Biometric Based Intrusion Detection System using Dempster-Shafer Theory for Mobile Ad hoc Network Security

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 7 384 – 391
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IJRITCC | July 2017, Available @ http://www.ijritcc.org
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Biometric Based Intrusion Detection System using Dempster-Shafer Theory for
Mobile Ad hoc Network Security
1
Dr. Ramalingam M., 2
Dr. Prabhusundhar P, 3
Dr. Thiagarasu V
1,2
Assistant Professors in Computer Science
3
Associate Professor in Computer Science
Gobi Arts & Science College (Autonomous)
Gobichettipalayam, T.N India
ramsgobi@gmail.com, drprabhusundhar@gmail.com
Abstract—In wireless mobile ad hoc network, mainly, two approaches are followed to protect the security such as prevention-based approaches
and detection-based approaches. A Mobile Ad hoc Network (MANET) is a collection of autonomous wireless mobile nodes forming temporary
network to interchange data (data packets) without using any fixed topology or centralized administration. In this dynamic network, each node
changes its geographical position and acts as a router for forwarding packets to the other node. Current MANETs are basically vulnerable to
different types of attacks. The multimodal biometric technology gives possible resolves for continuous user authentication and vulnerability in
high security mobile ad hoc networks (MANETs). Dempster’s rule for combination gives a numerical method for combining multiple pieces of
data from unreliable observers. This paper studies biometric authentication and intrusion detection system with data fusion using Dempster–
Shafer theory in such MANETs. Multimodal biometric technologies are arrayed to work with intrusion detection to improve the limitations of
unimodal biometric technique.
Keywords- Mobile Ad hoc network, Dempster–Shafer theory, Intrusion Detection System.
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I. INTRODUCTION
This paper is concerned with the study and analysis of
biometric-based security for mobile ad hoc network to
progress the security in order to decrease the network
attacks and leakage of information. With the propagation of
inexpensive, smaller and more powerful mobile devices,
mobile ad hoc networks (MANETs) have become one of the
wildest growing areas of research and it becomes a popular
research subject due to their self-configuration and self-
maintenance capabilities. Wireless nodes can initiate a
dynamic network without a static infrastructure. This type of
network is very useful in tactical operations where there is
no communication setup. However, security is a major
concern for providing reliable communications in a
potentially hostile situation. This new type of self-
organizing network combines wireless communication with
a high degree node mobility. Unlike, conventional wired
networks mobile ad hoc networks don’t have fixed structure
(base stations, centralized management points and the like).
The union of nodes forms an arbitrary topology. This
malleable nature makes them attractive for many
applications such as military applications, where the
network topology may change rapidly to reflect a force’s
operational movements and disaster recovery operations,
where the existing/fixed infrastructure may be non-
operational. The ad hoc self-organization also makes them
suitable for virtual conferences, where setting up a
traditional network infrastructure is a time consuming high-
cost task. Basic functions like packet forwarding, routing
and network management are accomplished by the dedicated
nodes in the conventional networks. In ad hoc networks
these are carried out collaboratively by all available nodes.
Nodes on MANETs use multi-hop communication: nodes
within the radio range can communicate directly via
wireless links, meanwhile nodes which are far must rely on
intermediate nodes to act as routers to relay messages.
Mobile nodes can move, leave and join the network and
routes are need to be updated frequently due to the dynamic
network topology.
II. MANET SECURITY
Because of MANET’s special characteristics, there are some
important metrics in MANET security that are important in
all security approaches; call them ―Security Parameters‖.
Being unaware of these parameters may cause a security
approach useless in MANET. Figure 1. shows the relation
between security parameters and security challenges. Each
security approach must be aware of security parameters as
shown in Figure 1. All mechanisms proposed for security
aspects, must be aware of these parameters otherwise they
may be useless in MANET. Security parameters in MANET
are as follows:

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Figure 1. Relation between Security Parameters and
Security aspects [Ali Dorri et al., 2015]
Network Overhead: This parameter refers a number of
control packets generated by security approaches. Due to
shared wireless media, additional control packets may easily
congestion or collision in MANET. Packet lost is one the
results of congestion and collision. Therefore, high packet
overhead increases packet lost and the number of
retransmitted packets. This process could easily waste nodes
energy and networks resources.
