=Paper=
{{Paper
|id=Vol-1578/paper2
|storemode=property
|title=Machine to Machine Trust in the IoT Era
|pdfUrl=https://ceur-ws.org/Vol-1578/paper2.pdf
|volume=Vol-1578
|authors=Ling Liu,Margaret Loper,Yusuf Ozkaya,Abdurrahman Yasar,Emre Yigitoglu
|dblpUrl=https://dblp.org/rec/conf/atal/LiuLOYY16
}}
==Machine to Machine Trust in the IoT Era==
https://ceur-ws.org/Vol-1578/paper2.pdf
Machine to Machine Trust in the IoT Era
Ling Liu(+), Margaret Loper(*), Yusuf Ozkaya(+), Abdurrahman Yasar(+), Emre Yigitoglu(+)
(+)
School of Computer Science
(*)
Georgia Tech Research Institute
Georgia Institute of Technology
Abstract
Machine to machine communications are at the center stage of the Internet of things (IoT).
Connecting the physical world with the digital world not only creates new opportunities for
innovation and discovery, but also opens doors for misuse and abuse. This paper argues that
reputation based trust can be an effective countermeasure for securing machine-to-machine
communications. We propose to establish machine-to-machine trust by taking into account both
transaction/interaction service behaviors and feedback rating behaviors in the presence of bogus
transactions and dishonest feedbacks. Our machine-to-machine trust model, called M2MTrust,
introduces two novel trust metrics: (1) pairwise similarity based feedback credibility and (2)
threshold-controlled trust propagation. We compute the direct trust from machine A to machine B by
utilizing their pairwise rating similarity as the weight to the normalized aggregate of ratings that A
has given to B. Our direct trust computation model can effectively constrain malicious nodes to gain
direct trusts from dishonest feedback ratings by leveraging feedback credibility. Furthermore, our
threshold-controlled trust propagation mechanism can successfully block the trust propagation from
good nodes to malicious nodes. We conduct extensive experiments using simulation and real
datasets and the experimental results show that M2MTrust significantly outperforms other trust
metrics in terms of both attack resilience and performance in the presence of dishonest feedbacks
and sparse feedback ratings against four representative attack models.
1. Introduction
The Internet of Things (IoT) refers to the capability of enabling physical objects to be connected, tracked,
coordinated, or controlled by using sensors, actuators and Internet technology. By bringing machines into
the connected world, be it hand-held devices, smart phones, self-driving vehicles, consumer appliances, and
wireless terminals, the IoT holds the promise of enabling machines with embedded actuators and sensors to
be programmed to take action on their own. Machine to machine communications will be at the center stage
of the Internet of everything (IoE). Connecting the physical world with the digital world not only creates
new opportunities for science and engineering discovery, for business and industry innovation, and for new
life-enhancing experiences but also opens doors for misuse and abuse, as well as new privacy risks and
security violations. For example, the connections that allow remote machines to take action without a
human operator are subject to hacking by criminals or terrorists. The machines in the connected world may
be infected by external and side channel attacks, such as Trojan horse programs, viruses, dataflow replay
and DDoS attacks. Trust and reputation management are recognized as a popular and yet effective
countermeasure for secure machine to machine (M2M) interactions.
We argue that in-depth understanding of trust and reputation is critical for agent societies where agents
can be machines with embedded actuators and sensors, or humans with hand-held devices. Trust operates at
many levels of interactions in agent societies, including human-to-human, human-to-machine, and
machine-to-machine interactions. Trust has multi-facet and can play many roles in many contexts. For
instance, the use of reputation mechanisms is one way to establish trusts based on interactions and feedback
ratings of the interactions. Many computational and theoretical models and approaches to reputation have
been developed in recent years (for ecommerce, social networks, blogs, etc.). To ensure reputation based
trust is established reliably, one needs to ascertain reliable interactions and transactions by identity and
associated trustworthiness. A high quality trust is capable of reflecting the trade-off between individual
utility and collective interest.
