INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 3, Issue 12 December, 2017

IJTC201712002 www. ijtc.org 420

Network Attack Aware Routing using AODV and

TORA in MANETs 1Manju, 2Mrs Maninder kaur

1Research Scholar, 2Assistant professor

12DIET, Mohali

Abstract: Mobile ad-hoc networks (MANETs) are used in the variety of applications now-a-days, which includes military

applications, weather, security, etc. The MANETs are known as the unstructured network, which means there is no need of any base

station or centralized nodes in order to connect the client nodes. These ad-hoc networks are capable of forming the communicating

cluster by inter-connecting the nodes in the particular manner. The inter-connectivity of MANET nodes requires the lower layer

communication along with a robust routing algorithm. There are several routing algorithms, which involves AODV, TORA, DSR,

DSDV, etc. In this thesis, we have worked on the further improvements in the TORA and AODV protocols to create the more

adaptable and secure protocols. The routing protocols are responsible to route the data of source nodes to the destination nodes in

the given MANET clusters. The ad-hoc networks are known to keep the neighboring tables, which are used to elect the paths between

the source and destination nodes. The neighboring tables are queried by the source nodes in the 1-hop based layered architecture in

all of the directions until the target node is found. Afterwards the query response is provided to the source node in order to use that

path for the transmissions across the networks towards the destination node. In the MANETs, the multi-directional traffic takes

place and creates the MANET cluster with non-directed graph methodology. The MANETs are vastly prone to the various attacks,

as MANET nodes do not get pre-embedded security protocols due to their limited processing capability. Hence, these networks

require the security schemes to be embedded within the routing protocols to prevent them from the outside attacks. In this thesis,

the AODV and TORA have been evaluated under both normal and DDoS attack conditions, which is the most popular attack on

MANET nodes. The performance has been measured in the form of multiple performance parameters, which includes data drop,

end to end delay, network load, jitter, etc. The data drop has been observed highest in the TORA (2792 packets) in the under attack

situation, whereas AODV (1600 packets) has performed far better in similar situation. The delay has been also observed higher in

TORA (62 milliseconds) than AODV (25.57 milliseconds) under the attack situation. The AODV is prone to carrying the higher

network loads (overhead) at 1.08 KBPS under attack in comparison to 0.033 KBPS. The jitter is also observed at higher limits in

AODV (52 milliseconds) in comparison to TORA (34 milliseconds) under the attack situation. This clearly shows the robustness of

the TORA protocol under all situations in comparison to the AODV as per the analysis of the performance evaluation results.

KEYWORDS: MANETs, Routing protocol, Mobility Routing, Dynamic Path Allocation.

I. INTRODUCTION

A Mobile Ad Hoc Network (MANET) is the network having

no infrastructure. These networks are self organizing, all the

mobile nodes plays the role of router by itself. These networks

communicate via wireless links without any fixed

infrastructure or fixed access point that maintains all routing

activities of mobile nodes, in MANET term mobile nodes

implies that the nodes are wireless devices like (Smart phones,

laptops and etc.), Ad-hoc implies that the network having no

infrastructure for routing activities and wireless links shows

that communication is done by dynamic topology [18]. Shows

an example of an ad hoc network, where there are numerous

combinations of transmission areas for different nodes. From

the source node to the destination node, there can be different

paths of connection at a given point of time. But each node

usually has a limited area of transmission as shown in fig. 1

by an oval circle around each node. A source can only transmit

data to node B but B can transmit data either to C or D.

Figure 1.1: Ad hoc Networking Model [18]

Routing is the act of moving information from a source to a

destination in a network. During this process, at least one

intermediate node within the network is encountered. The

routing concept basically involves two activities: firstly,

determining optimal routing paths and secondly, transferring

the information groups (called packets) through a network.

Unlike wired network where data is transferred over the

physical link which makes these networks more secure than

the wireless networks [18]. Wireless links makes easy way for

attackers to attack because the communication medium is air

(radio communication channel). Due to this limitation various

types of attacks are active attacks and passive attacks. Active

attacks drop the packets and also modify the data and Passive

attacks only listen to the packets but does not modify.

