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International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 2 February 2015, Page No. 10396-10399
A Survey of Trust Management using types of Uncertain Reasoning in
Network Environment
Kadiwala shabnam1, Asst.Prof. Swapnil R.Andhariya 2,
1
Department of Computer Science & Engineering,
Parul Institute Of Technology, Limda
kadiwalasabanam@gmail.com
2
Department of Computer Science & Engineering,
Parul Institute Of Technology, Limda
Andhariya.swapnil@gmail.com
Abstract: A unified trust management scheme that enhances the security in MANETs using uncertain reasoning is proposed. In
the proposed scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With
direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain
reasoning when the full probability model can be defined. On the other hand, with indirect observation from neighbor nodes of the
observer node, the trust value is derived using the Dempster-Shafer theory, which is another type of uncertain reasoning when the
proposition of interest can be derived by an indirect method. Performance of a routing protocol including this trust management
scheme is evaluated under attack. Trust management considers the capability of the node along with its behavior while calculating
the trust. Hence the performance of the routing protocol is improved when both behavior and capability is considered for trust
evaluation.
behavior, direct trust can be established. Second, when the
Keywords: MANETS, Trust Management, Uncertain Reasoning.
subject receives recommendations
1. INTRODUCTION
A. Mobile ad hoc network (MANET)
A Mobile ad hoc network is a collection of wireless nodes
that can dynamically be set up anywhere and anytime without
using any pre-existing network infrastructure. It is an
autonomous system in which mobile hosts connected by
wireless links are free to move randomly.[6]
B. The Concept of Trust
Trust concept is important for communication and network
protocol designers when creating trust relationships between
participating nodes. Trust is also defined as degree of belief
about other entities behavior Trust is paramount to ensure
collaborative optimization of system metrics. Define trust as
“a set of relations among entities that participate in a
protocol. Such relations are based on evidence created by
earlier interactions within protocol entities. Generally, if
interactions are faithful to a protocol, then trust is more between
such entities.”[3]
C. Trust Representation
There are two common ways to establish trust in MANETs.
First, when the subject can directly observe the agent’s
from other entities about the agent, indirect trust can be
established. [7]
Direct Trust: It is established upon observations on
whether the previous behavior interactions between the
subject and the agent are successful. That is, the notation
DAB denotes the direct trust values between node A and
Bas shown in Fig.1. [7]
A
B
.
Figure1.Direct Trust [7]
Indirect Trust: Trust can transit through third parties. For
example, if A and B have established a recommendation
trust relationship and B and Y have established a direct trust
relationship, then A can trust Y to a certain degree if B tells
A its trust opinion (i.e. recommendation) about Y. So,
indirect trust is established through trust transitivity as
shown in Figure 2. [7]
A
Kadiwala shabnam IJECS Volume 4 Issue 2 February, 2015 Page No.10396-10399
B
Y
Page 10396
Figure2.Indirect Trust [7]
D. Trust management
Trust Management identified it as a separate component
of security services in networks and clarified that Trust
management provides a unified approach for specifying and
interpreting security policies, relationships, and credentials.”
Trust management has diverse applicability in many decision
making situations including key management, intrusion
detection, authentication, access control, isolating misbehaving
nodes for effective routing, and other purposes. Trust
management includes trust establishment (i.e., collection of
appropriate trust evidence trust generation, trust distribution,
trust discovery, and evaluation of trust evidence), trust update,
and trust revocation. [4]
2. TRUST ORIENTED AD HOC NETWORK
FRAMEWORK: [10]
Trust oriented Ad hoc Framework
Trusted Ad hoc
network
Ad hoc Network
Ad hoc Network set Up
Trust Policy
Trust Model
Trusted Protocol
FIG: 3 Abstract Representation of Trust Oriented
Framework [10]
Ad
Hoc Network
3. LITERATURE SURVEY
Trust
management using, Uncertain Reasoning is a hot
research area now days. With NS2 growing of it development
and social web sites, increasing trends in people to posting online
reviews. There are different tactics for extracting the review, I
look around and survey different methods for it. Choosing the
best one out is depend upon what type of review you want to
summarized.
In[1] Zhexiong Wei, Helen Tang, F. Richard Yu, Maoyu
Wang, and Peter Mason, MANETs, including dynamic topology
and open wireless medium, lead to many security weaknesses.
Malicious node scan drop or modify packets that are received
from other nodes and reduce the reliability of networks.
Therefore, secure routing in orithms
MANETs is an important area of research. In this paper, a
scheme that enhances routing security based on trust. In order to
improve the accuracy of trust values with indirect observations,
we use Dempster-Shafer theory of evidence to fuse observers’
opinions. Consequently, a more accurate trust value can be
calculated from indirect observations. And use the trust value to
improve the MANET routing protocol security. Simulation
results indicated the effectiveness of our scheme, which
improves secure throughput and packet delivery ratio
considerably with slightly increased average end-to-end delayto
enhance the MANET routing protocol security. Simulation
results indicated the effectiveness of our scheme, which
improves secure throughput and packet delivery ratio
considerably with slightly increased average end-to-end delay.
