Survey of wormhole attack in wireless sensor networks

Computer Science and Information Technologies
Vol. 2, No. 1, March 2021, pp. 33~42
ISSN: 2722-3221, DOI: 10.11591/csit.v2i1.p33-42  33
Journal homepage: http://iaesprime.com/index.php/csit
Survey of wormhole attack in wireless sensor networks
Umashankar Ghugar, Jayaram Pradhan
Department of Computer Science, Berhampur University, Brahmapur, Odisha 760007, India
Article Info ABSTRACT
Article history:
Received May 12, 2020
Revised Jul 2, 2020
Accepted Jul 29, 2020
From the last decade, a wireless sensor network (WSN) has a very important
role over the networks. The primary features of WSN include satellite
communication, broadcast channel, hostile environment, medical system and
data gathering. There are a lot of attacks available in WSN. In wormhole attack
scenario is brutal from other attacks, which is smoothly resolved in networks
but tough to observe. This survey paper is an experiment to observing threats
and also focuses on some different method to identify the wormhole attacks.
Keywords:
IDS
MANET
Sensor node
Wormhole attacks
WSN This is an open access article under the CC BY-SA license.
Corresponding Author:
Umashankar Ghugar
Department of Computer Science
Berhampur University
Brahmapur, Odisha 760007, India
Email: ughugar@gmail.com
1. INTRODUCTION
Wireless sensor network (WSN) built a network, which is a spread, automatic governing network and
it corresponding with several sensor nodes in specific environment. Nodes are observed by the natural
conditions, such as humidity, compression, heat, wave and direction at different areas [1]. It is a tiny device
which has a limited measurement resource. They are gradually arranged in a wireless sensor environment [2].
WSN are broadly utilized on different applications such as, area observing, defense surveillance, health care
system, home affirmation and satellite communication.WSN suffers from various security issue because
usually it is deployed in hazardous environment. Sensor node has some limitation such as limited lifetime, less
computing capability and low memory space [3, 4]. Based on these limitations, they are arranged in noisy
environment, it is highly affected and sensitive to several types of attacks [5]. Basically, sensor nodes are
category by four sub-systems [6-13, 14]. Processor and memory, transceiver, sensor and battery. Here we have
discussed the several types of attacks.Mainly attacks are classified by two parts. First part is the attack against
security mechanism and another is routing mechanism. Numbers of attacks are listed as below but we are
focuses on wormhole attack.
 Wormhole Attack
 Sybil Attack
 Blackhole Attack
 Hello flood Attack
 Sinkhole Attack
 Denial of Service

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Comput. Sci. Inf. Technol., Vol. 2, No. 1, March 2021: 33 – 42
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Thus, these survey papers basically focus on various approaches to detect wormhole attacks. In
Section 2 discussed the intrusion detection system in WSN; In Section 3 discussed the wormhole attack in
wireless sensor networks; In Section 4 discussed various detection approaches of the wormhole attack in
wireless sensor networks with summary. Finally in Section 5, we have discussed the future research challenges
and conclusion.
2. INTRUSION DETECTION SYSTEMS
An intrusion detection system (IDS) is used for observing the network diagnosis against nasty
movements and informed to the base stations. Mainly it is divided by two types: misuse IDS and anomaly IDS.
Misuse IDS In this system, the abnormal pattern is calculated and contrast with the previous data [5]. Signal’s
energy is used to detect the malicious node, where if the energy is collision with the actual positions then the
message transmission is considered as doubtful [6]. Anomaly IDS- It is detected by protocol, where prevention
method is used before the detection stage. Here protocols are activated on the data with respect to the network
performance. When the data is satisfying the rule then it is called as normal node else malicious node. When
the intruder is detected, then informs to the system [7, 8]. In routing ,various multipath routing technique is
used for best redundancy path with high energy efficiency efficiency [9]. Watchdog technique is a detection
technique, where each node can observers their neighboring nodes within the radio range [10].
