International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
An Empirical Trust Computation Method With
Integrated Load Balancing Factor In P2P Networks
Pallae Raja Sree Marthanda Kulapathy1, ,Vegi Srinivas2
1
M.Tech Scholar,2 Associate Professor
1,2
Dept of CSE, Dadi Institute of Engineering & Technology.
Abstract:- For the recent developments in the scalable
networks, security and privacy are the most important factors
like p2p networks. Most of our approaches works on statistical
measures like trust computations, (i.e. Direct and indirect trust
computations), these approaches are not optimal, because
anonymous user may not have (or use) same set of
characteristics as previous connection. In this paper a proposal
to an efficient and empirical trust computation method for
identifying and allowing the nodes based on trust measures was
done. Here, we are proposing an improved trust computation
method by enhancing the traditional trust computation method
with balancing factor and the results are shown at the last for
the efficiency of proposed system.
I. INTRODUCTION
Peer-to-peer (P2P) electronic commerce (E-Commerce)
communities can be seen as truly distributed computing
applications in which peers (members) communicate directly
with one another to exchange information, distribute tasks, or
execute transactions. P2P E-Commerce communities can be
implemented either on top of a P2P network [1, 2, 3] or using
a conventional client-server platform. Gnutella is an example
of P2P E-Commerce communities that are built on top of a
P2P computing platform. Person-to-person online auction
sites such as eBay and many business-to-business (B2B)
services such as supply-chain-management networks are
examples of P2P communities built on top of a client server
computing architecture.
Reputation Systems:
In E-Commerce settings P2P communities are often
established dynamically with peers that are unrelated and
unknown to each other. Peers of such communities have to
manage the risk involved with the transactions without prior
experience and knowledge about each other’s reputation. One
way to address this uncertainty problem is to develop
strategies for establishing trust and develop systems that can
assist peers in accessing the level of trust they should place
on an E-Commerce transaction. For example, in a buyerseller market, buyers are vulnerable to risks because of
potential incomplete or distorted information provided by
sellers[4][5]. Trust is critical in such electronic markets as it
can provide buyers with high expectations of satisfying
exchange relationships. A recent study [9] reported results
from both an online experiment and an online auction
ISSN: 2231-5381
market, which confirmed that trust can mitigate information
asymmetry (the difference between the amountsof
information the two transacting parties possess) by reducing
transaction-specific risks, therefore generating price
premiums for reputable sellers.
Recognizing the importance of trust in such
communities, an immediate question to ask is how to build
trust. There is an extensive amount of research focused on
building trust for electronic markets through trusted third
parties or intermediaries [14, 8]. However, it is not applicable
to P2P e-Commerce communities where peers are equal in
their roles and there are no entities that can serve as trusted
third parties or intermediaries. Reputation systems [11]
provide a way for building trust through social control
without trusted third parties. Most research on reputationbased trust utilizes information such as community-based
feedbacks about past experiences of peers to help making
recommendation and judgment on quality and reliability of
the transactions. Community based feedbacks are often
simple aggregations of positive and negative feedbacks that
peers have received for the transactions they have performed
and cannot accurately capture the trustworthiness of peers. In
addition, peers can misbehave in a number of ways, such as
providing false feedbacks on other peers.
The challenge of building a trust mechanism is how to
effectively cope with such malicious behavior of peers.
Another challenge is that trust context varies from
communities to communities and from transactions to
transactions. It is important to build a reputation based
system that is able to adapt to different communities and
different situations. Furthermore, there is also a need for
experimental evaluation methods of a given trust model in
terms of the effectiveness and benefits. Most traditional trust
models only give an analytical model without any
experimental validation due to the subjective nature of trust.
There is a need of general metrics for evaluating the
effectiveness and benefits of trust mechanisms. With these
research problems in mind, we develop Peer Trust, a peer-topeer trust model for quantifying and assessing the
trustworthiness of peers in P2P E-Commerce communities.
Our goal is to build a general trust metric that provides an
effective measure for capturing the trustworthiness of peers,
addresses the fake or misleading feedbacks, and has the
capability to adapt to different communities and situations.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
II. TRADITIONAL TRUST MEASURES
A) Interaction trust
Interaction trust (IT) models the trust that ensues from the
direct interactions between two agents. Here we simply
exploit the direct trust component of Regret [10] since this
meets all our requirements for dealing with direct
experiences. In more detail, consider a commercial
transaction where agent a buys a particular product from
agent b. The outcome of the transaction may consist of the
product price, product quality, and the delivery date. From
this outcome, agent a may give ratings about agent b’s
service in terms of price, quality, and delivery for that
particular interaction.
Ratings are thus tuples in the following form:
r = (a, b, I, c, v); where a and b are the agents that
participated in the interaction i, and v is the rating a gave b
for the term c (e.g. price, quality, delivery). The range of v
is[¡1; +1], where 1 means absolutely negative, +1 means
absolutely positive, and 0 means neutral or uncertain. In
order to calculate IT from past experiences, an agent needs to
record its past ratings in a (local) rating database. This
database stores at maximum the H latest ratings that the agent
gave to the each partner that it has interacted with. Here H is
called the local rating history size. When calculating the IT
value for agent b with respect to term c, agent a has to query
its database for all the ratings that have the form (a, b, c ),
where the ‘ ’ symbol can be replaced by any value.
