Iaetsd organizing the trust model in peer-to-peer system using

Organizing the Trust Model in Peer-To-Peer System Using
SORT
R. Kiruthikalatha, B. Arputhamary M.C.A., MPhil,
M.Phil scholar, Department of computer science, Bishop Heber college(Autonomous),
Trichirapalli-620 017
Asst.professor, Department of computer Applications, Bishop Heber college(Autonomous),
Trichirapalli-620017
kiruthikalatha@gmail.com
arputhambaskaran@rediffmail.com
ABSTRACT
Peer-to-Peer (P2P) Systems have
attracted significant interest in recent years.
In P2P networks, each peer acts as both a
server and client. This characteristic makes
peers vulnerable to a wide variety of attacks.
Having robust trust management is very
critical for such open environment to
exclude unreliable peer from the system.
Managing trust is a problem in peer to peer
environment, so our SORT model is built
based on trust metrics. The trust metrics
such as reputation, service trust,
recommendation trust are defined to
precisely measure trustworthiness of peers.
A peer’s trustworthiness is evaluated by
considering provided services and given
recommendations with service and
recommendation contexts. When evaluating
recommendation, recommender’s
trustworthiness and confidence level about
the information provided are also
considered. Experiments on file sharing
application demonstrate that peers with the
highest trust value are considered and build
the trust model in their contiguity and
insulate malignant peers. Building trust
relationships among peers can mitigate
attacks of malicious peers. Peers create their
own trust network in their proximity by
using local information available and global
trust information.
Keywords: Peer-to-peer systems, trust
management, reputation, services, security.
I. INTRODUCTION
PEER-TO-PEER (P2P) systems rely
on collaboration of peers to accomplish
tasks. Ease of performing malicious activity
is a threat for security of P2P systems.
Creating long-term trust relationships among
peers can provide a more secure envi-
ronment by reducing risk and uncertainty in
future P2P interactions. However,
establishing trust in an unknown entity is
difficult in such a malicious environment.
Furthermore, trust is a social concept and
hard to measure with numerical values.
Metrics are needed to represent trust in
computational models. Classifying peers as
either trustworthy or untrustworthy is not
sufficient in most cases. Metrics should have
precision so peers can be ranked according
to trustworthiness. Interactions and
feedbacks of peers provide information to
measure trust among peers. Interactions with
a peer provide certain information about the
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peer but feedbacks might contain deceptive
information. This makes assessment of trust-
worthiness a challenge.
In the presence of an authority, a
central server is a preferred way to store and
manage trust information, e.g., eBay. The
central server securely stores trust
information and defines trust metrics. Since
there is no central server in most P2P sys-
tems, peers organize themselves to store and
manage trust information about each other
[1], [2]. Management of trust information is
dependent to the structure of P2P network.
In distributed hash table (DHT)-based
approaches, each peer becomes a trust
holder by storing feedbacks about other
peers [1], [3], [4]. Global trust information
stored by trust holders can be accessed
through DHT efficiently. In unstructured
networks, each peer stores trust information
about peers in its neighbor-hood or peers
interacted in the past. A peer sends trust
queries to learn trust information of other
peers. A trust query is either flooded to the
network or sent to neighborhood of the
query initiator. Generally, calculated trust
information is not global and does not
reflect opinions of all peers.
We propose a Self-Organizing Trust
model (SORT) that aims to decrease
malicious activity in a P2P system by
establishing trust relations among peers in
their proximity. No a priori information or a
trusted peer is used to leverage trust
establishment. Peers do not try to collect
trust information from all peers. Each peer
develops its own local view of trust about
the peers interacted in the past. In this way,
good peers form dynamic trust groups in
their proximity and can isolate malicious
peers. Since peers generally tend to interact
with a small set of peers [7], forming trust
relations in proximity of peers helps to
mitigate attacks in a P2P system.
In SORT, peers are assumed to be
strangers to each other at the beginning. A
peer becomes an acquaintance of another
peer after providing a service, e.g.,
uploading a file. If a peer has no
acquaintance, it chooses to trust strangers.
An acquaintance is always preferred over a
stranger if they are equally trustworthy.
Using a service of a peer is an interaction,
which is evaluated based on weight
(importance) and recentness of the
interaction, and satisfaction of the requester.
An acquaintance’s feedback about a peer,
recommendation, is evaluated based on
recommender’s trust-worthiness. It contains
the recommender’s own experience about
the peer, information collected from the
recommender’s acquaintances, and the
recommender’s level of confidence in the
recommendation. If the level of confidence
is low, the recommendation has a low value
in evaluation and affects less the trustworthi-
ness of the recommender. and defines two
contexts of trust: service and recommen-
dation contexts. Information about past
interactions and recommendations are stored
in separate histories to assess competence
and integrity of acquaintances in these
contexts. SORT defines three trust metrics.