3. WORMHOLE ATTACK
The wormhole attacks are most brutal in nature. Generally, more than two malicious nodes create a
secrete route is called tunnel. Here the attackers are built a connection to each node, so that they can
communicate at a high speed over the networks with other nodes. A wormhole attacks can be freely carried out
across routing in the sensor networks. Routing protocols has no mechanism to prevent against it [11]. In other
words, when the wormhole attacks occurs, it dropping all the packets and cause network interruption.
Wormhole attack is also used in the form of merging of selective forward and Sybil attack [12]. In Figure 1,
the data packet accepted Node D from Node A and vice versa.
Figure 1. Wormhole attack in WSN
3.1. Types of wormhole attack
Here, we have categories the wormhole attack established on the several techniques. Numbers of
nodes are participating for establishing the method for wormhole into following types [15].
 Using packet encapsulation: The number of data packet and node are encapsulated between two nasty
nodes.
 Using out-of-band channel: Only single nasty node is occurring with the high speed of communication
scope.
 Using packet relay: The nasty node gives replays to all data packets between the two communicated nodes.
Finally, the duplicate node is created by nasty node.
 Using protocol distortion: Single nasty node is tries for cracking the attack, which is attack by the routing
protocol.

Comput. Sci. Inf. Technol. 
Survey of wormhole attack in wireless sensor networks… (Umashankar Ghugar)
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3.2. Routing protocols for wormhole attack
Most of the routing protocols are used in WSN.The routing protocols are categories into: Proactive
and Reactive [16]. AODV, Secure-AODV and DSR are proactive routing protocols where as DSDV, OLSR,
OSPF are Reactive routing protocols.
Figure 2. Routing Protocols
3.2.1Aodv routing protocol
The AODV stand for ad-hoc on-demand distance vector. Its is a mostly used protocol and called as
dynamic reactive routing protocol [17-18], that create a self route on call support. When a sender node sends a
data packet to destination node, it must take the help of routing table. If the node gain recent paths then the
data packet are forwarded to destination else it uses the route discovery process. In AODV protocol, two control
message is used by route discovery process i.e route request (RREQ) and Route Reply (RREP). To obtain the
recent path, RREQ and RREP control messages are used. When the route discovery process is over, than the
data packet of source and destination node can be connected.
3.2.2Saodv routing protocol
AODV protocol on extension leads to SAODV protocol [19]. This has a greater utility in the security
to protect the route discovery mechanism. From desirable asymmetric cryptosystem, each node has a couple
of signature key and it is ability to verify the assumption between given address and public key of the same
node. So SAODV has the task of key management scheme [20].
3.2.3Dynamic source routing protocol (DSR)
DSR protocol is use to update the cache memory of route by route discovery process. It updates the
information about all links between the sender and receiver node. In order to transmit data, a well defined route
is taken into account by the route discovery process for node. This motive is achieved by route discovery
process and route maintenance process.
Route Discovery process: When a sender node forward a data to another node over network, it has to
go through its route cache. In case of unavailability of routes between the receiver and sender than route is
discarded and it broadcast RREP (Route Reply).When the receiver node or any intermediate node has received
the fresh path from the sender node, then RREP (Route Reply) is generated[21].
Route Maintenance Process: With the initiation of data transmission process, it is the task of sender
node to confirm that very next hop received both the data and transmit the route to receiver. In case sender
didn’t get a confirmation message than it generates route error message. After that the hop again starts the route
discovery process.
3.2.4Destination sequenced distance vector routing protocol (DSDV)
As per the theory of Bellman algorithm, it is a table driven routing program. Here the authors describe
the concept of routing loop problem using their algorithm. In this algorithm routing table store the sequence

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number. Basically the sequence number is used even number for the active network and odd number for
inactivate network. When the routing information circulated among inactive node, at that time occurs more
sending troubles [22].
3.2.5Optimized link state routing protocol (OLSR)
It is a proactive routing protocol and used for IP routing [23].Basically it is compatibility with mobile
ad hoc networks and ad hoc networks. To identify and set up the transmission link over network then it must
used hello and control message of topology. In OLSR, each node is calculating the next hop destination using
shortest forwarding path.