Processing Time: Each security approach needs time to
detect misbehaviors and eliminate malicious nodes. Due to
MANET’s dynamic topology it’s strongly possible that
routes between two different nodes break because of
mobility. Therefore, security approaches must have as low
as possible processing time in order to increase MANET
flexibility and avoid rerouting process.
Energy Consumption: In MANET nodes have limited
energy supply. Therefore, optimizing energy consumption is
highly challengeable in MANET. High energy consumption
reduces nodes and network’s lifetime. Each security
protocol must be aware of these three important parameters.
In some situations, a trade-off between these parameters is
provided in order to perform a satisfaction level in all of
them. Security protocols that disregard these parameters
aren’t efficient as they waste network resources [Ali Dorri et
al., 2015].
III. MANET SECURITY CHALLENGES
Generally, there are two important aspects in security:
Security services and Attacks. Services refer to some
protecting policies in order to make a secure network, while
attacks use network vulnerabilities to defeat a security
service. Security Services The aim of a security service is
to secure network before any attack has happened and made
it harder for a malicious node to break the security of the
network. Due to special features of MANET, providing
these services face lot of challenges. For securing MANET,
a trade-off between these services must be provided, means
that, if one service guarantees without the notice of other
services, security system will fail. Providing a trade-off
between these security services depend on the network
application, but the problem is to provide services one by
one in MANET and presenting a way to guarantee each
service. The below section discuss five important security
services and their challenges as follows:
Availability: According to this service, each authorized
node must have access to all data and services in the
network. Availability challenge arises due to MANET’s
dynamic topology and open boundary. Accessing time is the
time needed for a node to access the network services or
data. It is important, because time is one of the security
parameters. Authors provided a new way to solve this
problem by using a new trust based clustering approach. In
the proposed approach which is called ABTMC
(Availability Based Trust Model of Clusters), by using
availability based trust model, hostile nodes are identified
quickly and should be isolated from the network in a period
of time, therefore availability of MANET will be
guaranteed.
Authentication: The goal of this service is to provide
trustable communications between two different nodes.
When a node receives packets from a source, it must be sure
about identity of the source node. One way to provide this
service is using certifications, whoever, in absence of central
control unit, key distribution and key management makes
challengeable. Ali Dorri et al., [2015] presented a new way
based on trust model and clustering to public the certificate
keys. In this case, the network is divided into some clusters
and in this clusters public key distribution will be safe [Ali
Dorri et al., 2015]. But it has some limitations like
clustering. MANET dynamic topology and unpredictable
nodes position, made clustering challengeable.
Data confidentially: According to this service, each node or
application must have access to specified services that are
permitted to access. Most of the services that are provided
by data confidentially use encryption methods, but in
MANET, as there is no central management, key
distribution faces lots of challenges and in some cases
impossible. Authors proposed a new scheme for reliable
data delivery to enhance the data confidentially. The basic
idea is to transform a secret message into multiple shares by
secret sharing schemes and then deliver the shares via
multiple independent paths to the destination. Therefore,
even if a small number of nodes that are used to relay the
message shares, been compromised, the secret message as a
whole is not compromised. Using multipath delivering
causes the variation of delay in packet delivery of different
packets. It also leads out-of-order packet delivery.

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Integrity: According to integrity security service, just
authorized nodes can create, edit or delete packets. As an
example, Man-In-The-Middle attack is against this service.
In this attack, the attacker captures all packets and then
removes or modifies them. Authors presented a mechanism
to modify the DSR routing protocol and gain data integrity
by securing the discovering phase of routing protocol.
Non-Repudiation: By using this service, neither source nor
destination can repudiate their behavior or data. In other
words, if a node receives a packet from node 2, and sends a
reply, node 2 cannot repudiate the packet that it has been
sent. Authors presented a new approach that is based on
grouping and limiting hops in broadcast packets. All group
members have a private key to ensure that another node
couldn’t create packets with its properties. But creating
groups in MANET is challengeable [Ali Dorri et al., 2015].
IV. BIOMETRIC SECURITY
A biometric is defined as a unique, measurable,
biological characteristic or trait for automatically
recognizing or verifying the identity of a human being.
Statistically analyzing these biological characteristics are
known as the science of biometrics. In those days, biometric
technologies are typically used to analyze human
characteristics for security purposes. The most common
physical biometric patterns analyzed for security purposes
are the fingerprint, hand, eye, face and voice [Colin Soutar
et al., 1999].
