Trust Management Scheme in MANET using Uncertain Reasoning and Fuzzy Logic in Trust Model

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 2 | Issue 02 | July 2015 ISSN (online): 2349-6010

All rights reserved by www.ijirst.org 268

Trust Management Scheme in MANET using

Uncertain Reasoning and Fuzzy Logic in Trust

Model

Soorya Sekhar C Meji Jose

Student Assistant Professor

Department of Computer Science and Engineering Department of Computer Science and Engineering

Nehru College of Engineering and Research Centre Pampady,

Thrissur, India

Nehru College of Engineering and Research Centre Pampady,

Thrissur, India

Abstract

Mobile Ad hoc networks are becoming popular with the advances in wireless technology. In MANET the main issue is the

security management. When it comes to node level a trust scheme is needed for secure transmission of data. We have to ensure

that a minimum trust is guaranteed for each node. In this paper a review is conducted on the basis of a trust model proposed for

MANETs including two methods for trust evaluation, Direct Observation and Indirect Observation. Uncertain reasoning derived

from artificial intelligence used to evaluate the trust values. In direct observation method a node is directly observing the other

node to which it has to send the data. Then calculate the trust value using Bayesian Inference. Indirect observation deals with

observing nodes indirectly from neighbor nodes. Then calculate the trust value using Dempster Shafer Theory. Bayesian

Inference and DST are coming under the field of uncertain reasoning. Values obtained from these two components are combined

to get more accurate trust value. Routing protocol OLSRV2 is used to evaluate the trust model. With this proposed scheme

throughput and packet delivery ratio can be improved.

Keywords: MANET, Trust, Security

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I. INTRODUCTION

MANETs are becoming popular with the recent advances in wireless technology. Mainly in military applications MANETs have

become the key communication method. In Mobile Ad hoc networks managing trust is a challenging activity. Especially in this

military environment ensuring security is important. There are many chances for security attacks in MANETs due to its

distinctive features such as infrastructure less nature and distributed pattern. So maintaining a secure communication is an

important research topic.

Basically there are two approaches that can protect MANETs, that are prevention-based and detection-based approaches. For

prevention-based approaches a centralized key infrastructure should be there. The problem arise when this infrastructure is

destroyed then the whole system will become useless. This infrastructure may be the main target of the attackers in military

environment. Even though it can prevent malicious activities there are chances for misbehaviors. Still malicious nodes can

involve in routing activity. Detection-based approaches are the second method of protection. As the name indicates it can detect

malicious activities earlier and resolve it.

In this paper detection-based approach based on the concept of trust is utilized for ensuring the security of mobile ad hoc

networks. Trust can be of two types: a context independent reliability and decision trust. Here trust is considered as a degree of

belief function which says whether a node performs as expected. In a mobile ad hoc network trust of a node can be decide using

two methods: direct observation and indirect observation. Direct observation involves directly observing from an observer node.

With indirect observation a third party is utilized for observation and collection of data and is used in the case in which the

observer node is unable to reach observed node. Then combine the values from these two methods to get more accurate trust

value.

Fig. 1: Trust management model

Trust Management Scheme in MANET using Uncertain Reasoning and Fuzzy Logic in Trust Model (IJIRST/ Volume 2 / Issue 02/ 046)


Trust management involves trust establishment, trust update and trust revocation. Establishment is the process of evidence

collection and trust generation. Trust update component updates values according to secure transmission. Revocation component

recall the node with lower trust for the malicious activity.

The difference between this trust model and the existing approaches is the use of uncertain reasoning to calculate the trust

values. Uncertain reasoning coming under the field of artificial intelligence is mainly used in expert systems. Trust value

calculation with direct observation is done using Bayesian Inference. The same in indirect observation is done using Dempster

Shafer Theory. These two methods are coming under uncertain reasoning. The scheme differentiates data packets and control

packets and also prevents dropping packets.

The scheme is then evaluated under the routing protocol OLSRv2. OLSRv2 is a new version of OLSR which inherits some

algorithms from it and has added some new features.

II. RELATED WORKS

Trust based systems are mainly introduced in detection based techniques. Trust computations are of three types, direct, indirect

and hybrid models. In [5] trust is a fractional value in [0,1] calculated from observing node’s updates based on its neighbor’s

behavior. It also considers the past behaviors and weighing them on the basis of time. It requires the memory for storing these

past experiments. Here computational complexity is high. Trust computation is biased.

Past action and present behaviors are combined in [6]. Then use Bayesian estimate to determine trust. Measuring of trust is

considered as a probability value. Trust is measured for various observations. Here the single point failure is not considered.

Calculations required memory as well as computational complexity.

In [7] trust evaluation is based on controlled flooding recommendations. Trust is calculated in [0, 1]. Additional hardware and

computations are not needed. Flooding will cause communication overheads. Trust computation time is comparatively high.

III. SYSTEM MODEL

Ensuring greater network security involves combining hard security with soft security methods. Soft security methods involve

the trust of nodes by identifying malicious activities, assuring node reliability and access control. The trust model described in

this paper is coming under these soft security methods. The model is used to derive the trust value between two nodes. Trust can

be defined in terms of belief function. So trust is the degree of belief that a node performs as configured.

Trust: Properties A.

The term trust is desired when any of the nodes in a network want to start a communication session. The basic properties of trust

include dynamicity, subjectivity, context dependency, non-transitivity and asymmetry. Dynamicity is the frequent change of the

trust of a node with change in its behavior. Subjectivity is the right of an observer node to identify the trust of an observed node.