3.2.6Open shortest path first
It is used to find the least –cost path from a source node to a destination node within a group of nodes.
As shown in Figure 3, a group of routers using the same routing protocol for all introduced to an autonomous
system (AS).Upon joining the AS, a node uses the hello protocol to discover neighboring nodes. Then it forms
adjacencies with its new neighbors to exchange routing information [24] .Above all, it is faulty for every node
on a network connect to all other node of the network. To prevent this situation, a node is considered as the
destination node. It is considered to be neighboring node of each node over network and communicate the
information between them.
Figure 3. Connected autonomous system (ASS)
A backup node, which is always maintain update records for successful transaction so that if the
primary designated node crash can be replaced immediately. At the time of regular process, each node
repeatedly floods updated messages to neighboring nodes of every node. This message indicates its status and
provides the cost for topological database. When flooding message are proved acknowledgement that means
system is reliable. A node can check whether the incoming link is older or newer using sequence number.
When the cost is change then it sends all these messages. Database Messages provide the sequence number for
the entire channel, which is held by sender. When the value is comparing with the sender, then receiver can
resolve the most current values. When a line is delivering then this message is fully used in the system as the
result of this algorithm is that each pair of neighboring nodes detects the most recent data and new information
is transmitted on this way [25, 26].
4. DETECTION APPROACHES OF THE WORMHOLE ATTACK
We have discussed the different technique of intrusion detection system for wormholes attack and
categorized the different technique in ascending order from year 2013 to 2016. In [27], a wise solution is
prescribed to eradicated wormhole attacks for ad-hoc network by providing directional antenna to the nodes.
Node uses the definite regions of their antenna in establishing connection among them. Each pair of node has
evaluated the direction of receiving the information from either. Hence relation between consecutive neighbors
is established only if the direction of information flow of both the nodes is in arrangement with one another.
This additional information enable wormhole discovery and introduces the network fluctuation. So that it can
be smoothly spot. In [28], the authors’ proposed a more simple tool known as “Packet leashes“ accordance
with the recognition of geographical and temporal leashes. The information provided to the packets that
controls the transmission distance called Leach. The distance of sender and the receiver is specified by the
geographical leash. When the receiving nodes accept the data packets, it calculates the distance and time of the

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transmission. The receiver analyze now on comparing this information can detect whether the packet has
forwarded through wormhole attacks or not. Here the packet limitation is known by temporal leashes, which
is determining the distance it can cover the most. In this technique the position of node is not that important
rather than time factor plays an important role. It can access the time calculation and its comparison up to an
order of nanosecond. On each packet, the sender mention an authorized time bar, which is compared by the
receiver and the packet forwarding distance is simply given by the product of velocity of lights and transmission
time. In case of a large time difference it indicates the presence of wormhole. In [29], the authors put forward
a “graph theoretical” approach to prevent wormhole attacks. This concept is purely established on the “location
aware guard node” (LAGNs).When the key establishment process is used for detecting wormhole attack and it
also used the decoded message. If same message is heard from one guard or two LAGNs are heard from
different far away LAGNs then wormhole is detected.
In [30], the authors proposed that wormhole attacks in stationary sensor network are investigated using
network visualization. In this method, the signal strength determines the distance. Each sensor conveys all the
gathered information to the main station. The controller computes the networks physical topology using sensor
predicted distance. If a wormhole attack is present then it is seen that a string pulling the network terminals, if
not then the topology is flat. In [31], the authors adopted lightweight countermeasure for wormhole attack
called LITEWORP and this result has advantages of very quick detection of wormhole attacks and the loss of
fraction of packets is very less. In [32], here the author’s emphasis on the “round –trip travel time” (RTT)
message, which provides the maximum times require for the transmission. When this time is multiplied with
speed of lights it gives the distanced travelled. Now this distance is to be compared with the predicted distance.