A. Biometric-Based User Authentication
Biometric technology can be used automatically and
continuously identify or verify individuals by their
physiological or behavioral characteristics. Biometric
systems include two kinds of operation models: 1)
identification and 2) authentication. In the proposed system,
the biometric systems operate in authentication mode, (one-
to-one match process) to address a common security
concern: positive verification (the user is whoever the user
claims to be). In most real-world implementations of
biometric systems, biometric templates are stored in a
location remote to the biometric sensors. In biometric
authentication processes, two kinds of errors can be made:
1) false acceptance (FA) and 2) false rejection (FR). FAs
result in security breaches since unauthorized persons are
admitted to access the system/network. FRs result in
convenience problems since genuinely enrolled identities
are denied access to the system/network and maybe some
further checks need to be done. The frequency of FA errors
and of FR errors is called FA rate (FAR) and FR rate (FRR),
respectively. The FAR can be used to measure the security
characteristics of the biometric systems since a low FAR
implies a low possibility that an intruder is allowed to access
the system/network. In tactical MANETs, failure in user
authentication might result in serious consequences. Hence,
more than one biometric sensor is used at each time period
in the system to increase the effectiveness of user
authentication [K.K.Lakshmi Narayanan et al., 2012]. The
classification of biometrics shown in figure 2.
B. Multimodal Biometric Systems
In order to overcome the disadvantages of uni-modal
biometrics, biometrics to be ultra-secure and to provide
more-than-average accuracy, more than one form of
biometrics is required and hence, the need arises for the use
of multimodal biometrics. Instead of using a single
biometrics, a combination of different biometric can be used
for recognizing a human being. Multimodal biometric can
be composed in three different fusion methodologies, such
as fusion at the feature level, match score level and decision
level. As fourth level, a new fusion technique is used in this
work, which fuses the security services provided by the
system by adding more biometric modalities the security
level increases. [Snehlata Barde et al., 2012].
C. Operational modes of multimodal biometric systems
It is evident that a single biometric trait is not enough to
meet the variety of requirements including matching
performance and recognition accuracy imposed by several
large-scale authentication systems. Multimodal biometric
recognition systems appear more reliable due to the
presence of multiple, independent pieces of data. A
multimodal biometric system can operate in three different
modes. In the serial mode of operation, the output of one
biometric trait is used to narrow down the number of
possible identities before the next trait is used. In a parallel
mode of operation, information from multiple traits is used
simultaneously to perform recognition. In the hierarchical
scheme, individual classifiers are combined in a treelike
structure.

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Figure 2. Two Biometric Families [Claude Barral 2010]
V. INTRUSION DETECTION SYSTEM
Intrusion detection is a process of monitoring computer
networks and systems for violations of security and can be
automatically performed by IDS. User authentication is
performed by using one or more types of validation factors:
knowledge factors, possession factors, and biometric factors.
Knowledge factors (such as passwords) and possession
factors (such as tokens) are very easy to implement but can
make it difficult to distinguish an authentic user from an
impostor if there is no direct connection between a user and
a password or a token. Biometrics technology, such as the
recognition of fingerprints, irises, faces, retinas, etc.,
provides possible solutions to the authentication problem.
Using this technology, individuals can be automatically and
continuously identified or verified by their physiological or
behavioral characteristics without user interruption. In
addition, intrusion detection systems (IDSs) are important in
MANETs to effectively identify malicious activities and so,
MANET may appropriately respond. IDSs can be
categorized as follows: 1) network-based intrusion
detection, which runs at the gateway of a network and
examines all incoming packets; 2) router-based intrusion
detection, which is installed on the routers to prevent
intruders from entering the network; and 3) host-based
intrusion detection, which receives the necessary audit data
from the host’s operating system and analyzes the generated
events to keep the local node secure. For MANETs, host-
based IDSs are suitable, since no centralized gateway or
router exists in the network.