Reputation metric is calculated based on
recommendations. It is important when
deciding about strangers and new
acquaintances. Reputation loses its
importance as experience with an
acquaintance increases. Service trust and
recommendation trust are primary metrics to
measure trustworthiness in the service and
recommendation contexts, respectively. The
service trust metric is used when selecting
service providers. The recommendation trust
metric is important when requesting
recommendations. When calculating the
reputation metric, recommendations are
evaluated based on the recommendation
trust metric. We implemented a P2P file
sharing simulation tool and conducted
experiments to understand impact of SORT
in mitigating attacks. Parameters related to
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peer capabilities (bandwidth, number of
shared files), peer behavior (online/ offline
periods, waiting time for sessions), and
resource distribution (file sizes, popularity
of files) are approximated to several em-
pirical results. This enabled us to make more
realistic observations on evolution of trust
relationships. We studied 16 types of
malicious peer behaviors, which perform
both service and recommendation-based
attacks. SORT mitigated service-based
attacks in all cases. Recommendation-based
attacks were contained except when
malicious peers are in large numbers, e.g.,
50 percent of all peers. Experiments on
SORT show that good peers can defend
themselves against malicious peers without
having global trust information. SORT’s
trust metrics let a peer assess trustworthiness
of other peers based on local information.
Service and recommendation contexts
enable better measurement of
trustworthiness in providing services and
giving recommendations. Outline of the
paper is as follows: Section 2 discusses the
related research. Section 3 explains the
computational model of SORT. Section 4
presents the simulation experiments and
results. Section 5 summarizes the results and
possible future work directions.
Fig 1. Peer-to-Peer Model
II. RELATED WORK
[1] Run fang Zhou, Kai Hwang.,[R
Zhou, Kai Hw,06] describes the
trustworthiness of participating peers and to
combat the selfish, dishonest, and malicious
peer behaviors.
[2] Guruprasad Khataniar, Hitesh
Tahbildar, and Prakriti Prava Das [Guru, Hit
Tah, PravDa, 13] describes There are
various overlay structures that provide
efficient and scalable solutions for point and
range query in a peer-to peer network.
[3] Karl Aberer, Philippe Cudré-
Mauroux, Anwitaman Datta, Zoran
Despotovic, Manfred Hauswirth, Magdalena
Punceva, Roman Schmidt.[Mag Pun, Zor,
Anw Datt, 12] describes, sometimes p2p
system structure are not in uniform our
concept is used to organizing the p2p system
structure in self.
[4] Kevin Hoffman, David Zage,
Cristina Nita-Rotaru.[Ke Hoff, Cris Nit Dav
Za, 07] describes, The rapid growth of
communication networks such as the
Internet and ad hoc wireless mesh networks
has spurred the development of numerous
collaborative applications.
[5] Stephen Boyd Arpita Ghosh
Balaji Prabhakar Devavrat Shah.[ApriGho,
Pra Dev, 06] Motivated by applications to
sensor, peer-to-peer and ad hoc networks,
we study distributed algorithms, also known
as gossip algorithms, for exchanging
information and for computing in an
arbitrarily connected network of nodes. The
topology of such networks changes
continuously as new nodes join and old
nodes leave the network.
[6] Sylvia Ratnasamy, Paul Francis,
Mark Handley, Richard Karp1, Scott
Shenker [Sy Pat,Paul, Mk Han, 01]. Hash
tables – which map “keys” onto “values” –
are an essential building block in modern
software systems. We believe a similar
functionality would be equally valuable to
large distributed systems.
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[7] Yao Wang[Ya Wan, 05 ]. Peer-
to-peer networks are networks in which all
peers cooperate with each other to perform
article function in a decentralized manner
All peers are both users and providers of
resources and can access each other directly
without intermediary agents. Compared with
a centralized system, a peer-to-peer (P2P)
system provides an easy way to aggregate
large amounts of resource residing on the
edge of Internet or in ad-hoc networks with
a low cost of system maintenance.
[8] Jon Kleinberg[Jo Kl, 09]. A social
network exhibits the small-world
phenomenon if, roughly speaking, any two
individuals in the network are likely to be
connected through a short sequence of
intermediate acquaintances.
[9] Runfang Zhou, Kai Hwang[R Zhou, Kai
Hw, 07]. Peer-to-Peer (P2P) reputation
systems are needed to evaluate the
trustworthiness of participating peers and to
combat selfish and malicious peer behaviors.
The reputation system collects locally
generated peer feedbacks and aggregates
them to yield global reputation scores.
Development of decentralized reputation
system is in great demand for unstructured
P2P networks since most P2P applications
on the Internet are unstructured.