Trust and reputation also involve deception, privacy, security and control [Du+14, Hwa09, Var14].
Furthermore, many cloud infrastructure providers requite the IoT multi-agent systems and applications to
be responsible for the application level security, privacy and trust vulnerabilities [Ber08, Li+13, Ris09]. For
example, Amazon [AWS] states that security of tenant virtual machines is the responsibility of tenants since
they are free to run any of operating systems or applications, although it claims to safeguard the underlying
infrastructure. Also attackers can pretend to be legitimate (good) service providers but provide
untrustworthiness service components. The service components provided by authentic providers may also
embody security threats that can be exploited by attackers. Furthermore, multiple malicious attackers may
launch colluding attacks on certain targeted service functions [BarXX,Du+14]. The reputation-based trust
management can effectively measure resource provision QoS through trust-guided selection of services
providers [Hwa09, Ima13, Li+10, San14].
In this paper, we present a reputation based machine-to-machine trust framework, called M2MTrust, to
facilitate a network of machines to accomplish a collaboration task with high quality and high reliability.
Each of these machines can be viewed as a collaborative agent in a networked multi-agent system.
M2MTrust is novel in two aspects: First, M2MTrust utilizes machine to machine interaction experiences to
establishes direct trust between a pair of machines. Example interactions can be transactional services and
feedback ratings. To increase attack resilience of our M2MTrust model in the presence of malicious
services (e.g., inauthentic file downloads) and dishonest feedback, M2MTrust promotes a clean distinction
of transactional experience based reputation from feedback referral based reputation and compute the direct
trust from machine A to machine B based on both the feedback ratings that B have received from A as well
as the rating similarity that both A and B have given to the same set of machines that they have interacted
with historically. We show that the rating similarity based feedback credibility can be instrumental for
improving the attack resilience of the simply rating aggregation based direct trust computation. Second,
M2MTrust employs a trust propagation kernel to handle the rating sparseness and cold start problem with
feedback rating based trust computation [Kam03, Xio04]. However, we argue that though the uniform trust
propagation kernel is popular and simple to implement, it is vulnerable to some strategic malicious attacks
[Kam03, Fan41]. To counter such vulnerabilities, we develop a threshold-based controlled trust propagation
kernel. This allows M2MTrust to propagate the trust of an agent (e.g., machine A) to only those of the
agents to whom A has rated before (i.e., A’s neighbor agents in the rating network) and with whom A also
has similar transactional and feedback rating behaviors. Combined with the above two novel features,
M2MTrust makes it much harder for malicious entities to gain trust from good entities, and enables good
entities to share their experiences and feedback within the circle of agents who share similar transactional
and feedback behaviors. Our controlled trust propagation scheme is highly attack resilient in the presence of
dishonest feedback, sparse ratings, and a large number of malicious entities, because M2MTrust can
significantly block the trust propagation paths from good entities to malicious ones. We conduct extensive
evaluations of our proposed M2MTrust in terms of its effectiveness and efficiency against four
representative attack models using both simulation and realistic datasets. Our experimental results show
that M2MTrust significantly outperforms other reputation-based trust models.
The rest of this paper is organized as follows. Section 2 gives an overview of the basic concepts and
terminology, the attack models and the core components of a reputation-based trust model. We describe in
detail our M2MTrust model in Section 3. We report the experimental results in Section 4 and conclude the
paper in Section 5.
2. OVERVIEW
2.1 Preliminary
Machine to Machine Interaction Network.
Let G= represent a large-scale machine to machine
interaction network of n entities, |V|=n. Each entity is connected to a minimum number (m>1) of other
entities. An edge e=(i, j) is in E if two entities i and j are connected in the network G. Example transactions
performed in such a network could be content/file sharing or scientific computation. It is well known that
the real world machine to machine interaction network typically follows a zipf skewed degree distribution
such that large number of entities connect to a small number of entities and only a few entities connect to a
large number of entities. Thus an entity may be served by another entity that is several hops away in the
network G. When an entity receives several responses for its service request, it may rely on their reputation
trust scores to determine which one is selected to be its service provider.