Two main technologies of identifying intrusion detection in
IDSs are given as follows: misuse detection and anomaly
detection. Misuse detection is the most common signature-
based technique, where incoming/outgoing traffic is
compared against the possible attack signatures/patterns
stored in a database. If the system matches the data with an
attack pattern, the IDS regards it as an attack and then raises
an alarm. Unable to detect new forms of attack is the main
drawback of misuse detection. Anomaly detection is a
behavior-based method, which uses statistical analysis to
find changes in baseline behavior. This technology is
weaker than misuse detection but has the benefit of catching
the attacks without signature existence. Multiple algorithms
have applied in model attack signatures or normal behavior
patterns of systems. Three common algorithms are naive
Bayes, artificial neural network (ANN) and decision tree
(DT). A naive Bayes classifier is based on a probabilistic
model to assign the most likely class to a given instance.
ANN is a pattern recognition technique with the capacity to
adaptively model user or system behavior. DT, which is a
useful machine learning technique, is used to organize the
attack signatures to a tree structure. Most of the IDSs only
use one of the preceding algorithms [K.K.Lakshmi
Narayanan and A.Fidal Castro, 2012].
IDS Protocol
Finger Print
Biometrics
BehaviouralPhysiological
Hand Scan
Iris Scan
Retina Scan
Facial Scan
Signature
Voice
Keystroke
Gait

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The IDS protocol is classified as, region-voting-based IDS
and group voting based IDS. Both protocols derive the
concept of distributed revocation based on majority voting
for evicting a target node in the context of sensor networks.
To do so, each node is preinstalled with host-based IDS to
collect information to detect the status of neighboring nodes.
Techniques such as misuse detection (also called signature
based detection) and anomaly detection can be used to
implement host-based IDS in each node. The effectiveness
of host-based IDS is measured by two parameters, namely,
the per-node false negative probability (p1) and per-node
false positive probability (p2). In voting-based IDS,
compromised nodes are detected based on majority voting.
If the majority decide to vote against the target node, then
the target node would be evicted from the system. This adds
intrusion tolerance to tolerate collusion of compromised
nodes in MANETs. Jin-Hee Cho and Ing-Ray Chen [2008]
explained and characterized voting-based IDS by two
parameters, namely, false negative probability (Pfn) and
false positive probability (Pfp). These two parameters are
calculated based on (a) the per-node false negative and
positive probabilities (p1 and p2); (b) the number of vote-
participants, m, selected to vote for or against a target node;
and (c) an estimate of the current number of compromised
nodes which may collude with the objective to disrupt the
service of the system. Since m nodes are selected to vote, if
the majority of m voting-participants casts negative votes
against a target node, the target node is considered
compromised and will be evicted from the system. The two
voting-based algorithms investigated in [Jin-Hee Cho and
Ing-Ray Chen, 2008], namely, region-voting-based IDS and
group-voting-based IDS, are differentiated by the way m
vote-participants are selected when evaluating a target node.
Each node periodically exchanges its routing information,
location, and id to its neighboring nodes. In region-voting-
based IDS, only nodes in the same geographical ―region‖
are candidates as vote-participants with respect to a target
node. [Jin-Hee Cho and Ing-Ray Chen, 2008].
VI. DEMPSTER-SHAFER THEORY
The Dempster-Shafer evidence theory is not only a theory of
evidence but also a probable reasoning. It is a framework
that can be deployed in diverse areas such as pattern
matching, computer vision, expert systems and information
retrieval. The D-S evidence theory can handle the
randomness and subjective uncertainty together in the trust
evaluation. By accumulating evidences, it can narrow down
a hypothesis set which provides a powerful method for the
representation and process of the trust uncertainty without
the demand of prior distribution. Moreover, Dempster’s rule
of combination is the procedure to aggregate and summarize
a corpus of evidence [Bo YANG et al., 2013].
Without a fixed security infrastructure, mobile ad hoc
networks must distribute intrusion detection among their
nodes. But even though a distributed intrusion detection
system can combine data from multiple nodes to estimate
the likelihood of an intrusion, the observing nodes might not
be reliable. The Dempster-Shafer theory of evidence is well
suited for this type of problem because it reflects
uncertainty. Moreover, Dempster’s rule for combination
gives a numerical procedure for fusing together multiple
pieces of evidence from unreliable observers [Thomas M,
2005].