We call the set of those ratings RL(a, b, c). Then the IT
(denoted by TI ) is calculated as the weighted mean of the
rating values of all the ratings in the set:
TI (a, b, c) =Xri2RL(a, b, c)(ri) ¢ vi; (1)where vi is the value
of the rating ri and (ri) is the weight corresponding to ri. The
weight (ri) for each rating is selected such that it gives more
weight to more recent ratings, with a constraint that Pri2RL(a
,b, c) (ri) = 1. This is to ensure that the trust value TI (a, b, c)
is in the range[1,+1].
B) Role Based Trust
Role-based trust (RT) models the trust resulting from the
role-based relationships between two agents (e.g. owned by
the same company, a service provider and its registered user,
friendship relationship of their owners). Since there is no
general method for computationally quantifying trust based
on this type of relationship, we use rules to assign RT values.
This means end users can add new rules to customize this
component to suit their particular applications. Rules are
tuples of the following form :rul= (role a, role b, c, vD , eD),
which describes a rule that if role a and role b are the roles of
agent a and b respectively, then the expected performance of
b in an interaction with ais vD(vD 2 [1, 1]) with respect to the
term c; eD 2 [0, 1]
is the default level of influence of this rule on the resulting
RT value.
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For example, possible rules may be:rul1 = (buyer; seller;
quality;0:2,0:3);rul2 = (friend-buyer; friend-seller; quality;
0;,0:6), andrul3 = ( ; government-seller; quality; 0, 0:9):
rul1 expresses an agent’s belief that an ordinary seller will
usually sell a product of slightly lower quality than agreed,
but the reliability of this belief is low (0:3); rul2 is the belief
that in a close partnership the buying agent can expect the
seller to do what is agreed in terms of product quality; and
this is also true for a governmental seller almost all of the
time (rul3).Each agent has its own set of rules which are
stored in a (local) rule database. In order to determine the RT
with an agent b, agent a looks up the relevant rules from its
rule database. Then the value of RT is given by the following
formula:
TR(a, b, c) =Pruli2Rules(a;b;c) eDi ¢ vDiPruli2Rules(A;B;c) eDi;
(5)where ruli= (role a; role b; c; vDi; eDi) is a rule in the set of
rules Rules(A;B; c). This set is a subset of the rule database
in which only the rules that are relevant to the roles of a, the
roles of b, and the term care selected.
Since the rules for RT are specified by the agent’s owner, the
reliability of RT also needs to be set by the agent’s
Owner. We use TR(a; b; c), again in the range [0, 1], to
denote this value. A reliability measure that can be used for
role-based trust is:
TR(a; b; c) =Pruli2Rules(a;b;c) eDijRules(a, b, c)j; (6)where
jRules(a, b, c)j is the cardinality of the set of rules.
C) Certified reputation
Certified reputation(CR) are ratings presented by the rated
agent (agent b) about itself which have been obtained from its
partners in past interactions [6]. These ratings are
certifications(provided by the rating agents) of agent b’s past
performance (somewhat like a reference when applying fora
job). They allow an agent to prove its achievable
performance as viewed by previous interaction partners2.
Since agent b can choose which ratings it puts forward, a
rational agent will only present its best ratings. Therefore, we
should assume that CR information probably overestimates
an agent’s expected behaviour. Thus, although it cannot
guarantee agent b’s performance in future interactions, the
CR information does reveal a partial perspective on agentb’s
past behavior. The main benefit of this type of information is
its high availability. With the cooperation of its partners,
agent b can have CR information from just a small lnumber
of interactions. Therefore, CR is available to agents in most
circumstances; even in situations where the other components
may fail to provide a trust measure.
Now a day’s security and privacy issues hav ebecome
critically important with the fast expansion of multi agent
systems(MAS).Most network applications such as grid
computing , and p2p n/w ‘s can be viewed as MAS which are
open, anonymous, and dynamic in nature. Such
characteristics of MAS introduce vulnerabilities and threats
to providing secured communication when malicious agents
appear. One feasible way to minimize the threats is to
evaluate the trust and reputation of the interacting agents
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
Most trust/computation models works on statistical
measures(i.e. Direct and indirect trust computations)which
are not optimal to address this issue. So, to cope with the
strategically altering behavior of malicious agents; we
present in this paper an efficient trust computation method,
which is based on classification for analyzing the nodes or
user when connects to the network.
D) Trust Computation with Load Balancing Factor
In this paper we are proposing an efficient trust
computation method with load balancing factor, when nodes
or agents tries to connect with service providers, connection
needs to optimize the optimal service providers, for trust
worthy various mechanisms implemented as trust measures
for measuring the trust of the service providers. Even though
trust measure is the basic measure we need to consider the
load balancing factor, because even though service provider
is good, if load is high, agent can not choose as its service
provider because it may not leads to optimal results.