II. LITERATURE REVIEW

Mary et al [18] examined the performance of reactive

multicast routing protocol Multicast Ad hoc on demand

Distance Vector Protocol (MAODV) under the influence of

wormhole nodes under different scenarios and design a Worm

Hole Secure MAODV (WHS-MAODV) by applying

certificate based authentication mechanism in the route

discovery process. The proposed technique can greatly

enhance network performance in the presence of malicious

nodes. WHS-MAODV is as effective as MAODV in

discovering and maintaining routes in addition to providing

the required security. The proposed protocol reduces the

packet loss due to malicious nodes to a considerable extent

thereby improve the performance. Mary et al [17] analyzed

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the performance of reactive multicast routing protocol On

Demand Multicast Routing Protocol (ODMRP) under the

influence of worm hole nodes under different scenarios and

design a Worm Hole Secure ODMRP (WHS-ODMRP) by

applying certificate based authentication mechanism in the

route discovery process. The proposed protocol reduces the

packet loss due to malicious nodes to a considerable extent

thereby enhancing the performance. Abdesselam et al [1]

presented an effective method for detecting and preventing

wormhole attacks in OLSR. To find wormhole tunnels a

simple four-way handshaking message exchange method is

used. The proposed solution is easy to deploy: it does not need

the time synchronization or any location information .It does

not require any complex computation or special hardware

requirement. The performance of this approach shows a high

detection rate under various scenarios. This method first

attempt to pinpoint links that may potentially be part of a

wormhole tunnel, then a proper wormhole detection

mechanism is applied to suspicious links by means of an

exchange of encrypted probing packets between the two

supposed neighbors (end points of the wormhole). Zhou et

al[28] proposed a new algorithm called Neighbor-Probe-

Acknowledge (NPA) for detection of wormhole attacks.

NPA does not require time synchronization or any other

hardware. Moreover, it accomplishes higher detection rate

and lower false alarm rate than the methods using RTT under

different background traffic load conditions. Lazos et al [15]

proposed the use of geometric random graphs induced by the

communication range constraint of the nodes; we present the

necessary and sufficient conditions for detecting and

defending against wormholes. Using our theory, we also

present a defense mechanism based on local broadcast keys.

We believe our work is the first one to present analytical

calculation of the probabilities of detection. Dhurandher et al

[8] proposed an energy efficient scheme to detect the

wormhole attack called Energy efficient scheme to immune

the wormhole attack (E2IW). This protocol usage the location

information of the mobile nodes to find the presence of a

wormhole, and in case a wormhole exists in the path, it

discovers another routes involving the nodes of the selected

path so as to get a more secure route to terminus. S. Gupta et

al [22] proposed an approach, called WHOP (Wormhole

Attack Detection Protocol using Hound Packet), which is

based on the AODV protocol and considered to detect

wormhole attack with the help of hound packets. In this

approach a hound packet is sent after the route discovery

process, means after the route has been discovered. This

hound packet is processed by all the nodes except that nodes

which are involve in the path setup process. Basically the path

discovery is done by the help of the two types of packet, called

RREQ and RREP. Yih-Chun Hu et al [7] described that the

wormhole attack can form a serious threat in wireless

networks, especially against many ad hoc network routing

protocols and location-based wireless security systems. To

detect and defend against the wormhole attack, two types of

leash information were used Geographical Leash and

Temporal Leash. In geographical leashes each node must have

its accurate location information and loose clock

synchronization.

III. EXPERIMENTAL DESIGN

Previously the works done on MANETs focused mainly on

different security threats and attacks such as DoS, DDoS, and

Impersonation, Wormhole, Sybil, and Black Hole attack.

Among these attacks Black Hole attack involved in MANET

is evaluated based on reactive routing protocol like Ad Hoc

On Demand Distance Vector (AODV) and TORA and its

effects are elaborated by stating how this attack disrupt the

performance of MANET. Very little attention has been given

to the fact to study the impact of Denial of Service attack in

MANET using Reactive and Hybrid routing protocols and to

compare the vulnerability of both these protocols against the

attack. There is a need to address these types of protocols

under the attack, as well as the impacts of the attacks on the

MANETs. This thesis analyzes Denial of Service attack in

MANETs using AODV and TORA which are reactive and

hybrid routing protocols respectively in nature.

This research project analyzes the AODV and TORA under

Denial of Service and Distributed Denial of Service attacks,

which are reactive and hybrid routing protocols respectively

in nature. These attacks can result as a long and unexpected

service downtime which can affect the cellular networks and

businesses at a large, can result in mass losses to the cellular

network services companies. To avoid these situation the

selection of the existing MANET protocols based on their

security mechanism becomes extremely important. Also the

existing popular routing protocol has to be improved

periodically to avoid the future developments in the security

attack mechanisms for MANETs. To make the selection and

improvements in the existing protocols it is extremely

important to analyze the performance of the existing MANET

protocols. The popular MANET protocols in these days are

AODV and TORA. In this research we have analyze the

performance of these protocols under DoS and DDoS attacks.