Machine to Machine Transaction Rating Network. Upon the completion of a transaction between a pair
of entities, the service entity can rate the provider entity in terms of its quality of service (QoS) provisioned,
denoted by tr(i, j). An entity i of the system can rate another entity j if it has an actual transaction with
entity j. The rating can be either binary [Kam03,Ric03] or multi-scale [Fen12,Su+13]. For example, with
binary rating model, i can give j the positive feedback rating by tr(i, j) = 1 or negative rating by tr(i, j) = −1.
By default, tr(i, j) = 0 for i, j = 1, …, n, and it implies that i has never had any transaction with j. Based on
the rating relationship between a pair of machines, we can construct a machine to machine rating network.
Note that the n entities in the rating network are the same as the machine-to-machine interaction network.
However, two entities that have edges in the rating network may not be connected in the machine-to-
machine interaction network and vice versa.
Simple Rating Aggregation. Let sij denote the simple aggregate rating that entity i gives to another entity j.
We can define sij by the sum of individual feedback ratings: sij = ∑ tr (i, j ) . For binary rating, this is
equivalent to the difference between the satisfied transaction number sat(i, j) and unsatisfied transaction
number unsat(i, j) that entity i has received from entity j. Namely sij = sat(i, j) – unsat(i, j).
Normalized Rating Aggregate and Direct Trust Score. It is well understood that using sij to define the
direct trust value that i gives to j is problematic [Xio04], because this can introduce certain vulnerability
due to some unwanted bias. For example, with 21 positive rating and 1 negative rating will receive the same
trust score of 20 as the entity with 100 positive rating and 80 negative ratings. However, it is obvious that
the entities that received large proportion of negative ratings are at the risk of being dishonest or malicious
raters. Thus, normalized rating aggregation schemes are proposed [Kam03, Li+04] to prevent dishonest or
malicious entities from colluding by giving arbitrarily high direct trust to other malicious entities, and
arbitrarily low direct trust to good entities. In addition, for those entities that have not received any ratings
because they have not been selected as service providers for any request (e.g., cold start or rating
sparseness), a common mechanism is to use a small number of bootstrap entities to serve as pre-trusted
entities (machines) in the network. One method to normalize the direct trust score that i has over j, denoted
by cij , is given below [Kam03]:
cij = max(sij ,0) ∑ k max(sik ,0) if ∑ k max(sik ,0) ≠ 0
(1)
cij = p j otherwise
When sij = 0, we set cij = pj and pj = 1/|P| if j ∈ P, P is the set of pre-trusted bootstraping entities. Using a
small number of pre-trusted members as the central authority of the system, it can help bootstrap the trust
system initially [Kam03]. By utilizing cij (i, j=1, …, n), we transform the rating network of n entities into a
direct trust network for the same n entities and if two entities have rating relationship, then they will have
direct trust relationship.
Trust Propagation. When a trust network is very sparse, namely each entity i only trusts a small number of
other entities, say j, such that cij>0, and for most j ∈[1, n], we have cij = 0. This will make it very hard for i
to find and select the right service providers because most of time entity i may not have any other entity that
can provide the service requested by i. This skewed problem has lead to the use of trust propagation kernel
to compute the transitive trust that entity i has over entity j as long as j is reachable from i in the rating (also
direct trust) network by graph traversal. Concretely, if j is reachable from i via another entity q in the direct
trust network, then we can compute the trust cij by the weighted summation of ciq and cqj : cij = ∑ ciq ⋅ cqj .
q
Let n denote the total number of entities in the system, we can define C as the matrix [ cij ] with n rows and
!
n columns. Let t k+1 denote the global trust vector of size n to be computed at (k+1)th round of iterations,
! ! !
