passive diagnosis for wireless sensor
networks, IEEE/ACM Trans. Netw., vol. 18, no. 4, pp. 1132–1144,
Aug. 2010.
[4] C. Tang et al., “Comparative investigation on CSMA/CA-based oppor-
tunistic random access for Internet of Things, IEEE Internet Things J.,
vol. 21, no. 1, pp. 33–41, Apr. 2014.
[5] O. Bello and S. Zeadally, “Intelligent device-to-device communication
in the Internet of Things, IEEE Syst. J., to be published.
[6] L. Foschini, T. Taleb, A. Corradi, and D. Bottazzi, “M2M-based
metropolitan platform for IMS-enabled road traffic management in IoT,
IEEE Commun. Mag., vol. 49, no. 11, pp. 50–57, Nov. 2011.
[7] W. He, G. Yan, and L. Xu, “Developing vehicular data cloud services
in the IoT environment, IEEE Trans. Ind. Informat., vol. 10, no. 2,
pp. 1587–1595, May 2014.
[8] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context
aware computing for the Internet of Things: A survey, IEEE Commun.
Surveys Tuts., vol. 16, no. 1, pp. 414–454, May 2014.
[9] C. Lai et al., “CPAL: A conditional privacy-preserving authentication
with access linkability for roaming service, IEEE Internet Things J.,
vol. 1, no. 1, pp. 46–57, Feb. 2014.
[10] H. Celdran, G. Clemente, G. Perez, and M. Perez, “SeCo-
Man: A semantic-aware policy framework for developing privacy-
preserving and context-aware smart applications, IEEE Syst. J.,tobe
published.
[11] J. Huang, Y. Meng, X. Gong, Y. Liu, and Q. Duan, A novel deployment
scheme for green Internet of Things, IEEE Internet Things J.,vol.1,
no. 2, pp. 196–205, Apr. 2014.
[12] A. Aziz, Y. Sekercioglu, P. Fitzpatrick, and M. Ivanovich, A survey
on distributed topology control techniques for extending the lifetime
of battery powered wireless sensor networks, IEEE Commun. Surveys
Tuts., vol. 15, no. 1, pp. 121–144, 2013.
[13] K. Zhang, R. Lu, X. Liang, J. Qiao, and X. Shen, “PARK: A privacy-
preserving aggregation scheme with adaptive key management for
smart grid, in Proc. IEEE Int. Conf. Commun. China (ICCC), 2013,
pp. 236–241.
[14] M. Wen et al., “PaRQ: A privacy-preserving range query scheme over
encrypted metering data for smart grid, IEEE Trans. Emerg. Topics
Comput., vol. 1, no. 1, pp. 178–191, Jun. 2013.
[15] X. Li et al., “Smart community: An Internet of Things application,
IEEE Commun. Mag., vol. 49, no. 11, pp. 68–75, Nov. 2011.
[16] J. Jin, J. Gubbi, S. Marusic, and M. Palaniswami, An information
framework of creating a smart city through Internet of Things, IEEE
Internet Things J., vol. 1, no. 2, pp. 112–121, Apr. 2014.
[17] P. Vlacheas et al., “Enabling smart cities through a cognitive man-
agement framework for the Internet of Things, IEEE Commun. Mag.,
vol. 51, no. 6, pp. 102–111, Jun. 2013.
[18] Q. Lian et al., An empirical study of collusion behavior in the Maze
P2P file-sharing system, in Proc. IEEE Int. Conf. Distrib. Comput. Syst.
(ICDCS)
, 2007, pp. 56–66.
[19] M. Yang, Z. Zhang, X. Li, and Y. Dai, An empirical study of free-
riding behavior in the Maze P2P file-sharing system, in Proc. 4th Int.
Workshop Peer-To-Peer Syst. (IPTPS), 2005, pp. 182–192.
[20] K. Zhang, X. Liang, R. Lu, and X. Shen, “Exploiting multimedia ser-
vices in mobile social network from security and privacy perspectives,
IEEE Commun. Mag., vol. 52, no. 3, pp. 58–65, Mar. 2014.