[10] Ion Stoica, Robert Morris, David
Liben-Nowell, David R. Karger, M. Frans
Kaashoek, Frank Dabek , Hari Balakrishnan
[ I stoica, R Morris, D Karger, Fr Kaashoek
,F Dabek, 01]. Peer to Peer Systems are
loosley organized systems without any
centralized control or any hierarchical
system. Each node runs software with
equivalent functionality. Peer to peer
systems provide users with features such as
availability, permanence, redundant
storage, selection of nearby servers,
anonymity.
[11] Shanshan Song, Kai Hwang,
and Runfang Zhou., [S Song, K Hwang, R
Zhou, 05]. System uses fuzzy logic
inference rules to calculate local trust scores
and to aggregate global reputation. This
system benefits from the distinct advantages
of fuzzy inferences, which can handle
imprecise linguistic terms effectively. In the
“Fuzzy Logic Inference and Applications”
sidebar, we give details on how to perform
fuzzy inference in the Fuzzy Trust system.
III. SYSTEM MODEL
SORT defines three trust metrics.
Reputation metric is calculated based on
recommendations. It is important when
deciding about strangers and new
acquaintance increases. Service trust and
recommendation trust are primary metrics to
measure trustworthiness in the service and
recommendation contexts, respectively. The
service trust metric is used when selecting
service providers. The recommendation trust
metric is important when requesting
recommendations. When calculating the
reputation metric, recommendations are
evaluated based on the recommendation
trust metric. We implemented a P2P file
sharing simulation tool and conducted
experiments to understand impact of SORT
in mitigating attacks. Parameters related to
peer capabilities (bandwidth, number of
shared files), peer behavior (online/ offline
periods, waiting time for sessions), and
resource distribution (file sizes, popularity
of files) are approximated to several
empirical results. This enabled us to make
more realistic observations on evolution of
trust relationships. We studied 16 types of
222
ISBN: 378-26-138420-01

malicious peer behaviors, which perform
both service and recommendation- based
attacks. SORT mitigated service-based
attacks in all cases. Recommendation-based
attacks were contained except when
malicious peers are in large numbers, e.g.,
50 percent of all peers. Experiments on
SORT show that good peers can defend
themselves against malicious peers without
having global trust information. SORT’s
trust metrics let a peer assess trustworthiness
of other peers based on local information.
Service and recommendation contexts
enable better measurement of
trustworthiness in providing services and
giving recommendations.
Fig. 3 Architecture Flow Process
A. Trust model for Peer-To-Peer
In Proposed SORT reputation based
metric has been used. After calculating
value, pi updates recommendation many
important files are shared with peer to peer
system. The Central server makes the
trustiness between the peer systems. In
many cases there is no central server in the
peer to peer system. Many possibilities
hacking of information. Self organized
based peer systems provide the better
security among the number of system. on
trust values of recommenders based on
accuracy of their recommendations. In
SORT method trust is based on the
service and recommendation .There are
three metrics are used for peer to peer
system. Reputation metric is calculated
based on recommendations. It is important
when deciding about strangers and new
acquaintance increased. Different types
attacks prevented in this method. When pi
searches for a particular service, it gets a list
of service providers. Considering a file
sharing application, pi may download a file
from either one or multiple up loaders. With
multiple up loaders, checking integrity is a
problem since any file part downloaded
from an up loader might be inauthentic.
When evaluating an acquaintance’s
trustworthiness in the service context, a peer
first calculates competence and integrity
belief values using the information in its
service history.
IV.PERFORMANCE EVALUATION
A file sharing simulation program is
implemented in Java to observe results of
using SORT in a P2P environment. Some
questions studied in the experiments are as
follows: how SORT handles attacks, how
much attacks can be mitigated, how much
recommendations are (not) helpful in
correctly identifying malicious peers, and
what type of attackers are the most harmful.
A. Method
The simulation runs as cycles. Each
cycle represents a period of time.
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Downloading a file is an interaction. A peer
sharing files is called an uploader.
B. Attacker Model
Attackers can perform service-based
and recommendation based attacks.
Uploading a virus infected or an inauthentic
file is a service-based attack. Giving a
misleading recommendation intentionally is
a recommendation-based attack.
C. Analysis on Individual Attackers
This section explains the results of
experiments on individual attackers. For
each type of individual attacker, two
separate network topologies are created: one
with 10 percent malicious and one with 50
percent malicious. Each network topology is
tested with four trust calculation methods. In
the experiments, a hypocritical attacker
behaves malicious in 20 percent of all
interactions. A discriminatory attacker
selects 10 percent of all peers as victims.
D. Analysis on Individual
Pseudospoofers
This section explains the results of
experiments on individual pseudospoofers.
Pseudospoofers change their pseudonyms
after every 1,000 cycles. For other
parameters, attackers behave as described.