Now a day’s identifying the unauthorized user in
network is still an important research issue during the peer to
peer connections. Networks are protected using many
firewalls and encryption software’s. But many of them are
not sufficient and effective. Most trust computation systems
for mobile ad hoc networks are focusing on either routing
protocols or its efficiency, but it fails to address the security
issues. Some of the nodes may be selfish, for example, by not
forwarding the packets to the destination, thereby saving the
battery power. Some others may act malicious by launching
security attacks like denial of service or hack the information.
The ultimate goal of the security solutions for wireless
networks is to provide security services, such as
authentication, confidentiality, integrity, anonymity, and
availability, to mobile users.
This paper incorporates agents and data mining
techniques to prevent anomaly intrusion in mobile adhoc
networks. Home agents present in each system collect the
data from its own system and using data mining techniques to
observe the local anomalies. The Mobile agents monitoring
the neighboring nodes and collect the information from
neighboring home agents to determine the correlation among
the observed anomalous patterns before it will send the data.
This system was able to stop all of the successful attacks in
an adhoc networks and reduce the false alarm positives.
We define transaction success rate as a metric to
measure the productivity and security level of a community.
A transaction is considered successful if both of the
participating peers cooperate. Otherwise one or both of the
peers is faced with the risk of malicious behaviors from the
other peer. The successful transaction rate is defined as a
ratio of the number of successful transactions over the total
number of transactions in the community up to a certain time.
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A community with a higher transaction success rate has a
higher productivity and a stronger level of security. We
expect that a community with an effective trust mechanism
should have a higher transaction success rate as peers are able
to make informed trust decisions and avoid unreliable and
dishonest peers.
Trust Computation Model (TCM) is based on a set
of required factors to be known prior to making a trust
decision [10]. These factors include information about other
agents’ knowledge base, and interactions during the current
task. A key feature of this model is that it considers how
much knowledge one should have about the trustee agents in
order to make a trust decision. TCM model defines direct
trust based on three concepts: familiarity, similarity and past
experience, whereas indirect trust is defined based on
recommendations.
Overall trust metric can be calculated with expected
trust(i.e Expected trust reflects expected performance of the
target agent and it is deduced from both recent and historical
trust.) and deviation reliability.(i.e Deviation reliability is a
measure of how much deviation we are willing to tolerate.
Malicious agents sometimes strategically oscillate between
raising and milking their reputation which seriously affects
the performance of the network),coming to the load
balancing factor, we are enhancing the previous load
balancing factor with additional trust measures as by
integrating the transaction trust and again recursively
calculate the distance again if it is minimum then lamda. The
following algorithm shows the load balancing factor with
enhances trust measure as follows.
III. LOAD BALANCING FACTOR
Algorithm 1:
Selection of service providing agent(p,S)
Input: Evaluating agent p and the set of agents responding to
a service request S
Output: Service providing agent q.
For each x € S do
Trust(p,x) = ET(p,x)*DR(p,x)
If(Trust(p,x)<¥) then
Trust(p,x)
=
ET(P,x)*DR(p,x)+TransTrust(p,x).
If(Trust(p,x)<¥) then
UUu {x}
Else
GG u {x}
End if
Else
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
End if
End for
If G ≠ Ø then
For each x € G do
N(p,x)= I(p,x)+∑ FC(p,y)*I(y,x).
End for
Sort G in increasing order of lad N
return agent q with the smallest load N
else
Total_trust0
For each x € U do
Total_trustTotal_trust+ Trust(p,x)
End for
If Total_trust>0 then
For each x € U do
Compute Prob(p,x)
End for
return agent q with probability Prob(p,q)
End if
End if
IV. RESULTS
Our experimental results show the efficient performance
than the traditional trust computation with previous load
balancing factor. Performance evaluation of previous
traditional and our proposed approach as follows:
5
4
3
2
1
0
Time
Load
Trust
measures
TA with TA with PA with
out LB LB Factor LB Factor
Factor
TA-Traditional approach
PA-Proposed approach
LB-Load Balance
V. CONCLUSION
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We are concluding our proposed work by enhancing the load
balancing factor with integrated trust computation for
selection of the optimal service provider with balancing load
factor and Secured Trust can ensure secured communication
among agents by effectively detecting strategic behaviors of
malicious agents.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
BIOGRAPHIES
Pallae Raja Sree Marthanda KulapathY is
Pursuing M.Tech (CSE) from DIET
College (JNTU Kakinada) and he
obtained B.E.(Electrical & Electronics
Engg) with Distinction from S.T.J.I.T.
College
Dharwad
Dist,Karnataka.(Karnatak University).India.
Vegi Srinivas received his M.Sc.
and M.Tech.in Computer
Science
and
Technologyfrom the Dept.
of Computer
Science
and
Systems Engineering,
College
of
Engineering, Andhra University. He is
currently doctoral candidate in the
Department ofComputer Science & Engineering, JNTU,
Kakinada, India. He is working as Associate Professor in the
Dept. of Computer Science and Engineering, Dadi
Institute of
Engineering
and
Technology,
Anakapalli, Visakhapatnam, India. His main areas of
interests are Security, Privacy, Trust and Cloud Computing.
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