[21] Businessinsider. (2013, Mar.). [Online]. Available: http://www.
businessinsider.com/facebook-targets-76-million-fake-users-in-war-on-
bogus-accounts-2013-2
[22] X. Lin, “LSR: Mitigating zero-day Sybil vulnerability in privacy-
preserving vehicular peer-to-peer networks, IEEE J. Sel. Areas Com-
mun., vol. 31, no. 9, pp. 237–246, Sep. 2013.
[23] X. Liang, X. Lin, and X. Shen, “Enabling trustworthy service evalua-
tion in service-oriented mobile social networks, IEEE Trans. Parallel
Distrib. Syst., vol. 25, no. 2, pp. 310–320, Feb. 2014.
[24] H. Yu, M. Kaminsky, P. Gibbons, and A. Flaxman, “SybilGuard:
Defending against Sybil attacks via social networks, IEEE ACM Trans.
Netw., vol. 16, no. 3, pp. 576–589, Jun. 2008.
[25] H. Yu, P. Gibbons, M. Kaminsky, and F. Xiao, “SybilLimit: A near-
optimal social network defense against Sybil attacks, IEEE/ACM Trans.
Netw., vol. 18, no. 3, pp. 885–898, Jun. 2010.
382 IEEE INTERNET OF THINGS JOURNAL, VOL. 1, NO. 5, OCTOBER 2014
[26] Z. Yang et al., “Uncovering social network Sybils in the wild, in Proc.
Int. Microelectron. Conf. (IMC), 2011, pp. 259–268.
[27] D. Quercia and S. Hailes, “Sybil attacks against mobile users: Friends
and foes to the rescue, in Proc. IEEE IEEE Conf. Comput. Commun.
(INFOCOM), 2010, pp. 336–340.
[28] S. Mathur et al., “ParkNet: Drive-by sensing of road-side parking
statistics, in Proc. MobiSys, 2010, pp. 123–136.
[29] A. Pantelopoulos and N. Bourbakis, A survey on wearable sensor-based
systems for health monitoring and prognosis, IEEE Trans. Syst., Man,
Cybern. C, Appl. Rev., vol. 40, no. 1, pp. 1–12, Jan. 2010.
[30] X. Liang et al., “Enabling pervasive healthcare through continuous
remote health monitoring, IEEE Wireless Commun., vol. 19, no. 6,
pp. 10–18, Dec. 2012.
[31] K. Zhang, X. Liang, M. Baura, R. Lu, and X. S. Shen, “PHDA:
A priority based health data aggregation with privacy preservation for
cloud assisted WBANs, Elsevier Inform. Sci., pp. 1–12, Jun. 2014.
[32] C. Qian, H. Ngan, Y. Liu, and L. Ni, “Cardinality estimation for large-
scale RFID systems, IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 9,
pp. 1441–1454, Sep. 2011.
[33] M. Li, W. Lou, and K. Ren, “Data security and privacy in wire-
less body area networks, IEEE Wireless Commun., vol. 17, no. 1,
pp. 51–58, Feb. 2010.
[34] F. Zhang and Y. Lian, “QRS detection based on multiscale mathematical
morphology for wearable ECG devices in body area networks, IEEE
Trans. Biomed. Circuits Syst., vol. 3, no. 4, pp. 220–228, Aug. 2009.
[35] B. Lee, S. Chen, and K. Sienko, A wearable device for real-time motion
error detection and vibrotactile instructional cuing, IEEE Trans. Neural
Syst. Rehabil. Eng., vol. 19, no. 4, pp. 374–381, Apr. 2011.
[36] A. Champion et al., “E-SmallTalker: A distributed mobile system for
social networking in physical proximity, IEEE Trans. Parallel Distrib.
Syst., vol. 24, no. 8, pp. 1535–1545, Jun. 2013.
[37] H. Shen, Z. Li, G. Liu, and J. Li, “SOS: A distributed mobile Q&A
system-based on social networks, IEEE Trans. Parallel Distrib. Syst.,
vol. 25, no. 4, pp. 1066–1077, Apr. 2014.