0. To compute the similarity between two feedback vectors of participants
i and j, we use the Weighted Euclidean Distance (WED) method, which captures the degree of “dispersion”
in the historical feedback given by the participants i and j. The larger the dispersion is, the smaller the
similarity will be. Thus the feedback based similarity between two participants i and j can be defined as
follows:
⎧⎪1 − ∑ w(i , j , q ) ⋅ (Tr (i, q) − Tr ( j, q)) 2 if | comn(i, j ) |≠ 0
sim(i, j ) = ⎨ q∈comn ( i , j )
⎪⎩0 otherwise
| r ( i , q )|
∑ k =1
trk (i, q) (5)
Tr (i, q) =
| r (i, q) |
| r ( j , q )|
∑ k =1
trk ( j, q)
Tr ( j, q) =
| r ( j, q) |
where comn(i, j ) denotes the subset of common participants that have had interaction with both i and j,
r (i, q) is the total number of transactions between i and q, and trk (i, q) denotes the kth normalized local trust
that participant i places on another participant j. w(i , j , q ) denotes the normalized weight of participant q’s
impact on similarity measure by calculating its standard deviation:
(Tr (i, q) − Avg (i, j, q)) 2 + (Tr ( j, q) − Avg (i, j, q)) 2
w '(i , j , q ) =
2
Tr (i, q) + Tr ( j, q)
Avg (i, j, q) = (6)
2
⎧ w '(i , j , q )
⎪⎪ if ∑ w '(i , j , m ) ≠ 0
w(i , j , q ) = ⎨ ∑ w '(i , j , m ) m
m
⎪
⎪⎩0 otherwise
The use of weighted Euclidean distance allows us to leverage different weights to amplify the rating
dissimilarity over the common transactional participants that are rated differently by i and j in the vectors
and . Consider an example feedback vectors of participants i and j over the
four other common participants are <0.10, 0.30, 0.02, 0.05> and <0.01, 0.05, 0.05, 0.85> respectively, we
can calculate that the traditional ED based similarity, which is 0.578, and the WED based similarity, which
is 0.328. Based on the intuitive analysis on the two vectors of feedback ratings, we can perceive that i and j
should be dissimilar. This shows that using weighted Euclidean distance based formula is more effective.
Similarity based Feedback Credibility. We define feedback credibility and utilize it to constrain the
malicious participants from receiving high feedback from other good participants even when they provide
satisfactory transactions. We use the exponential function of the pairwise similarity:
crij = e(1−1/ sim(i , j )) (7)
This formula indicates that the feedback credibility is exponentially constrained: the feedback credibility
will be high when the pairwise similarity is high, and vice versa. If sim(i, j ) = 1.0, then crij = 1.0; if sim(i, j )
= 0.0, then crij ≈ 0.0. Let cf ij denote the feedback credibility weighted direct trust score that participant i has
placed on participant j. We compute cf ij by utilizing similarity based feedback credibility as the weight to cij
as follows:
cfij = crij ⋅ cij = e(1−1/ sim(i , j )) ⋅ cij (8)
This feedback credibility weighted direct trust computation formula states that a participant has high local
trust value only if this participant has received high transaction based ratings (cij) and high feedback
credibility at the same time. For example, the direct trust value that a good participant i places on a
malicious participant j should be weighted by their similarity-based feedback credibility. Given that the
good participant i and the malicious participant j will be extremely dissimilar in their feedback behaviors,
thus the feedback credibility crij is very small. By Formula (8), the weighted direct trust that i has for j will
be much smaller than the normalized rating aggregate value, cij. Thus, by using feedback credibility as a
weight to the transaction based rating, M2MTrust can effectively reduce the positive ratings from good
participants to malicious participants no matter whether they are malicious camouflage (Attack Model C) or
malicious spies (Attack Model D).