Random Way Point Model: The random waypoint mobility

model is simple and is widely used to evaluate the

performance of MANETs. The random waypoint mobility

model contains pause time between changes in direction

and/or speed. Once a mobile node (MN) begins to move, it

stays in one location for a specified pause time. After the

specified pause time is elapsed, the MN randomly selects the

next destination in the simulation area and chooses a speed

uniformly distributed between the minimum speed and

maximum speed and travels with a speed 𝑣whose value is

uniformly chosen in the interval (0, 𝑉max). 𝑉max is some

parameter that can be set to reflect the degree of mobility [20].

Then, the MN continues its journey toward the newly selected

destination at the chosen speed. As soon as the MN arrives at

the destination, it stays again for the indicated pause time

before repeating the process.

Figure 1 Random Way Point Model Structure [20]

Reference Point Group Mobility Model: This model is

described as another way to simulate group behavior where

each node belongs to a group where every node follows a

logical center (group leader) that determines the group's

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motion behavior. The nodes in a group are usually randomly

distributed around the reference point. The different nodes use

their own mobility model and are then added to the reference

point which drives them in the direction of the group. At each

instant, every node has a speed and direction that is derived

by randomly deviating from that of the group leader. This

general description of group mobility can be used to create a

variety of models for different kinds of mobility applications.

Group mobility as such can be used in military battlefield

communications [21]. One example of such mobility is that a

number of soldiers may move together in a group. Another

example is during disaster relief where various rescue crews

(e.g., firemen, policemen, and medical assistants) form

different groups and work cooperatively.

Figure 2 Reference Model Structure [20]

Experimental Procedure: In this Dissertation we have used

NS2 to implement wormhole attack and its prevention.

Bonnmotion tool is used to generate Random Way Point

Model and Reference Group Point model.

Scenario 1:

First some nodes were taken randomly.

One node is taken as source one as destination and two as

malicious.

Random way point model is implemented on these nodes.

Malicious nodes in wormhole attack make a tunnel and shows

that they can directly communicate and can send packet

though each other. But actually they use some nodes of

network to send packet thus may not be the shortest path.

To detect wormhole attack we are calculation distances

between each pair of path.

When distance of malicious nodes is checked (as they have

shown that they communicate directly), the distance was

larger than the threshold value of range.

So, it becomes clear from above method that there is a

wormhole attack.

To prevent from wormhole attack we can change path of

packets.

Energy, throughput, ratio of packets is calculated from trace

file of above scenario.

Second Scenario 2:

Then in second scenario nodes were put in reference model

and mobility scenario of reference group point model is

generated using bonnmotion tool.

Then first scenario is repeated for wormhole attack and its

prevention and energy throughput is calculated from trace file

of second scenario as it was done in first scenario.

Then comparison of energy, throughput of both scenarios is

made.

RESULTS AND DISCUSSION

The comparative results are observed between the different

situations performed on the given set of routing protocols, i.e.

AODV and TORA. The following table 5.1 shows the

readings of data drop for every 0.5 seconds. This table

represents the four simulation models involving the AODV

and TORA protocols under normal and attack situations. The

normal observation of the table indicates the increase in the

data drop with every 0.5 seconds in all of the cases. However,

the data drop rate is differentiated in all of the models, where

the maximum overall drop has been observed in the case of

TORA under the DDoS attack. It is followed by the AODV

under DDoS attack. In the case of normal scenario, the TORA

is learned to outperform AODV by marginal difference.

Table 5.1: Data drop between AODV and TORA under normal and attack situations

Simulation Time AODV DDoS TORA DDoS TORA normal TORA normal

0.5 0.1 1 1 1

1 0.2 2 2 2

1.5 0.3 32 3 4

2 0.4 100 4 5

2.5 0.5 204 5 6

3 56.5 322 6 7

3.5 132.5 452 7 8

4 216.5 590 8 9

4.5 316.5 750 8 9

5 428.5 936 9 10

5.5 556.5 1154 10 11

6 692.5 1398 11 12

6.5 848.5 1642 12 13

7 1016.5 1910 13 14

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7.5 1200.5 2188 14 15

8 1392.5 2482 15 16

8.5 1600.5 2792 16 17

9 1820.5 3136 17 18

9.5 2056.5 3506 18 19

The comparative results are observed between the different

situations performed on the given set of routing protocols, i.e.