[38] X. Liang et al., “Fully anonymous profile matching in mobile social
networks, IEEE J. Sel. Areas Commun., vol. 31, no. 9, pp. 641–655,
Sep. 2013.
[39] X. Liang, X. Lin, K. Zhang, and X. Shen, “Security and privacy
in mobile social network: Challenges and solutions, IEEE Wireless
Commun., vol. 21, no. 1, pp. 33–41, Feb. 2014.
[40] K. Zhang, X. Liang, R. Lu, and X. Shen, “SAFE: A social based
updatable filtering protocol with privacy-preserving in mobile social
networks, in Proc. Int. Conf. Commun. (ICC), 2013, pp. 6045–6049.
[41] K. Zhang, X. Liang, R. Lu, X. Shen, and H. Zhao, “VSLP: Voronoi-
socialspot-aided packet forwarding protocol with receiver location pri-
vacy in MSNS, in Proc. IEEE Conf. Global Commun. (GLOBECOM),
2012, pp. 348–353.
[42] D. Tran, B. Min, J. Li, and L. Subramanian, “Sybil-resilient online
content voting, in Proc. USENIX Netw. Syst. Design Implement. (NSDI),
2009, pp. 15–28.
[43] Y. Reddy, A game theory approach to detect malicious nodes in
wireless sensor networks, in Proc. 3rd Int. Conf. Sensor Technol. Appl.
(SENSORCOMM), 2009, pp. 462–468.
[44] G. Wang et al.
, “You are how you click: Clickstream analysis for Sybil
detection, in Proc. 22nd USENIX Security Symp., 2013, pp. 241–255.
[45] J. Kleinberg, “The small-world phenomenon: An algorithm perspective,
in Proc. ACM Symp. Theory Comput. (STOC), 2000, pp. 163–170.
[46] A. Cheng and E. Friedman, “Sybilproof reputation mechanisms, in
Proc. SIGCOMM Workshop, 2005, pp. 128–132.
[47] K. Walsh and E. Sirer, “Experience with an object reputation system
for peer-to-peer filesharing, in Proc. 3rd Conf. Netw. Syst. Design
Implement. (NSDI), 2006, pp. 1–14.
[48] R. Andersen, F. Chung, and K. Lang, “Local graph partitioning using
pagerank vectors, in Proc. 47th Annu. IEEE Symp. Foundations Com-
put. Sci. (FOCS), 2006, pp. 475–486.
[49] T. Haveliwala, “Topic-sensitive PageRank: A context-sensitive ranking
algorithm for web search, IEEE Trans. Knowl. Data Eng., vol. 15, no. 4,
pp. 784–796, Jul./Aug. 2003.
[50] R. Morselli, B. Bhattacharjee, M. Marsh, and A. Srinivasan, “Efficient
lookup on unstructured topologies, IEEE J. Sel. Areas Commun.,
vol. 25, no. 1, pp. 62–72, Feb. 2007.
[51] P. Flajolet, D. Gardy, and L. Thimonier, “Birthday paradox, coupon
collectors, caching algorithms and self-organizing search, Elsevier
Discrete Appl. Math., vol. 39, no. 3, pp. 207–229, 1992.
[52] F. Spitzer, Principles of Random Walk. New York, NY, USA: Springer,
1964.
[53] H. Yu, “Sybil defenses via social networks: A tutorial and survey,
SIGACT News, vol. 42, no. 3, pp. 80–101, 2011.
[54] L. Alvisi, A. Clement, A. Epasto, S. Lattanzi, and A. Panconesi, “SoK:
The evolution of Sybil defense via social networks, in IEEE Symp.
Security Privacy, 2013, pp. 382–396.
[55] L. Bilge, T. Strufe, D. Balzarotti, and E. Kirda, All your contacts
are belong to us: Automated identity theft attacks on social net-
works, in Proc. 18th Int. Conf. World Wide Web (WWW), 2009,
pp. 551–560.
[56] L. Von Ahn, M. Blum, N. Hopper, and J. Langford, “CAPTCHA:
Using hard AI problems for security, in Proc. EUROCRYPT, 2003,
pp. 294–311.
