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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.
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