E. Analysis on Collaborators
Collaboration of attackers generally
makes attack prevention harder. This section
presents experimental results on
collaborators. Collaborators form teams of
size 50 and launch attacks as teams. We first
tried teams of size 10 but this was not
enough to benefit from collaboration and
results were close to individual attackers.
Hence team size is increased to observe
effects of collaboration better.
V. CONCLUSION AND FUTURE
WORK
A. CONCLUSION
Trust model for P2P networks is
presented, in which a peer can develop a
trust network in its proximity. A peer can
isolate malicious peers around itself as it
develops trust relationships with good peers.
Two context of trust, service and
recommendation contexts, are defined to
measure capabilities of peers in providing
services and giving recommendations.
Interactions and recommendations are
considered with satisfaction, weight, and
fading effect parameters. A recommendation
contains the recommender’s own
experience, information from its
acquaintances, and level of confidence in the
recommendation. These parameters
provided us a better assessment of
trustworthiness. Individual, collaborative,
and pseudonym changing attackers are
studied in the experiments. Damage of
collaboration and pseudo spoofing is
dependent to attack behavior. Although
recommendations are important in
hypocritical discriminatory attackers.
Attacks in most experiments. However, in
extremely malicious environments such as a
50 percent malicious network, collaborators
can continue to disseminate large amount of
misleading recommendations. Another issue
about SORT is maintaining trust all over the
network. If a peer changes its point of
attachment to the network, it might lose a
part of its trust network. These issues might
be studied as a future work to extend the
trust model. Using trust information does
not solve all security problems in P2P
systems but can enhance security and
effectiveness of systems. If interactions are
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modeled correctly, SORT can be adapted to
various P2P applications, e.g., CPU sharing,
storage networks, and P2P gaming. Defining
application specific context of trust and
related metrics can help to assess
trustworthiness in various tasks.
B. Future Work
Managing trust is a problem of
particular importance in peer-to-peer
environments where one frequently
encounters unknown agents. We present an
approach that addresses the problem of
reputation-based trust management at both
th data management and the scientific level.
We employ at both levels scalable data
structures and algorithm that require no
central control and allows assessing trust by
computing any agents reputation from its
former interactions with other agents. Thus
the method can be implemented in a p2p
environment and scales well for very large
number of participants. We expect that
scalable methods for trust management are
an important factor, if fully decentralized
peer-to-peer systems should become the
platform for more serious applications than
simple exchange.
Reference
[1] A Robust and Scalable Reputation
System for Trusted Peer-to-Peer
Computing: Run fang Zhou, Kai Hwang.,[R
Zhou, Kai Hw,06]
[2] Attacks and Counter Measures in BST
Overlay Structure of Peer-To-Peer
System: Guruprasad Khataniar, Hitesh
Tahbildar, and Prakriti Prava Das [Guru, Hit
Tah, PravDa, 13]
[3] P-Grid: Self-organizing Structured
P2P System: Karl Aberer, Philippe Cudré-
Mauroux, Anwitaman Datta, Zoran
Despotovic, Manfred Hauswirth, Magdalena
Punceva, Roman Schmidt.[Mag Pun, Zor,
Anw Datt, 12]
[4] A Survey of attacks on Reputation
Systems: Kevin Hoffman, David Zage,
Cristina Nita-Rotaru.[Ke Hoff, Cris Nit Dav
Za, 07]
[5] Randomized Gossip Algorithms:
Stephen Boyd Arpita Ghosh Balaji
Prabhakar Devavrat Shah.[ApriGho, Pra
Dev, 06].
[6] A Scalable Content-Addressable
Network: Sylvia Ratnasamy, Paul Francis,
Mark Handley, Richard Karp1, Scott
Shenker [Sy Pat,Paul, Mk Han, 01].
[7] Bayesian Network Trust Model in
Peer-to-Peer Networks: Yao Wang[Ya
Wan, 05 ].
[8] The Small-World Phenomenon: An
Algorithmic Perspective Jon Kleinberg[Jo
Kl, 09].
[9] Gossip-based Reputation Aggregation
for Unstructured Peer-to-Peer Networks.
Runfang Zhou, Kai Hwang[R Zhou, Kai
Hw, 07].
[10] A scalable peer-to-peer look-up
protocol for internet applications Ion
Stoica, Robert Morris, David Liben-Nowell,
David R. Karger, M. Frans Kaashoek, Frank
Dabek , Hari Balakrishnan [ I stoica, R
Morris, D Karger, Fr Kaashoek ,F Dabek,
01].
[11] Trusted P2P Transactions with
Fuzzy Reputation Aggregation: Shanshan
Song, Kai Hwang, and Runfang Zhou., [S
Song, K Hwang, R Zhou, 05].
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