[57] J. Leskovec, J. Kleinberg, and C. Faloutsos, “Graphs over time: Densi-
fication laws, shrinking diameters and possible explanations, in Proc.
11th ACM SIGKDD Int. Knowl. Discovery Data Mining (KDDWS),
2005, pp. 177–187.
[58] Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, Aiding the detection
of fake accounts in large scale social online services, in Proc. 9th
USENIX Conf. Netw. Syst. Design Implement. (NSDI), 2012, pp. 1–14.
[59] G. Danezis and P. Mittal, “SybilInfer: Detecting Sybil nodes using social
networks, in Proc. NDSS, 2009, pp. 1–15.
[60] P. Fong, “Preventing Sybil attacks by privilege attenuation: A design
principle for social network systems, in IEEE Symp. Security Privacy,
2011, pp. 263–278.
[61] P. Denning, “Fault tolerant operating systems, ACM Comput. Surveys,
vol. 8, no. 4, pp. 359–389, 1976.
[62] N. Tran, J. Li, L. Subramanian, and S. Chow, “Optimal Sybil-resilient
node admission control, in Proc. IEEE Conf. Comput. Commun.
(INFOCOM), 2011, pp. 3218–3226.
[63] Q. Cao and X. Yang, “SybilFence: Improving social-graph-based
Sybil defenses with user negative feedback, Duke Univ., Comput.
Sci., Tech. Rep. CS-TR-2012-05, Mar. 2012 [Online]. Available:
http://arxiv.org/abs/1304.3819
[64] P. Dandekar, A. Goel, R. Govindan, and I. Post, “Liquidity in credit
networks: A little trust goes a long way, in Proc. 12th ACM Conf.
Electron. Commerce (EC), 2011, pp. 147–156.
[65] B. Viswanath, M. Mondal, K. Gummadi, A. Mislove, and A. Post,
“Canal: Scaling social network-based Sybil tolerance schemes, in Proc.
7th ACM Eur. Conf. Comput. Syst. (EuroSys), 2012, pp. 309–322.
[66] R. Delaviz, N. Andrade, J. Pouwelse, and D. Epema, “SybilRes:
A Sybil-resilient flow-based decentralized reputation mechanism, in
Proc. IEEE 32nd Int. Conf. Distrib. Comput. Syst. (ICDCS), 2012,
pp. 203–213.
[67] A. Mohaisen, N. Hopper, and Y. Kim, “Keep your friends close:
Incorporating trust into social network-based Sybil defenses, in Proc.
IEEE Conf. Comput. Commun. (INFOCOM), 2011, pp. 1943–1951.
[68] B. Viswanath, A. Post, K. Gummadi, and A. Mislove, An analysis of
social network-based Sybil defenses, in Proc. ACM SIGCOMM Conf.,
2010, pp. 363–374.
[69] W. Wei, F. Xu, C. Tan, and Q. Li, “SybilDefender: Defend against Sybil
attacks in large social networks, in Proc. IEEE Conf. Comput. Commun.
(INFOCOM), 2012, pp. 1951–1959.
[70] Z. Cai and C. Jermaine, “The latent community model for detecting
Sybils in social networks, in Proc. 19th Annu. Netw. Distrib. S yst.
Security Symp. (NDSS), 2012, pp. 1–13.
[71] L. Shi, S. Yu, W. Lou, and Y. T. Hou, “SybilShield: An agent-
aided social network-based Sybil defense among multiple communi-
ties, in Proc. IEEE Conf. Comput. Commun. (INFOCOM), 2013,
pp. 1034–1042.
[72] J. Xue et al., “VoteTrust: Leveraging friend invitation graph to defend
against social network Sybils, in Proc. IEEE Conf. Comput. Commun.
(INFOCOM), 2013, pp. 2400–2408.
[73] J. Jiang et al., “Understanding latent interactions in online social
networks, in Proc. 10th ACM SIGCOMM Conf. Internet Meas. (IMC),
2010, pp. 369–382.
[74] N. Dalal and B. Triggs, “Histograms of oriented gradients for human
detection, in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern
Recognit. (CVPR), 2005, pp. 886–893.