In [8] Sonja Buchegger and Jean-Yves Le Boudec, in
this paper a robust reputation system for misbehavior detection
in mobile ad-hoc networks. Solution is based on a modified
Bayesian estimation approach which we designed. In this
approach, everyone maintains a reputation rating and a trust
rating about everyone else who is of interest. The approach is
fully distributed and no agreement is necessary. Speed up the
detection of misbehaving nodes, it is advantageous to,
cautiously, make use also of reputation records from others in
addition to first-hand observations. These records are only
considered in the case when they come from a source that has
consistently been trustworthy or when they pass the deviation test
which evaluates compatibility with one’s own reputation ratings.
Even after passing the test, they only slightly modify the
reputation rating of a node. The results of the deviation test are
additionally used to update the trust rating. It can allow for
redemption and prevent capitalizing excessively on past behavior
by two mechanisms, namely re-evaluation and fading. It has been
argued that traditional statistical approaches so far do not assume
malicious behavior reputation system uses Bayesian estimation
to specifically, address lying nodes. In this paper showed that
method is coping well with false second-hand reports, as it keeps
the number of false positives and false negatives low. Simulation
also showed that the detection of misbehaving nodes accelerates
significantly with the use of selected second-hand information.
In[2]Boyang, Ryo Yamamoto, Yoshiaki Tanaka,In this
paper, a Dempster-Shafer (D-S) evidence based trust
management strategy is proposed to conquer not only
cooperative black hole attack but also gray hole attack. Neighbor
observing model based on watchdog mechanism is used to detect
single black hole attack by focusing on the direct trust value
(DTV). Historical evidence is also taken into consideration to go
against gray hole attacks. Then, a neighbor recommendation
model companied with indirect trust value (ITV) is used to figure
out the cooperative black hole attack. D-S evidence theory is
implemented to combine ITVs from different neighbors. Some of
the neighbor nodes may declare a false ITV, which effect can
also be diminished through the proposed method. The simulation
is firstly conducted in the Matlab to evaluate the performance of
the algorithm. Then the security routing protocol is implemented
in the GloMoSim to evaluate the effectiveness of the strategy.
Both of them show good results and demonstrate the advantages
of proposed method by punishing malicious actions to prevent
Kadiwala shabnam IJECS Volume 4 Issue 2 February, 2015 Page No.10396-10399
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the camouflage and deception in the attacks. Black hole attack
and gray hole attack are discussed and two algorithms, NNOMbased DTV and NRTM-based ITV are proposed. The proposed
DTV can be used to detect the gray hole attacks in the networks.
The proposed ITV aims at the recommendation of cheating
neighbor nodes. If there is no such recommendation node or the
cheating nodes are too many, the proposed ITV may not take
effects.
In[7]Wingbow,
HuangChuanhe,
YangWenzhong,
WangTong, in this paper route and forward the data, it is
important to evaluate the individual behavior of each node in
network. Benevolent nodes can keep the network working
smoothly. Malicious nodes present in the network that try to
distort, disrupt or disturb the network traffic. the method of the
trust value computation connected with the behaviors of
communication with its one-hop neighbor nodes reliably, and
integrally, appropriately timed, they establish a trust routing
model to evaluate and maintain trust relationships from the
aspects of direct and indirect trust level to discover a most
trustworthy routing. That the proposed model and algorithm can
detect effectively malicious behaviors, bypass the malicious
nodes, so as to improve the network packet delivery fraction,
routing overhead and decrease the average end-to-end delay.
Distributed trust routing model for ad hoc networks that is highly
reliable, robust and scalable. This analyze the trust model, we
have presented a solution to solve it based on trust graph. We
implement the algorithm: an extension to AODV: IBTR in NS2.
Better performance on the packer delivery, routing overhead and
average end-to-end delay under varying the different number of
malicious nodes. This model can serve as the network layer
complement for network security solutions such as IDS schemes
or secure routing protocols for ad hoc networks.
In[5]N. Marchang,R. Datta, In this study, the authors
present a light-weight trust-based routing protocol. It is lightweight in the sense that the intrusion detection system (IDS) used
for estimating the trust that one node has for another, consumes
limited computational resource. Moreover, it uses only local
information thereby ensuring scalability. Our light-weight IDS
takes care of two kinds of attacks, namely, the black hole attack
and the grey hole attack. Whereas our proposed approach can be
incorporated in any routing protocol, the authors have used
AODV as the base routing protocol to evaluate our proposed
approach and give a performance analysis. The trust a node has
for a neighbor forms the basic building block of our trust model.
The proposed trust estimation technique, which is executed by
every node in the network independently, uses only local
information thereby making it scalable. Moreover, unlike other
techniques based on monitoring traffic that require a lot of space
and time for buffering packets and searching for a packet match,
our approach does not require such an overhead. Moreover, we
presented two variants of the proposed approach. Depending
upon one’s priority and need, a variant can be chosen.
Simulation results illustrate the effectiveness of our approach. It
is seen that the RF in LTB-AODV is less than that of AODV for
all cases The routes (most trusted) established in LTB-AODV
would potentially be longer than those (shortest routes)
established in AODV When a route is established between a
source and a destination, all the intermediate nodes in between
them have also consequently established the routes to the source
and the destination.