AODV and TORA. The following table 5.2 shows the

readings of network delay for every 0.5 seconds in the

simulation. This table represents the four simulation models

involving the AODV and TORA protocols under normal and

attack situations. The normal observation of the table

indicates the increase in the data drop with every 0.5 seconds

in all of the cases for all of the models.

However, the data drop rate is differentiated in all of the

models, where the maximum overall delay has been observed

in the case of TORA under the DDoS attack. It is followed by

the AODV under DDoS attack. In the case of normal scenario,

the TORA is learned to outperform AODV by marginal

difference.

Table 5.2: The comparison based on Data Delay in AODV and TORA

Simulation Time

AODV

Normal AODV DDoS TORA Normal TORA DDoS

0.5 0 0 1 2

1 0 0 2 4

1.5 0 0 3 6

2 0 0 4.606342628 9.212685257

2.5 0 0 6.212685257 12.42537051

3 2.695928114 5.900935921 7.819027885 15.63805577

3.5 5.391856227 8.978558323 9.425370513 18.85074103

4 6.643974737 10.91428851 11.03171314 22.06342628

4.5 7.896093247 12.85001869 12.63805577 25.27611154

5 8.858512627 14.82793177 14.94001005 29.88002009

5.5 9.820932006 16.49689699 17.24196432 34.48392864

6 10.65237887 18.1658622 19.5439186 39.08783719

6.5 11.48382573 19.68463804 21.84587287 43.69174574

7 12.24720372 21.20341387 24.14782715 48.29565429

7.5 13.01058171 22.7221897 26.44978142 52.89956284

8 13.73002673 24.22878306 28.7517357 57.50347139

8.5 14.44947174 25.57435989 31.05368997 62.10737995

9 15.13953816 26.91993672 33.35564425 66.7112885

9.5 15.82960457 28.2336665 35.65759852 71.31519705

In the following table, the comparison of all four models is

performed on the basis of network load. The network load has

clearly observed higher in the case of attack than normal,

when compared in all of the simulation models. The highest

load has been observed in the case of AODV under attack.

However, the network load under both normal and attack

conditions of AODV protocol are quite higher than TORA in

both of the conditions.

Table 5.3: The comparison based on Network Load in AODV and TORA

Simulation Time AODV normal AODV DDoS TORA normal TORA DDoS

0.5 0 0 0.00032 3.20E-05

1 0 0 0.00064 6.40E-05

1.5 0 0 0.00096 9.60E-05

2 0 0 0.01408 0.001408

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2.5 0 0 0.0272 0.00272

3 0.00512 0.01632 0.04032 0.004032

3.5 0.01024 0.03776 0.05344 0.005344

4 0.0368 0.08064 0.06656 0.006656

4.5 0.06336 0.12352 0.07968 0.007968

5 0.11136 0.18272 0.1056 0.01056

5.5 0.15936 0.24704 0.13152 0.013152

6 0.2288 0.31136 0.15744 0.015744

6.5 0.29824 0.39712 0.18336 0.018336

7 0.38912 0.48288 0.20928 0.020928

7.5 0.48 0.56864 0.2352 0.02352

8 0.59232 0.67584 0.26112 0.026112

8.5 0.70464 0.80448 0.28704 0.028704

9 0.8384 0.93312 0.31296 0.031296

9.5 0.97216 1.0832 0.33888 0.033888

In the following table, the comparison of all four models is

performed on the basis of jitter. The jitter has clearly observed

higher in the case of attack than normal, when compared in all

of the simulation models. The highest jitter has been observed

in the case of AODV under attack. However, the jitter under

attack condition of AODV protocol is quite higher than

TORA in both of the conditions, whereas AODV under

normal condition is slightly lower than the jitter in normal

TORA.