[75] C. Hsu and C. Lin, A comparison of methods for multiclass support
vector machines, IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425,
Mar. 2002.
[76] G. Wang et al., “Social turing tests: Crowdsourcing Sybil detec-
tion, in Proc. Netw. Distrib. Syst. Security Symp. (NDSS), 2012,
pp. 1–16.
[77] H. Yu, C. Shi, M. Kaminsky, P. Gibbons, and F. Xiao, “DSybil: Optimal
Sybil-resistance for recommendation systems, in IEEE Symp. Security
Privacy, 2009, pp. 283–298.
[78] W. Chang, J. Wu, C. Tan, and F. Li, “Sybil defenses in mobile social
networks, in Proc. IEEE Conf. Global Commun. (GLOBECOM), 2013,
pp. 1–6.
ZHANG et al.: SYBIL ATTACKS AND THEIR DEFENSES IN IoT 383
[79] D. Boneh and H. Shacham, “Group signatures with verifier-local revo-
cation, in Proc. 11th ACM Conf. Comput. Commun. Security (CCS),
2004, pp. 168–177.
[80] T. Zhou, R. Choudhury, P. Ning, and K. Chakrabarty, “Privacy-
preserving detection of Sybil attacks in vehicular ad hoc networks,
in Proc. MobiQuitous, 2007, pp. 1–8.
[81] T. Zhou, R. Choudhury, P. Ning, and K. Chakrabarty, “P
2
DAP - Sybil
attacks detection in vehicular ad hoc networks, IEEE J. Sel. Areas
Commun., vol. 29, no. 3, pp. 582–594, Mar. 2011.
[82] B. Triki, S. Rekhis, M. Chammem, and N. Boudriga, A privacy
preserving solution for the protection against Sybil attacks in vehicular
ad hoc networks, in Proc. Wireless Mobile Netw. Conf. (WMNC), 2013,
pp. 1–8.
[83] K. Ren, H. Su, and Q. Wang, “Secret key generation exploiting channel
characteristics in wireless communications, IEEE Wireless Commun.,
vol. 18, no. 4, pp. 6–12, Aug. 2011.
[84] K. Ren, W. Lou, and Y. Zhang, “LEDS: Providing location-aware end-
to-end data security in wireless sensor networks, IEEE Trans. Mobile
Comput., vol. 7 , no. 5, pp. 585–598, May 2008.
[85] L. Xiao, L. Greenstein, N. Mandayam, and W. Trappe, “Channel-
based detection of Sybil attacks in wireless networks, IEEE Trans. Inf.
Forensics Security, vol. 4, no. 3, pp. 492–503, Sep. 2009.
[86] M. Demirbas and Y. Song, An RSSI-based scheme for Sybil attack
detection in wireless sensor networks, in Proc. Int. Symp. World
Wireless Mobile Multimedia Netw. (WOWMOM), 2006, pp. 564–570.
[87] S. Lv, X. Wang, X. Zhao, and X. Zhou, “Detecting the Sybil attack
cooperatively in wireless sensor networks, in Proc. Int. Conf. Comput.
Intell. Security (CIS), 2008, pp. 442–446.
[88] G. Guette and B. Ducourthial, “On the Sybil attack detection in
VANET, in Proc. IEEE Int. Conf. Mobile Adhoc Sensor Syst. (MASS),
2007, pp. 1–6.
[89] B. Yu, C. Xu, and B. Xiao, “Detecting Sybil attacks in VANETs,
J. Parallel Distrib. Comput., vol. 73, no. 6, pp. 746–756, 2013.
[90] S. Abbas, M. Merabti, D. Llewellyn-Jones, and K. Kifayat, “Lightweight
Sybil attack detection in MANETs, IEEE Syst. J., vol. 7, no. 2,
pp. 236–248, Jun. 2013.
[91] C. Piro, C. Shields, and B. Levine, “Detecting the Sybil attack in mobile
ad hoc networks, in Proc. SecureComm, 2006, pp. 1–11.