In[9]Meenakshi Bansal, Rachna Raj put and Gaurav
Gupta, Nodes in Wireless ad hoc network need to operate as
routers in order to maintain the information about network
connectivity as there is no centralized infrastructure. Therefore,
Routing Protocols are required which could adapt dynamically to
the changing topologies and works at low data rates. As a result,
there arises a need for the comprehensive performance
evaluation of the ad -hoc routing protocols in s a me frame work
to understand their comparative merits and suitability or
deployment in different scenarios. In this paper the protocols
suite selected for comparison are AODV, DSR, TORA and
OLSR ad- hoc routing protocols, as these were the most
promising from all other protocols. The performance of these
protocols is evaluated through exhaustive simulation sing the
OPNET Modeler network simulator under. different parameter s
like routing overhead, delay, throughput and network load under
varying the mobile nodes.
Routing Overhead
Ad hoc networks are designed to be scalable. As the
network grows, various routing protocols perform differently.
The amount of routing traffic increases as the
network grows. An important measure of the scalability of the
protocol, and thus the network, is its routing overhead. It is
defined as the total number of routing packets transmitted over
the network, expressed in bits per second or packets per second.
Packet End-to-End Delay
The packet end-to-end delay is the average time that
packets take to traverse the network. This is the time from the
generation of the packet by the sender up to their reception at the
destination’s application layer and is expressed in seconds. It
therefore includes all the delays in the network such as buffer
queues, transmission time and delays induced by routing
activities and MAC control exchanges. The delay is also affected
by high rate of CBR packets. The buffers become full much
quicker, so the packets have to stay in the buffers a much longer
period of time before they are sent.
Throughput
The amount of throughput in all cases is highest for
OLSR as compared with other protocols as routing paths are
readily available for the data to be sent from source to
destination. The amount of throughput for TORA is higher at
start from AODV and DSR in case of 10 and 30 nodes but it fall
below AODV throughput curve as the nodes start moving.
AODV performs better in network with relatively high number
of traffic sources and higher mobility. The DSRs throughput is
very low in the network in all the cases.
4. CONCLUSION
After studying some papers related to trust management for
uncertain reasoning, it is very help full in the field of MANET.
Evaluate the trust values of observed nodes in MANETs.
Misbehaviors such as dropping or modifying packets can be
Kadiwala shabnam IJECS Volume 4 Issue 2 February, 2015 Page No.10396-10399
Page 10398
detected in scheme through trust values by direct and indirect
observation. Nodes with low trust values will be excluded by the
routing algorithm. The main goal of more accurate trust can be
obtained by considering different types of packets, indirect
observation from one-hop neighbors and other important factors
such as buffers of queues and states of wireless connections,
which may cause dropping packets in friendly nodes. Improve
the throughput, packet delivery ratio, delay and Routing load.
REFERENCES
[1] Zhexiong Wei, Helen Tang, F. Richard Yu, Maoyu Wang,
and Peter Mason”Trust Establishment with Data Fuion for
Secure Routing in MANETs”, IEEE ICC 2014.
Kadiwala shabnam received the B.E degree in Computer
Science and Engineering from Shri S’ vidya Mandal Institute of
Technology in 2013. She is receiving M.E. degree in Computer
Science and Engineering from Parul Institute Of Technology,
Gujarat Technological University,Ahmedabad
[2] BOYANG,
Ryo
YAMAMOTO,
Yoshiaki
TANAKA,”Dempster-Shafer Evidence Theory Based Trust
Management Strategy against Cooperative Black Hole
Attacks and Gray Hole Attacks in MANETs”, ICAC,2013.
[3] Menaka, Dr. V. Ranganathan “A Survey of Trust related
Routing Protocols for Mobile Ad Hoc Networks”,R.,
IJETAE.com, April 2013
[4] Jin-Hee Cho, Member, Ananthram Swami, Fellow,and IngRay Chen, Member, “A Survey on Trust Management for
Mobile Ad Hoc Networks”, IEEE,2011.
[5] N.Marchang,R. Datta, “Light-weight trust-based routing
protocol for mobile ad hoc networks”, IETDL.org,2010.
[6] sanket.b.radder
networks”,2010
“Challenges
in
mobile
ad-hoc
[7] Huang Chuanhe, Yang Wenzhong, WangTong, ”An
Individual Behavior-based Trust Routing Model for Ad hoc
Networks” Wingbow, IEEE 2009.
[8] S. Buchegger and J.-Y. L. Boudec, “A robust reputation
system for P2P and mobile ad-hoc networks,” in Proc. 2nd
Workshop on the Economics of Peer-to-Peer Systems,
(Bologna, Italy), Nov. 2004
[9] S. Corson and J. Macker, “Mobile ad hoc networking
(MANET): routing protocol performance issues and
evaluation considerations,” IETF RFC 2501, Jan. 1999.
[10] Amandeep Verma and Manpreet Singh Gujral,”Trust
Oriented Security Framework For Ad Hoc Network”, pp.
19–26, © CS & IT-CSCP 2012.
Author Profile
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