Threshold-Controlled Trust Propagation. Although we can restrain malicious participants from
gaining high local trust through feedback credibility, we still cannot completely cut down the trust
propagation to malicious participants once they have built up the trust propagation path (direct trust) from
good participants. Thus, we propose a threshold-controlled trust propagation kernel:
τ ʹ′ij = 1/(1 + esim(i , j ) ) (9)
To coordinate with direct trust, we map this threshold to the same interval [0, 1] by max-min method:
τ ijʹ′ − 1/(1 + e max ( sim (u ,v )) )
τ ij = (10)
1/(1 + e min ( sim (u ,v )) ) − 1/(1 + e max ( sim (u ,v )) )
Obviously, max(sim(u,v))= 1.0 and min(sim(u,v)) = 0.0. Hence we compare the feedback credibility
weighted direct trust score cf ij with this exponential threshold τ ij . If cf ij ≥ τ ij , we propagate trust from i to j;
if cf ij < τ ij , we block the trust propagation. We compute the global trust scores at the (k+1)th iteration for all
n participants in the machine to machine network by utilizing the global trust scores computed at k th
iteration as follows:
! ! !
t k+1 = (1− a)LT t k + ap (11)
where a is a jumping factor to avoid trust propagation to be trapped in a malicious clique. Also, we set the
initial trust score for a participant i as ti0 = ∑ j cfij ⋅ cf ji .
The global trust for each participant over the transactional network can be defined by using the
threshold-controlled trust propagation matrix M:
" ϕ ⋅ cf … ϕ ⋅ cf %
$ 11 11 1n 1n '
M =$ ! " ! '
$ '
$ ϕ n1 ⋅ cf n1 ! ϕ nn ⋅ cf nn '
# &
)
+1, if cf uv ≥ τ uv
ϕ uv = *
+
,0, otherwise
where ϕuv denotes whether the feedback credibility weighted local trust value cf uv is bigger than or equal
to their threshold τ uv , if the local trust cf uv is big enough and the threshold τ uv is small enough, then u
propagates trust to v, otherwise u discards the trust propagation to v.
To facilitate the comparison of different propagating weights, we need to normalize matrix M:
⎧ ϕuv ⋅ cfuv
⎪ if ∑ q ϕuq ⋅ cfuq ≠ 0
muv = ⎨ ∑ q ϕuq ⋅ cfuq (12)
⎪
⎩0 otherwise
Thus we propose the following matrix formula to calculate the global trust score:
!k+1 !k "!
t = (1− α )M T ⋅ t + α ⋅ p (13)
th th
The (k+1) iteration computation relies on the k iteration:
t k +1 (i) = (1 − α )(m1i t k (1) + L + mni t k (n)) + α ⋅ pi (14)
Our threshold-controlled trust propagation kernel can successfully block the trust propagation from good
participants to malicious ones.
4. EXPERIMENTAL EVALUATION
We
evaluate
M2MTrust
model
in
terms
of
efficiency,
effectiveness
and
attack
resilience.
To
make
a
fair
comparison
with
EigenTrust,
we
build
a
simulator
on
the
top
of
TM/RM
simulation
platform
[TM/RM]
and
incorporate
all
four
attack
models
used
into
this
TM/RM
simulator
in
order
to
compare
the
performance
of
our
M2MTrust
with
EigenTrust,
ServiceTrust
and
Non-‐Trust
scenario.
Table
I
gives
the
list
of
the
parameters.
The
query/answer
network
is
setup
We evaluate M2MTrust model in terms of efficiency, effectiveness and attack resilience. To make a fair
comparison with EigenTrust, we build a simulator on the top of TM/RM simulation platform [TM/RM] and
incorporate all four attack models used into this TM/RM simulator in order to compare the performance of
our M2MTrust with EigenTrust, ServiceTrust and Non-Trust scenario. Table I gives the list of the
parameters. The query/answer network is setup in a similar fashion as [Kam03]. Both malicious and pre-
trusted participants have 10 initial neighbors, and good participants have 2 initial neighbors. Initially, only
pre-trusted participants have positive reputation. When a participant issues a query, the query is propagated
by the scoped broadcast mechanism with the specified hop-count horizon over the entire network.