[92] M. Mutaz, L. Malott, and S. Chellappan, “Leveraging platoon dispersion
for Sybil detection in vehicular networks, in Proc. Annu. Int. Conf.
Privacy Security Trust (PST), 2013, pp. 340–347.
[93] S. Park, B. Aslam, D. Turgut, and C. Zou, “Defense against Sybil
attack in the initial deployment stage of vehicular ad hoc network
based on roadside unit support, Security Commun. Netw., vol. 6, no. 4,
pp. 523–538, 2013.
[94] J. Newsome, E. Shi, D. Song, and A. Perrig, “The Sybil attack in sensor
networks: Analysis and defenses, in Proc. 3rd Int. Symp. Inf. Process.
Sensor Netw. (IPSN), 2004, pp. 259–268.
[95] Q. Zhang, P. Wang, D. Reeves, and P. Ning, “Defending against Sybil
attacks in sensor networks, in Proc. IEEE Int. Conf. Workshop Distrib.
Comput. Syst. (ICDCS), 2005, pp. 185–191.
[96] F. Li, P. Mittal, M. Caesar, and N. Borisov, “SybilControl: Practical
Sybil defense with computational puzzles, in Proc. Soc. Tech. Commun.
(STC), 2012, pp. 67–78.
[97] E. Damiani, S. De Capitani di Vimercati, S. Paraboschi, and P. Samarati,
“Managing and sharing servents’ reputations in P2P systems, IEEE
Trans. Knowl. Data Eng., vol. 15, no. 4, pp. 840–854, Jul./Aug. 2003.
[98] S. Marti, T. Giuli, K. Lai, and M. Baker, “Mitigating routing misbehavior
in mobile ad hoc networks, in Proc. 6th Annu. Int. Conf. Mobile
Comput. Netw. (MOBICOM), 2000, pp. 255–265.
[99] J. Dinger and H. Hartenstein, “Defending the Sybil attack in P2P
networks: Taxonomy, challenges, and a proposal for self-registration,
in Proc. 1st Int. Conf. Avail. Rel. Security (ARES), 2006, pp. 756–763.
Kuan Zhang (S’13) received the B.Sc. degree
in electrical and computer engineering and M.Sc.
degree in computer science from Northeastern Uni-
versity, Shenyang, China, in 2009 and 2011, respec-
tively, and is currently working toward the Ph.D.
degree at the University of Waterloo, Waterloo, ON,
Canada.
He is currently with the Broadband Communi-
cations Research (BBCR) Group, Department of
Electrical and Computer Engineering, University of
Waterloo. His research interests include security and
privacy for mobile social networks.
Xiaohui Liang (S’10–M’13) received the M.Sc. and
B.Sc. degrees in computer science from Shanghai
Jiao Tong University, Shanghai, China, in 2009
and 2006, respectively, and the Ph.D. degree in
electrical and computer engineering from the Uni-
versity of Waterloo, Waterloo, ON, Canada, in
2013.
He is a Postdoctoral Fellow with the Depart-
ment of Computer Science, Dartmouth College,
Hanover, NH, USA. His research interests include
security, privacy, and trustworthiness of information
and communication for mobile healthcare, mobile social networks, and cloud
computing.
Rongxing Lu (S’09–M’11) received the Ph.D. de-
gree in computer science from Shanghai Jiao Tong
University, Shanghai, China, in 2006, and the Ph.D.
degree in electrical and computer engineering from
the University of Waterloo, Waterloo, ON, Canada,
in 2012.
He is currently an Assistant Professor with the
Division of Communication Engineering, School
of Electrical and Electronics Engineering, Nanyang
Technological University, Singapore. His research
interests include wireless network security, applied
cryptography, and trusted computing.
Xuemin (Sherman) Shen (M’97–SM’02–F’09)
received the B.Sc. degree from Dalian Maritime
University, Dalian, China, in 1982, and the M.Sc.
and Ph.D. degrees from Rutgers University, New
Brunswick, NJ, USA, in 1987 and 1990, all in
electrical engineering.