If there is a large difference then it threat wormhole attacks. In [33], the authors describe that, wormhole attacks
in found in multipath routing. In case of new root requirements source excess by using route request (RREQ)
in the network and then the response is waited. The intermediate node only pass away this route request
(RREQ).On the same time the receiver will wait to get route after getting route request (RREQ).Statistical
Analysis of Multi-path (SAM) is introduced, that use Pmax and .which are higher if wormhole attack is
present.Pmax gives the probability of the routes out of all possible route and (theta) is the difference between
top two frequently papered links. If a wormholes attack is more than PMF (probability mass function) then it
gives high frequency. Here authors also analysis the multipath routing and DSR with fine comparisons.
In [34] a ”hello control message” is used to detect wormhole attacks as consent with OLSR in
particular. He used the aggregate of hello message time interval (HMTI) that lie within a jitter. A ranger= [T-
α, T+∞] is coated. In range HMTI are considered valid or else it is out of set of rules. In case of unusual HMTI
secondary checks are done. In addition to this an untrue positive alarm in negated in case of weak working
node which has many packets but this is not the case of and attacking node. In [35], the authors implemented
delay per hop indication (DelPHI) to detect wormhole attacks. It is also work on the same principle of
comparison of path time distance and predicted distance. This process works in two phases, first is collection
of route path by the receivers and senders include a DREQ packets similar to the concept of SAM and sign it
before sending. On the getting the packet the receiver has to add its ID and 1 hop count is incremented. The
minimum delay and hope count information are utilized for the minimum detection. In the second phase, “round
–trip travel time” (RTT) is used to calculate the time difference between the total number of sent information
and acknowledgement received. In this process the delay per hop value (DPH) is calculated as RTT/2h, where
h is the hop count to the definite consecutive. In normal case tiny hops have tiny RTT where as in case of
wormhole attack the tiny hops are giant RTT. If one delay per hop value (DPH) crosses the threshold value
then all paths next to this treated as under wormholes attacks In [36], the authors used a unique technique of
radio finger printing. It initiates with the radio signal receiving by the fingerprinting device and then the signal
is converted to the digital form. The signal passing is positioned and its characteristics are described. A set of
characters from fingerprints is later used for apparatus identification. In [37], the authors proposed a method,
when a sender send a RREQ message to receiver, then it waits for the RREP. Out of the number of RREP
received by the source, the RREP with highest frequency is compare with the predefined value. If the packet
drop ratio is larger than packet sent ration then it implies that wormhole is present. In [38], the authors proposed
that, two plot nodes are connected by tunnel such as they are neighbors.
The route request (RREQ) and topology control messages (TCM) are convey among these plot nodes
through tunnels. By using the extra tunnel nodes, these nodes have the shortest path. After the link is
establishing, the attacker select one another as multipoint relays (MRPs). As result few topologies control
messages and data packets are leaked through the tunnel. As consequence false topology information is spread
through the networks. In [39], the author’s proposed a trust based model for detection in wireless sensor
networks. In trust based system, each node has some values, which is called trust value. By using this trust
values the source node is calculated the actual route to the destination. When the transmission occurs over
network, in which number of packets drop ratio is high means trust value is less and wormhole attacks is present
in the network. If the trust value is high means, all the packets which is received by the destination, it indicates

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that the trust value is high on the neighboring node of source node between the source to destination. In [40],
the author’s proposed a distributed intelligent agent-based system. Here the ambition is the use of generalized
intrusion detection system (IDS) framework which is so lightweight that it can run on the sensors node and it
identifies the wormhole attacks along with its attackers. When that attacker’s node is found in the network,
then it is informed with an indication message. After that each node makes their conclusion on the base of
consecutive node repeat. In [41], it is assumed that behaviors of a node are control by its consecutive nodes. A
node uses its neighbor node to send route request (RREQ) message to the destination node. If the sender didn’t
get route reply (RREP) message within predicted time, then sender conclude the presence of wormholes attack
and enclose this route in the list of wormhole attacks list. A conjugative node that is managed by every node
that consists of RREQ sequence number, Neighbors node ID, sending & receiving time of RREQ.The
maximum time limit equal to WPT/2 is waited by the sender if RREQ is delayed more than thus it indicates
the wormhole attacks and entirely it doesn’t support DSR Routing protocol.