The Dempster-Shafer (DS) theory for uncertainty was first
developed by Arthur Dempster [P. Dempster,1968] and
extended by Glenn Shafer [G. Shafer,1976]. The theory
provides necessary tools for combining various evidences
and gives them various weightings, according to their
importance in the final decision making, their quality and
relevance. Glenn Shafer et al., justify the use of the DS
theory by the uncertain nature of the trust prediction
problem and the need to combine the different criteria
(evidences) and concerned with the value of some quantity
u, and the set of its possible values is U. The set U is called
frame of discernment. In the prediction scheme, the frame of
discernment U is a trust value of mobile node which is able
to become the trusted nodes in future. The frame of
discernment is U {T, ØT}, m(A) represents the exact belief
committed to A, according to the evidence associated with
each node’s opinion about the Suspicious node. If m(A) > 0
then A is called a focal element. The focal elements and the
associated bpa define a body of evidence. Each subset of U
is assigned a probability that represents the belief affected
by the evidence. This confidence value is usually computed
based on a density function m: 2U → [0, 1] called a basic
probability assignment (bpa) function.
  0, ( ) 1m A Um A   
CH has got the information from neighbor nodes and the
following probability assignments are given. If received
trust value t>0.5, the node is treated as trusted. If received
trust value t<0.5, node is treated as untrusted and the
probability is assigned accordingly.
m1 ({T}) = 0.8
m1 ({  T}) = 0
m1 ({T,  T}) = 0.2 [This state is for Suspicious]
And the CH has the probability assignments on the same
node
m2 ({T}) = 0.6
m2 ({  T}) = 0
m2 ({T,  T}) = 0.4 [This state is for Suspicious]
A. The Dempster Combination Rule
Let m1 and m2 be the bpa associated with two independent
bodies of evidence defined in a frame of discernment U. The
new body of evidence is defined by a bpa, m on the same
frame U.

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1
1( ) 2( )
( ) 1 2 1( ) 2( )
B C
B C A
K m B m c
m A m m K m B m C
 

 

  


The rule focuses only on those propositions that both bodies
of evidence support. The K of the above equation is a
normalization factor that ensures that m is a bpa. The
combination rule is commutative and associative. In this
approach, the clusterhead computes the trust of each node
according to each criterion (evidence) and combines them
two by two. An example solution is illustrated in Table 1.
Therefore,
m1  m2 ({T}) = (1) (0.24+0.32+0.12) = 0.68
m1  m2 ({  T}) = (1) (0) = 0
m1  m2 ({T,  T}) = (1) (0.08) = 0.08
m2
m1
{T}:0.6 {  T}:0 {T,
 T}:0.4
{T}: 0.8
.24 0 .32
{  T}: 0 0 0 0
{T, T}:
0.2
.12 0 .08
Table 1. An example of combining evidences using DS
Theory [Pushpita Chatterjee, 2009]
So the given evidence presented here by m1 and m2, the
most probable belief for this Universe of discourse is T with
probability 0.68. Any CH will execute this algorithm for
getting the most probable belief after collecting
recommendation trust from others [Pushpita Chatterjee,
2009].
B. Design of Multimodal Biometric IDS System
Biometric-Based User Authentication: Biometric technology
can be used to automatically and continuously recognize
two types of operation models: (i) identification and (ii)
authentication.
Step 1: select sensor uk+1 that will be used at
time k+1
Step 2: at time k+1, observe the output of
sensor uk+1
{Observation yk+1=yk+1(uk+1)}
Step 3: update information state πk+1 with
observation yk+1
Figure 3. Sensor scheduling and information state
update
In the proposed model, the biometric systems operate in
authentication mode to address a common security concern:
positive verification based on a comparison of the matching
score between the input sample and the enrolled template
with a decision threshold. Improved intrusion detection
model: Intrusion detection is a process of observing
computer networks and systems for abuses of security. Two
key technologies of detecting intrusion detection in IDS are
given as follows: (i) misuse detection and (ii) anomaly
detection [Jie Liu et al., 2009].
Step 1: Biometric technology can be used automatically and continuously identify the physiological or
behavioral characteristics
Step 2: Biometric-Based User Authentication: two kinds of operation models: i) identification and ii)
authentication
Step 3: Sensors are chosen for continuous authentication and IDS at each time space to detect the security
formal of the network
Step 4: Dempster–Shafer reasoning system: Set of mutually exclusive and exhaustive possibilities is
enumerated in the frame of discernment and two security states for each node: secure and compromised state
Step 5: Fusion of biometric sensors and IDS
Figure 4. Biometric based IDS Procedure
Data fusion of biometric sensors: In the proposed
model, sensors are picked for authentication and intrusion
detection at each time slot to detect the security state of the
network. Two major fusions are used to select the biosensor
such as class set reduction (CSR) and a class set reordering
(CSRR). CSR methods try to find the minimal reduced class
set, in which the true class is still represented. CSRR
methods try to increase the true class ranking as high as
possible. It produces soft outputs, which are the real values
in the range [0, 1]. Dempster–Shafer theory: In a Dempster–

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Shafer reasoning system, a set of mutually exclusive and
exhaustive possibilities is enumerated in the frame of
discernment. In this two security states for each node, secure
and compromised states are used in the Dempster–Shafer
theory in the fusion of biometric sensors and IDS.