He is a Professor and University Research Chair
with the Department of Electrical and Computer
Engineering, University of Waterloo, Waterloo, ON,
Canada. He is a coauthor of 3 books, and has
published more than 400 papers and book chapters
in wireless communications and networks, control, and filtering. His research
interests include resource management in interconnected wireless/wired net-
works, ultra-wideband (UWB) wireless communications networks, wireless
network security, wireless body area networks, and vehicular ad hoc and
sensor networks.
Dr. Shen is the Editor-in-Chief of IEEE Network, and will serve as a Techni-
cal Program Committee Cochair for IEEE INFOCOM 2014. He is the Chair
of the IEEE ComSoc Technical Committee on Wireless Communications,
and P2P Communications and Networking, and a voting member of GITC.
He was a Founding Area Editor for the IEEE T
RANSACTIONS ON WIRE-
LESS COMMUNICATIONS, and a Guest Editor for the IEEE JOURNAL OF
SELECTED AREAS IN COMMUNICATIONS, IEEE Wireless Communications,
and IEEE Communications Magazine. He also served as the Technical
Program Committee Chair for GLOBECOM07 and INFOCOM14, Tutorial
Chair for ICC08, and Symposia Chair for ICC10. He was the recipient
of the Excellent Graduate Supervision Award in 2006 and the Outstanding
Performance Award in 2004, 2007, and 2010 from the University of Waterloo,
and the Premiers Research Excellence Award in 2003 from the Province of
Ontario, Canada. He is a Registered Professional Engineer in the Province of
Ontario, Canada, a Fellow of the Engineering Institute of Canada, a Fellow
of the Canadian Academy of Engineering, and was a ComSoc Distinguished
Lecturer.
... However, the existing solutions are not effective for mobile and timevarying vehicular networks. This is due to the lack of the availability of the historical behavior in anomaly detection schemes and difficulty in tracing high mobility vehicles [7]. Some researchers propose to use privacy-preserving schemes against Sybil attacks [8], [9]. ...
Preprint
The integration of ML in 5G-based Internet of Vehicles (IoV) networks has enabled intelligent transportation and smart traffic management. Nonetheless, the security against adversarial attacks is also increasingly becoming a challenging task. Specifically, Deep Reinforcement Learning (DRL) is one of the widely used ML designs in IoV applications. The standard ML security techniques are not effective in DRL where the algorithm learns to solve sequential decision-making through continuous interaction with the environment, and the environment is time-varying, dynamic, and mobile. In this paper, we propose a Gated Recurrent Unit (GRU)-based federated continual learning (GFCL) anomaly detection framework against adversarial attacks in IoV. The objective is to present a lightweight and scalable framework that learns and detects the illegitimate behavior without having a-priori training dataset consisting of attack samples. We use GRU to predict a future data sequence to analyze and detect illegitimate behavior from vehicles in a federated learning-based distributed manner. We investigate the performance of our framework using real-world vehicle mobility traces. The results demonstrate the effectiveness of our proposed solution for different performance metrics.
... 3) Sybil Attacks. Sybil adversaries may manipulate multiple faked/stolen identities to gain disproportionately large influence [47] on metaverse services (e.g., reputation service and votingbased service), thereby compromising system effectiveness. ...
Preprint
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The main purpose of this book is to give a systematic treatment of singular homology and cohomology theory. It is in some sense a sequel to the author's previous book in this Springer-Verlag series entitled Algebraic Topology: An Introduction. This earlier book is definitely not a logical prerequisite for the present volume. However, it would certainly be advantageous for a prospective reader to have an acquaintance with some of the topics treated in that earlier volume, such as 2-dimensional manifolds and the funda­ mental group. Singular homology and cohomology theory has been the subject of a number of textbooks in the last couple of decades, so the basic outline of the theory is fairly well established. Therefore, from the point of view of the mathematics involved, there can be little that is new or original in a book such as this. On the other hand, there is still room for a great deal of variety and originality in the details of the exposition. In this volume the author has tried to give a straightforward treatment of the subject matter, stripped of all unnecessary definitions, terminology, and technical machinery. He has also tried, wherever feasible, to emphasize the geometric motivation behind the various concepts.
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