In [42], Al the sender’s nodes wait for acknowledgment (ACK) message. If ACK message is not
received then the next node is attack, which is wormhole attacks. ACK message should not retrace the path
and sent between the separation by two hops. Now Time to Live (TTL) plays a great role since the path is
different. If the ACK message is not received within TTL then wormhole attacks is detected. In [43], the authors
used two step mechanism for the detecting the wormhole attacks. The first steps consist of two methods. In the
first method, the node and his next node are identified by using round-trip-time (RTT) and in the second method
their list is made and if the destination node is not in that list then it is doubt full in nature. In the second step
mechanism, after detection of doubt full link the attack is concluded using RTS/CTS method. In [44], the
authors used AODV and DSR routing protocol. Here also a Trust based security model is used for detecting
intrusion. This model has been introduced to identify the attacks, which is called statically method. If any
connection gets doubtful, then the trust value is calculated to determine the wormhole attacks. In the trust
model, nodes monitored their neighbouring on the basis of packet drop pattern. If any node is found to be doubt
then stock trust is identified by the node, whether the node is affected by wormhole attack or not. In [45], the
authors proposed Digital investigation to detect wormhole attacks in WSNs. WSN are explained that add
generation and protects flow of evidences about sensors node characteristics in the network. A group of
detective nodes are spread over the networks to controls the topology and datagram passing by sensor nodes.
Observation node and base station node jointly forms different WSN networks called observation network.
Frequency bands are used to establish link between observers and the base station but this is not supported by
sensors node. The detection sensitivity of sensor node is less than the observer. In [46], the authors proposed a
'conflicting-set' for each node is made to filtering the false measurement of distance but its biggest limitation
was that, it works only where there is no packet loss but when attackers attacks then the Packet drops is certain
to happen. So the system is under a wormhole attacks.
In [47], the authors proposed a model, which create a cluster using no of nodes in MANET. In this
paper various data structure are explained and algorithm is also proposed. Here two layers are mention in the
cluster, where one node is treated as cluster head among several nodes. When a node is affected by a wormhole
attack in the layer1, then which informs to the cluster head of layer1.After that cluster head of layer1 will
indicates the cluster head of layer2 about the abnormal node. So that cluster head of layer2 indicate the message
to all the cluster head of layer1, then the cluster head of layer1 inform the messages to their respective node
within their cluster. In [48], the authors proposed localization-based systems, which are vulnerable to wormhole
attacks as they manipulate the localization method To prevent the wormhole attack, a 'distance-consistency-
based secure location' scheme was implemented, This works on the detection, exact location and trapping of
wormhole attacks In [49], the authors used techniques that identify the wormhole attacks. In the first way
algorithm uses hop counting method, rebuilt local maps at every nodes and then a diameter features to identify
by the problems due to wormhole attacks. The evaluated round trip times (RTT) between the consecutive nodes
are used to compare in the second way. Its major advantages is not required additional hardware and consume
less energy. In [50], the authors proposed that attackers may record the location of packets in WSN and send
them to one more location and again transmit them in to the network. When it found the roots, the wormhole
detection process is going on, which counts difference between the neighbour nodes to another node? If the
difference is more than the destination node detect the wormholes. In [51], the authors proposed the statistical
analysis to identify the wormhole attacks in WSN.The proposed algorithm is categories by three parts.i.e.
 Statistical analysis method, which is used for routing information for detecting the wormhole attacks.
 Determination of the vulnerable wormholes.
 Time constraints is used for validation in wormhole attacks.
It uses multi-path routing, time constraints and statistical analysis to verify the vulnerable connection.
It doesn’t need time synchronisation, directional antenna and GPS. In this method it can wormhole attacks with
high quality of accuracy. In [52], the authors propose the security emerges as a centrally in MANET. The
applications of MANET were deployed in various fields. Wormhole attack is a severe destructive in nature,

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which is smoothly resolved in networks but tough to observe. It is visible even if the intruder has not negotiated
at any situation and rest of all communication gives security, novelty, authenticity and confidently. In [53], the
author’s presents different types of sensor nodes and many layer wise attacks must be present in the network.