Participants that receive the query will forward it to the next hop participant(s) and also check whether they
have the requested file or not, if have, respond it. We set 7 hops as the default response range. Furthermore,
the number of distinct files assigned to each participant follows the Zipf distribution, and popular files have
TABLE I
EXPERIMENTAL CONFIGURATION
number of good participants 600, 600, 700, 1000
number of pre-trusted participants 30
Network Structure number of initial neighbors of good participants 2
number of initial neighbors of malicious participants 10
number of initial neighbors of pre-trusted participants 10
number of hops for query process 7
Zipf distribution over 200
file distribution at good participants
distinct files
number of distinct files at good participant uniform random distribution
top % queries for most popular files pre-trusted participants respond to 5%
File Distribution
% file categories owned by good participants in Attack Model A, B and C 15%
% file categories owned by good participants in Attack Model D 10%
% file categories owned by malicious participants in Attack Model A, B, D 100%
% of file categories owned by malicious participants in Attack Model C 55%
% of download requests in which good participant returns inauthentic file 5%
Participant
Behavior downloads source selection algorithm probabilistic algorithm
probability that participant with global trust value 0 is selected range [0%-10%]
more copies in the system. On the other hand, the
number of queries issued for different files is also
based on Zipf distribution.
4.1 Performance Evaluation
We compare M2MTrust with Non-Trust and
EigenTrust under the four attack models. Fig. 2
shows the results. The total numbers of
transactions are 6300 in Attack Models A and B,
7300 in Attack Model C and 10300 in Attack
Model D. Different from variable percentages of
malicious participants in Attack Models A and B,
the amounts of malicious participants are constant
Fig. 2. Attack Model A in Attack Models C and D, and the percentages of
malicious participants are 27% and 39%
respectively. We observe that M2MTrust effectively constrains the trust propagation to malicious
participants from good participants due to the low rating similarity between them, in addition to the
threshold-driven based controlled propagation kernel.
In Attack Models C and D, M2MTrust also
significantly outperforms Non-Trust and EigenTrust.
Although the strategic malicious participants can gain
trust scores by acting as regular participants to
provide good services, the M2MTrust can
differentiate malicious participants through from good
participants by employing rating similarity based
feedback credibility and threshold-controlled trust
propagation kernel. Fig. 3 shows the global trust
scores under Attack Model C when f is 40%. The
global trust scores of malicious participants are very
high in EigenTrust, and in contrast, M2MTrust can
completely reduce the trust scores of malicious
Fig. 3. Attack Model B participants to zero through rating similarity weighted
direct trust and the threshold controlled propagation
kernel, which cut off the trust propagation paths from
good participants to malicious participants effectively.
4.2
Evaluation
Using
Epinions
Dataset
We evaluate the performance of M2MTrust using real
dataset Epinions [Ric03] in terms of Attack Models.
As we know, the strategic malicious participants in
Attack Models C and D can gain trust scores
through providing good services. Thus we create
some colluding participants to learn the
effectiveness of our M2MTrust metrics. Concretely,
10 malicious participants (ID: 1000-1009) are
Fig. 4. Attack Model C (f=40%). added into the Epinions dataset and connected to
the 10 most highly connected participants already
in the network to receive as many ratings as
possible. Then, we organize them in two ways: one
is to make these 10 participants form a chain to
give colluding entities high direct trust ratings (say
1.0) according to configuration under Attack Model
C, and the other is to divide them into two groups
(Group B and Group D) with 5 participants each
group according to the configuration in Attack
Model D, participants in Group D provide
trustworthiness services to gain high trust scores,
and in return boost other participants in Group B.
Since those malicious participants in Attack Model
Fig. 5. Attack Model D C and Group D act as regular participants, good
participants will give them high feedback ratings
and thus high direct trust scores. Therefore, we set an interval [0.5, 1.0] from which good (regular in
Epimions) participants select their direct trust ratings to assess the malicious participants, and another
interval [0, 0.05] from which malicious participants select their direct trust to assess good participants.