Table 5.4: The comparative analysis on jitter in AODV and TORA

Simulation Time AODV normal AODV DDoS TORA normal TORA DDoS

0.5 0 0 1 2

1 0 0 2 4

1.5 1 3 2 4

2 3 8 3 6

2.5 5 13 4 8

3 7 15 5 10

3.5 9 17 6 12

4 11 19 7 14

4.5 13 22 7 14

5 15 25 8 16

5.5 17 30 9 18

6 18 33 10 20

6.5 21 34 11 22

7 23 37 12 24

7.5 25 38 13 26

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8 26 41 14 28

8.5 28 44 15 30

9 30 48 16 32

9.5 32 52 17 34

CONCLUSION

The proposed model is based upon the TORA and AODV

routing protocols, and works towards the elimination of the

attacked nodes and attackers from the network in order to

rejuvenate its performance. The rejuvenation of the network

requires the establishment of the robust and stable paths for

the data transmissions, which eventually inter-connect the

nodes and establishes the vital communication paths across

the networks. Both, TORA and AODV are evaluated on the

basis of different performance parameters, which eventually

dictate the performance in the terms of jitter, network load,

delay and data drop. TORA (62 milliseconds) takes higher

time than the AODV (25 millisecond) under the attack

situation, but manages to establish the stronger and stable

paths across the network. AODV has been outperformed by

TORA in the case of data drop rate, where AODV is recorded

with 2056 packets, which are dropped during the simulation

in comparison to the TORA with 3506 packets. This shows

nearly 41% less data volume, which is dropped during the

communication across the network paths. However, TORA

has been recorded with lesser network load, which means the

data drop may be occurred due to the early queue drop off

mechanism followed by TORA protocol. The TORA protocol

has been recorded with 0.034 seconds in comparison to 1.08

seconds in the case of AODV in the context of network load.

This shows the robust performance by TORA. Hence, we

have discovered that TORA is a better option in case the stable

paths is the necessity, whereas the AODV is much better

option if end-to-end delay plays the important role in the

certain application.

REFERENCES

[1]. Abdesselam F., Bensaou B., and Taleb T.(2008),

“Detecting and Avoiding Wormhole Attacks

in.Wireless Ad Hoc Networks”, IEEE Communication

Magagine,46(4):127-133.

[2]. Aschenbruck, Nils, et al.(2010), "Bonnmotion: a

mobility scenario generation and analysis

tool." Proceedings of the 3rd International ICST

Conference on Simulation Tools and Techniques. ICST

(Institute for Computer Sciences, Social-Informatics

and Telecommunications Engineering).

[3]. Cai F.,Yougquan C. et.al (2013), “Projection Pursuit

Based Wormhole Detection in Ad Hoc Network”, 2013

IEEE 10th International Conference on High

Performance Computing and Communications & 2013

IEEE International Conference on Embedded and

Ubiquitous Computing (HPCC_EUC),1315-1322.

[4]. Chan K.S. and Alam M.R. (2011), “TCBWD:

Topological comparison-based Byzantine wormhole

detection for MANET”, 2011 IEEE 7th International

Conference on Wireless and Mobile Computing,

Networking and Communications (WiMob), 388-394.

[5]. Chaurasia and Singh V.(2013), “MAODV: Modified

wormhole detection AODV protocol”, 2013 Sixth

International Conference on Contemporary

Computing (IC3),239-243.

[6]. Chiu H.S. and Shan K. L. (2006), “DelPHI: Wormhole

Detection Mechanism for Ad Hoc Wireless Networks”,

2006 1st International Symposium on Wireless

Pervasive Computing, 138-143.

[7]. Chun Y. H., Adrian P. and David B. J.(2003), “Packet

Leashes: A Defense against Wormhole Attacks in

Wireless Networks”, INFOCOM 2003 Twenty-Second

Annual Joint Conference of the IEEE Computer and

Communications, 3:1976-1986

[8]. Dhurandher S.K., Woungang I. et al.(2012), "E2SIW:

An Energy Efficient Scheme Immune to Wormhole

Attacks in Wireless Ad Hoc Networks",26th

International Conference on Advanced Information

Networking and Applications Workshops, 472-477.

[9]. Gambhir S. and Sharma S. (2013), "PPN: Prime

Product Number based Malicious Node Detection

Scheme for MANETs", IEEE 3rd International

Advance Computing conference, pp. 335-340.

[10]. Harri J., Fethi F., and Christian B.(2009), "Mobility

models for vehicular ad hoc networks: a survey and

taxonomy." Communications Surveys & Tutorials,

IEEE,11(4):19-41.

[11]. Jain M. and Kandwal H.(2009), “A Survey on

Complex Wormhole Attack in Wireless Ad Hoc

Networks”, International Conference on Advances in

Computing, Control, & Telecommunication

Technologies,555-558.