VII. CONCLUSION
In this research exposition, the MANET security systems
have been studied and classified into prevention-based
approaches such as authentication and detection-based
approaches such intrusion detection. The biometric method
improves the network security, in order to achieve high
security and good reliability in ad hoc network even the high
number of nodes. Further, analysis the existing multimodal
biometric based IDS system, operational modes of
multimodal biometric systems, data fusion, IDS protocol
and Dempster combination Rule.
References:
[1] Ali Dorri and Seyed Reza Kamel and Esmail
kheyrkhah, 2015., “Security Challenges in Mobile
Ad Hoc Networks: A Survey”, International Journal
of Computer Science & Engineering Survey
(IJCSES), Vol. 6, No. 1, pp. 15 – 29, February
2015.
[2] Arnab Maji., 2010., “Load Balancing in Wireless
Mobile Ad hoc Networks”, Bachelor of
Technology, National Institute of Technology,
Rourkela.
[3] Ali Hilal Al-Bayatti., 2009., “Security
Management for Mobile Ad hoc Network of
Networks (MANoN)”, De Montfort University.
[4] Bo Yang, Ryo Yamamoto and Yoshiaki Tanaka.,
2013., “Dempster-Shafer Evidence Theory Based
Trust Management Strategy against Cooperative
Black Hole Attacks and Gray Hole Attacks in
MANETs”, ICACT Transactions on Advanced
Communications Technology(TACT), Vol. 2(3),
pp. 223 – 232, May, 2013.
[5] Claude BARRAL., 2010., “Biometrics &
Security:Combining Fingerprints, Smart Cards and
Cryptography”, in the proceedings of ÉCOLE
POLYTECHNIQUE FÉDÉRALE DE
LAUSANNE.
[6] Carvallo M., and Garcia Luna Aceves J.J., 2004.,
“A Scalable Model for Channel Access Protocols
in Multihop Ad Hoc Networks”, IEEE Conference
on Mobile Computing and Networking, pp. 330 –
344.
[7] Dempster P., 1968., ―A Generalization of Bayesian
Interface‖, Journal of Royal Statistical Society, pp.
205 – 447.
[8] Jie Liu, Richard Yu F, Chung-Horng Lung and
Helen Tang., 2008., “A Framework of Combining
Intrusion Detection and Continuous Authentication
in Mobile Ad Hoc Networks”, in the proceedings of
ICC 2008 of IEEE Communications Society, 978-
1-4244-2075-9/08/$25.00 ©2008 IEEE, pp. 1515 –
1519.
[9] Jain A.K., Ross A. and Prabhakar S., 2004., “An
Introduction to Biometric Recognition”, in the
proceedings of IEEE Transactions on Circuits and
Systems for Video Technology, Vol. 14, pp. 4 – 20,
Jan, 2004.
[10] Jain A.K., Bolle R. and Pankanti S., 1998.,
“Biometrics, PersonalIdentification in Networked
Society: Personal Identification in Networked
Society”, Kluwer Academic Publishers, Norwell,
MA, USA.
[11] Jin-Hee Cho and Ing-Ray Chen., 2008., “Effect of
Intrusion Detection on Secure Group
Communications in Hierarchically Structured
Group Architectures”, in the proceedings of IEEE
Transactions, pp. 942 – 949.
[12] 12. Lakshmi Narayanan K K, Fidal Castro A.,
2012., “High Security for Manet Using
Authentication and Intrusion Detection with Data
Fusion”, International Journal of Scientific &
Engineering Research, Vol. 3, Issue 3, pp. 1 – 4,
March, 2012.
[13] Luc Hogie., 2007., “Mobile Ad Hoc Networks:
Modelling, Simulation and Broadcast-Based
Applications”, University of Le Havre, University
of Luxembourg.