In addition, we utilize Zipf Distribution to generate edge weight between a pair of regular participants.
Fig. 6 shows the trust values of good nodes and malicious nodes under Threat model C. Fig. 7 zooms
Fig. 6 Trust values of malicious and good nodes Fig. 7 Zoom-in of the trust values of malicious nodes
in ePinions in ePinions
into the trust values of all malicious nodes under
Threat model C. Different from the configurations in EigenTrust wherein the initial trust
scores of pre-trusted participants are non-zero (1/|P|, P is the set of bootstrap/pre-trust nodes) and others are
zero. In M2MTrust, we compute the PageRank values as the initial trust scores. In Attack Model C, since
the added participants (1000-1009) are set by the same links from/to other participants, their trust scores are
equal by using the PageRank computation formula. EigenTrust increases the trust scores of these 10
malicious participants as they receive high direct trust ratings. In contrast, M2MTrust can effectively reduce
the trust scores of these 10 malicious participants thanks to its rating similarity weighted direct trust and its
threshold-controlled trust propagation kernel.
4.3
Computation Complexity
In EigenTrust, the time overhead mainly depends on the computation of trust scores. For each participant,
its trust score is computed by aggregating the trust scores of other n-1 (n is the network size) participants,
thus, for n participants, the computation complexity is O(n2). In M2MTrust, we need to compute the
pairwise rating similarity. However, we do not need to compute rating similarity for every pair of
participants in each iteration of the trust score refinement during the entire simulation. We just compute the
pairwise similarity only when the direct trust ratings placed on the common set of participants rated by this
pair of participants have been changed. Furthermore, in M2MTrust, for each participant, the threshold-
controlled propagation needs O(1) time to check whether a connected participant meets the threshold. The
loop will continue until all the connected participants are checked. Given that the connected neighbors are
no more than n. Thus, for the total participants, the computation complexity is also O(n2). Moreover,
M2MTrust still needs O(n2) to compute trust scores for all the participants. In general, the computation
overhead for M2MTrust is less than EigenTrust because M2MTrust can discard partial participants from
being processed for trust propagation through feedback credibility when the pairwise similarity is zero, thus
its time consumption is less than EigenTrust. In addition, M2MTrust can further cut off those connected
participants from trust propagation when they fail to pass the threshold check, thus the computation
overhead is much less than EigenTrust.
5. Conclusion
We
have
presented
M2MTrust,
an
attack
resilient
machine-‐to-‐machine
trust
model
and
showed
analytically
and
experimentally
that
M2MTrust
is
significantly
more
attack
resilient
than
other
trust
metrics.
Concretely,
we
promote
three
principled
design
goals.
First,
the
direct
trust
between
a
pair
of
participants
in
the
M2M
network
should
be
computed
by
taking
into
account
both
quality
(satisfactory
and
unsatisfactory
experiences)
and
quantity
of
their
interactions,
making
it
harder
and
costly
for
attackers
to
manipulate
the
trust
model.
Second,
our
trust
metrics
advocate
a
clean
separation
of
the
transaction
or
interaction
quality
from
the
feedback
quality
in
trust
computation.
This
significantly
strengthens
the
attack
resilience.
Third
but
not
the
least,
M2MTrust
incorporate
threshold-‐controlled
trust
propagation,
instead
of
uniform
trust
propagation
to
capture
the
non-‐
uniformity
of
trust
propagation
and
experience
sharing
among
the
group
of
connected
nodes
in
the
network.
We
conduct
extensive
experimental
results
using
simulation
and
real
dataset,
and
show
that
our
proposed
machine
to
machine
trust
model
is
effective
terms
of
both
performance
and
attack
resilience
in
the
presence
of
dishonest
feedbacks
and
sparse
feedback
ratings
against
four
representative
attack
models.
References
[AWS] “AWS security center.” Available: http://aws.amazon.com/security/.