Context dependency says that the trust calculation is only based on the node’s behavior. Non-transitivity is the property that if

node A trusts node B and node B trusts node C then node A may not trust node C. Asymmetry says that if node A trusts node B

then node B need not trusts node A. In trust evaluation reputation plays an important role. Reputation is considered as the

collection of trust values of all nodes in the network.

Framework of Trust Model B.

There are three components in the model that are application, networking and trust scheme as shown in Fig. 2. Trust evaluation

and update component collects data from direct and indirect observation modules. It uses two approaches for the calculation,

Bayesian inference and DST. Then this updated value is stored in the trust repository module.

Fig. 2: Framework of the model



Networking component involves routing schemes responsible for secure routing. It establishes routing paths between the

communicating entities based on the values stored in trust repository. These routing paths are used by the application component

to send the data.

IV. TRUST EVALUATION FROM DIRECT OBSERVATION

With direct observation the trust value calculation is done by observing one hop neighbor. It is based on two behaviors, dropping

packet and modifying packet. We have to assume that each node can overhear packets forwarded by that node; thereby it can

determine whether it is modified or correctly forwarded. Then calculate the trust value using Bayesian Inference. It is used to

determine the unknown probability of the variable trust. For this a degree of belief function is used.

In this equation Ɵ is the belief function where 0< Ɵ <1. This formulation is derived from Baye’s theorem where x is the

number of packets forwarded successfully and y is the number of packets received successfully. P(x| Ɵ, y) is the likelihood

function, which is under Binomial distribution.

The method of direct and indirect method in a network is illustrated in fig. 3. If node A has to start a communication session

with node G, then it sends the data to node C. It is then forwarded to node E. At the same time node A overhears the packet send

by node C. Calculation is based on Bayesian Inference. TS is used to denote the trust from direct observation.

Fig. 3: Example of Mobile Ad hoc Network

Let ɵ be the belief function which is a random variable 0< ɵ<1.

TS = En [ɵ] (1)

TS is obtained by taking the expectation of belief function of n

th node. It considers past experience when calculating trust

value. If node has done any malicious activity then a punishment factor is assigned to it. It gives more weight for misbehavior.

So even if a node has a good history of trusted communication its trust value will be lower if it has undergone any malicious

activity recently. Its trust cannot be recovered easily. It ensures secure data transmission.

The punishment factor comes from our daily lives. That is after getting a punishment a node cannot recover its trust easily. So

within the network we can easily identify the malicious activities and such nodes. With direct observation the trust value

calculated is only based on the observer node’s opinion.



V. TRUST EVALUATION FROM INDIRECT OBSERVATION

Fig. 4: Method of indirect observation

Indirect observation deals with collecting information from neighbor nodes. Then applying Dempster Shafer Theory to calculate

the trust value. As illustrated in fig. 3, if node A wants to send data to node E, then it first send data to node C. For this it asks

node B and node D whether the node C can be trusted or not. Then calculates the trust value according too DST.

DST is used to collect the data from several sources and to combine these data. Here also belief function is used.

VI. PROBLEM DEFINITION

We have seen two methods; Direct and indirect observation. With direct observation the problem arise when one node is

mitigated to observer node and malicious to other nodes. The trust value calculation is only based on this observer’s opinion. So

there is chance for packet modification and dropping. The trust value calculated may be incorrect. As a solution indirect

observation is introduced. Here we are collecting information from neighbor nodes in order to calculate the trust value. Still there

can be malicious activities since the neighbor nodes can be malicious nodes. So the calculation will be incorrect as the

information from one source is incorrect.

VII. TRUST MODEL USING FUZZY LOGIC

Trust model which is working based on the principle of fuzzy logic is presented as the solution for above mentioned drawbacks.

Instead of observing and correcting each node it can register nodes using fuzzy logic. That is after finding the shortest path from

source to destination it will identify the nodes included in that path. Then register those nodes to the network using fuzzy logic.

Then sender can easily transmit the data to the destination through these registered nodes.

VIII. SIMULATION RESULTS

Simulation results show that the trust scheme used here is performing better than the existing schemes. By combining the trust

values of direct and indirect observation it can ensure more secure transformation. By applying fuzzy logic in truest model for

registering nodes another trust verification method can be developed which is performing better than the trust scheme. That is by

registering nodes using fuzzy logic it ensures a secure logical data transmission through these registered nodes. There is no

chance of malicious activity.

Fuzzy logic when applied in networking considered the whole network as a fuzzy system. Here in fuzzy rule based networks

nodes are considered as fuzzy rule bases and the connections between the nodes are considered as the interactions between these

rule bases.

Here fuzzy network is viewed as a fuzzy system having networked rule bases as opposed to fuzzy systems with single or

multiple rule bases. So utilizing fuzzy rule in this system gives better performance.



Fig. 5: Packet size versus loss comparison

Fig. 6: Routing overhead comparison

Fig. 7: Simulation time and packet delivery ratio comparison



IX. CONCLUSION

A trust model is introduced to enhance the security in mobile ad hoc networks that includes direct and indirect observation. The

use of uncertain reasoning will provide finest value for the trust variable. Bayesian inference with direct observation and

Dempster Shafer Theory with indirect observation are used to calculate the trust value. Trust assurance using fuzzy logic is

another better method. It registers each node needed for data transmission and sends the data. It ensures a secure transmission.

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Trust Management Scheme in MANET using Uncertain Reasoning and Fuzzy Logic in Trust Model