[12]. Kaur, S., Ataullah, M., & Garg, M. (2013), “Security

from Denial of Sleep Attack in Wireless Sensor

Network”, International Journal Of Computers &

Technology, 4(2):419-425.

[13]. Khalil I., Bagchi S. and Shroff N.B.(2005),

“LITEWORP: A Lightweight Countermeasure for the

Wormhole Attack in Multihop Wireless Networks”,

International Conference on Dependable Systems and

Networks, 612–621.

[14]. Khan Z.A. and IslamM.H.(2012), “Wormhole attack:

A new detection technique”, 2012 International

Conference on Emerging Technologies (ICET),3(2):1-

6.

[15]. Lazos L. and poovendran R.(2005), “Preventing

wormhole attacks on wireless ad hoc networks: a graph

theoretic approach”, IEEE Wireless Communications

and Networking Conference,2:1193-1199.

IJTC.O

RG

IJTC201712002 www. ijtc.org 426

[16]. Louazani A., Sekhri L and Kechar B.(2013), “A time

Petri net model for wormhole attack detection in

wireless sensor networks”, International Conference

on Smart Communications in Network Technologies,

1:1-6.

[17]. Mary A. , Bai V.T. et.al (2011), “Defending against

worm hole attacks in multicast routing protocols for

mobile ad hoc networks”, 2011 2nd International

Conference on Wireless Communication, Vehicular

Technology, Information Theory and Aerospace &

Electronic Systems Technology (Wireless VITAE), 1-5.

[18]. Mary A. and Vasudevan V. (2010), “A certificate-

based scheme to defend against worm hole attacks in

multicast routing protocols for MANETs”, 2010 IEEE

International Conference on Communication Control

and Computing Technologies (ICCCCT), 407-412.

[19]. Nayak P., Sahay A., Pandey K. (2013), “Detection and

Prevention of Wormhole Attacks in MANETs using

Detection Packet”, International Journal of Scientific

& Engineering Research, vol 4, no 6, pp.1216-1222.

[20]. Oztas B.,Kurt T. and Anarim E.(2011), “A survey of

social based mobility models for ad hoc networks”,

2011 2nd International Conference on Wireless

Communication, Vehicular Technology, Information

Theory and Aerospace & Electronic Systems

Technology (Wireless VITAE),1-5.

[21]. Qing, L., Zhu, Q., & Wang, M. (2006), “Design of a

distributed energy-efficient clustering algorithm for

heterogeneous wireless sensor networks”, Computer

communications, 29(12):2230-2237.

[22]. Saurabh G., Subrat K. and Dharmaraja S.(2011),

“WHOP: Wormhole Attack detection Protocol using

Hound Packet”, 2011 International Conference on

Innovations in Information Technology, 226-231.

[23]. Su M. Y.(2010), “WARP: A Wormhole Avoidance

Routing Protocol by Anomaly Detection in Mobile Ad

Hoc Networks”, Elsevier, Computers & Security,

29:208-224.

[24]. Sun Choi, Doo-Young Kim, Do-Hyeon Lee and Jae-D

Jung (2008), “WAP: Wormhole Attack Prevention

Algorithm in Mobile Ad hoc Networks”, In Proc. of

IEEE International Conference on Sensor Networks,

Ubiquitous and Trustworthy Computing, 343-348

[25]. Xia W. and Wong J. (2007), “An End-to-end Detection

of Wormhole Attack in Wireless Ad-hoc Networks”,

COMPSAC 2007. 31st Annual International Computer

Software and Applications Conference,1:39-48.

[26]. Xiaopei L., Dezun D and Xiangke L.(2013),

“WormPlanar: Topological Planarization Based

Wormhole Detection in Wireless Networks”, 2013

42nd International Conference on Parallel Processing

(ICPP),498-503.

[27]. Xu, W., Trappe, W., & Zhang, Y. (2007), “Channel

surfing: defending wireless sensor networks from

interference”, In Proceedings of the 6th international

conference on Information processing in sensor

networks, 499-508.

[28]. Z. Jie, C. Jiannong et.al(2012), “Analysis and

Countermeasure for Wormhole Attacks in Wireless

Mesh Networks on a Real Testbed”, 26th IEEE

International Conference on Advanced Infromation

Networking and Applications,59-66.

IJTC.O

RG