[Ber08] S. Berger et al., “TVDc: Managing Security in the Trusted Virtual Datacenter,” ACM SIGOPS
Operating Systems Rev., vol. 42, no. 1, pp. 40-47, 2008.
[Ris09] T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, “Hey, You Get Off My Cloud! Exploring
Information Leakage in Third-Party Compute Clouds,” Proc. 16th ACM Conf. Computer and Communications
Security (CCS), 2009.
[Du+14] J. Du, D. Dean, Y. Tan, et al. Scalable Distributed Service Integrity Attestation for Software-as-a-
Service Clouds. IEEE Transactions on Parallel and Distributed System, 25(3):730-739, 2014.
[Kam03] S.D. Kamvar, M.T. Schlosser, and H. Garcia-Molina. The eigentrust algorithm for reputation
management in p2p networks. Proceedings of the 12th international conference on World Wide Web, pages 640–
651. ACM, 2003
[IBM] IBM Red Boo. Fundamentals of Grid Computing, Technical Report REDP-3613-00 2000.
[Li+13] X. Li and J. Du. Adaptive and attribute-based trust model for service-level agreement guarantee in cloud
computing. IET Information Security, 7(1): 39-50, 2013.
[TM/RM] TM/RM simulator: http://rtg.cis.upenn.edu/qtm/p2psim.php3.
{San14] Z. Sanaei, S. Abolfazli, A. Gani, et al. Heterogeneity in Mobile Cloud Computing: Taxonomy and Open
Challenges. IEEE Communication Surveys & Tutorials, 16(1): 369-392, 2014.
[Xio04] L. Xiong and L. Liu, “Peertrust: Supporting reputation-based trust for peer-to-peer electronic
communities,” IEEE Transactions on Knowledge and Data Engineering, 16(7):843–857, 2004.
[Ric03] Matthew Richardson, Rakesh Agrawal, Pedro Domingos. Trust Management for the Semantic Web. IN
PROCEEDINGS OF THE SECOND INTERNATIONAL SEMANTIC WEB CONFERENCE, 2003.
[Gol08] J. Golbeck. Weaving a web of trust. Science, 321(5896):1640–1641, 2008.
[Wan10] Y. Wang and A. Nakao. Poisonedwater: An improved approach for accurate reputation ranking in p2p
networks. Future Generation Computer Systems, 26(8):1317–1326, 2010.
[Fen10] Q. Feng, L. Liu, and Y. Dai. Vulnerabilities and countermeasures in context-aware social rating services.
ACM Transactions on Internet Technology (TOIT), 11(3), 2012.
[Son05] S. Song, K. Hwang, R. Zhou, and Y.K. Kwok. Trusted p2p transactions with fuzzy reputation
aggregation. Internet Computing, IEEE, 9(6):24–34, 2005.
[Bar13] A. Barsoum and A. Hasan. Enabling Dynamic Data and Indirect Mutual Trust for Cloud Computing
Storage Systems. IEEE Transactions on Parallel and distributed Systems, 2013.
[Haw10] Hwang, K., Li, D.: ‘Trusted cloud computing with secure resources and data coloring’, IEEE Internet
Comput., 2010, 14, (5), pp. 14–22.
[Awn12] X. Wang, L. Liu, J. Su, RLM: A General Model for Trust Representation and Aggregation. IEEE
Transaction on Service Computing, 5(1): 131–143, 2012.
[Su+13] Zhiyuan Su, Ling Liu, Mingchu Li, et al. ServiceTrust: Trust Management in Service Provision
Networks. IEEE International Conference on Services Computing (SCC), 272-279, 2013.
[Fan12] Xinxin Fan, Ling Liu, Mingchu Li and Zhiyuan Su. EigenTrust++: Attack Resilent Trust Management.
Proceedings of 8th IEEE International Conference on Collaborative Computing (CollaborateCom2012).
[Var14] V. Varadharajan, and U. Tupakula, Security as a Service Model for Cloud Environment. IEEE
Transactions on network and Service management, 11(1): 60-75, 2014.