[14] Marco Conti, Silvia Giordano, Gaia Maselli, and
Giovanni Turi., 2004., “Mobile Metropolitan Ad
hoc Networks”, in the proceedings of the 8th
International IFIP-TC6 Conference, Lecture Notes
in Computer Science, LNCS - 2775, pp. 194 – 205.
[15] Macker J.P., and Corson S., 2003., “Mobile Ad hoc
Networks (MANET): Routing Technology for
Dynamic, Wireless Networking in Mobile Ad hoc
Networking”, IEEE, Press and John Wiley and
Sons, Inc., New York.
[16] Pushpita Chatterjee., 2009., “Trust Based
Clustering and Secure Routing Scheme for Mobile
Ad Hoc Networks”, International Journal of
Computer Networks & Communications (IJCNC),
Vol. 1, No. 2, pp. 84 – 97, July, 2009.
[17] Prabhusundhar P, Dr. Srinivasan B and
Narendirakumar V K., 2013., ―Border Crossing
Security and Privacy in Biometric Passport using
Cryptographic Authentication Protocol‖, in the
proceedings of International Conference on
Computer Communication and Informatics
(ICCCI), 4th to 6th January 2013, IEEE Catalog
Number: CFP1308R-CDR (CD), ISBN 978-1-
4673-2905-7 ©2013 IEEE, pp. 1 – 7.
[18] Prabhusundhar P and Dr. Srinivasan B.,
―Identification of Biometric- Based Continuous
User Authentication and Intrusion Detection
System for Cluster Based MANET‖, International
Journal on Recent and Innovation Trends in
Computing and Communication, ISSN No. 2321-
8169 (print), Vol. 4, No. 3, March- 2016, pp. 207 –
213.
[19] Prabhusundhar P and Dr. Srinivasan B.,
―Multimodal Biometric Based Intrusion Detection
System for Clustered Mobile Ad Hoc Network

Volume: 5 Issue: 7 384 – 391
_______________________________________________________________________________________________
391
_______________________________________________________________________________________
Using POMDP Algorithm‖, KASMERA Journal
(Science Citation Indexed Journal), ISSN: 0075-
5222, Vol. 44, No. 1, pp. 2 – 9.
[20] Raghu Trivedi T. and Dr. Seshadri R.., “Efficient
Cryptographic Key Generation using Biometrics”,
International Journal of Comp. Tech., Vol. 2, No.
1, pp. 183 – 187.
[21] Ramalingam M and Dr. Thiagarasu V., “Cluster
Based Stretch and Shrink Method for Manet Using
Load Balancing, Nearest Neighbor and Rule
Mining”, International Journal of Engineering
Sciences & Research Technology, ISSN: 2277-
9655, P.no 392-400, Oct-2014.
[22] Ramalingam M and Dr. Thiagarasu V., “Identified
the Cluster Based Stretch and Shrink Method
Based On Load Balancing Algorithm for Ad Hoc
Network Topology Stability‖, International Journal
on Recent and Innovation Trends in Computing
and Communication ISSN: 2321-8169, Volume: 2
Issue: 9 p.no: 2631 – 2635
[23] M. Ramalingam, Dr.V Thiagarasu, ―Routing and
Broadcasting in MANET: A comprehensive
Analysis based on, Routing technique, Clustering
and Architectural Model‖, International Journal of
Engineering Sciences & Research Technology,
ISSN: 2277-9655, IJESRT 3 (11),November 2014.
[24] Shengrong Bu, Richard Yu F, Xiaoping P. Liu,
Peter Mason and Helen Tang., 2011., “Distributed
Combined Authentication and Intrusion Detection
with Data Fusion in High-Security Mobile Ad Hoc
Networks”, IEEE Transactions On Vehicular
Technology, Vol. 60, No. 3, March 2011, Pp. 1025
– 1036.
[25] Shafer G 1976., ―A Mathematical Theory of
Evidence‖, Princeton University Press.
[26] Thomas M. Chen and Varadharajan
Venkataramanan., 2005., “Dempster-Shafer Theory
for Intrusion Detection in Ad Hoc Networks”, in
the proceedings of IEEE INTERNET
COMPUTING, Published by the IEEE Computer
Society, pp. 35 – 41.

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