Wormhole attacks are used in this paper in attack model, which is smoothly resolved in networks but tough to
observe. Here the authors proposed a method, which is used the Mint route protocol. In [54], the authors address
the multiple –hop Mobile ad hoc networks, where each node acts as a host and router in the route. Author
proposed a technique, which is identify the attacks without using synchronization requirements. The basic thing
is to find another way from source to next hop and finally it calculates the no of hops for detecting wormholes
attacks. In [55], it uses packet encapsulation technique. Here packets are encapsulated in AODV protocol. In
this technique, less hop count is created and it is compared to other normal links. MLDW maintain a big
structure, which is divided by 04 parts, i.e:
 Examination layer.
 Disclosure layer.
 Reorganization layer.
 Segregation layer.
Here the First 03 layers work as a Detector and last layer works as a Preventer for wormhole attacks
in MANET using AODV protocol. In [56] ,the author’s proposed a technique, which is gives secure data
transmission using neighbour node analysis concept to identify the wormhole attacks in MANET. This
technique analyze the neighbouring nodes .so that it checks the reliability of the nodes for data transmission
on the network, According to this technique, a node send a request to its neighbour nodes and it maintain the
request and response system. Here node maintains a table for tracing the time out. If a node doesn’t get the
reply time that means attacks occurs in the network. The entire node from source to destination is analyzed to
detect the wormholes attack using AODV protocol in MANET. In [57], the authors propose a technique, which
is liable to detect wormholes attacks in MANET using analysis of the misbehaving nodes concept. According
to the authors, it concentrates on the detection of the abnormal nodes and prevention of the wormhole attacks.
The route discovery process is used, which is a sender node want to data sending process with another node in
the network, it has to go through its route cache. In case of unavailability of routes between the receiver and
sender than route is discarded and it broadcast RREP. The RREP is generated, when the receiver node or any
intermediate node has got the recent route to the receiver node. Another important is that DSR protocol is used
to detect the nodes where the misbehaving nodes are simple discarded and not including into the routing table
of DSR. In DSR, parameter is used for evaluating the network performance i.e jitter, throughput and delay. In
[58], here the authors used a general mechanism, which is used without hardware. It explains the details about
packet detection technique. That packet holds the information of localization and clock synchronization for
detecting affected node in MANET. Detection Packet has four fields: total hop count, processing bit, count to
reach next hop and timestamp .This fields are added to the header of detection packet. In [59], the authors
proposed a normalized wormhole local intrusion detection algorithm, which is up gradation version of local
intrusion detection routing security in MANET. In this technique an intermediate neighbor nodes are uses
discovery mechanism process and packet drop calculator. Based on the isolation technique, at the time of
transmission over the network, where each node received packet for the confirmed Wormhole nodes.
In [60], the authors proposed technique, which is based on Hash based compression function (HCF).
It is basically used for secure hash function to calculate the value of hash field for route request (RREQ) passes
over the networks. Here AODV routing protocol is used .As per the authors. Source node starts the route
discovery process for searching the destination node. Then the source node compute the HCF and also compute
the value of hash field with RREQ and it passes to his neighboring node. If the value of neighboring node is
same to the value of destination node .At that situation the destination node receives the no of RREQ. Finally
the destination node implement the HCF concept. Otherwise the others intermediate node between source to
destination, they will implement HCF hash fields and passes to its next node. If the calculated hash value is
compared to append hash value and gets the same result then the destination node send back RREP message
to the source. Otherwise if calculate the hash value is not same with the append hash value then the destination
node detects the RREQ and it treated as affected node by wormhole attackers.
In [61], the authors used a hybrid technique “wormhole resistant hybrid technique (WRHT)”. It based on
watchdog and Delhi Concept. It gives information about the packet drop and the delay per each hops and used
for the full phase route process in wireless sensor network. Here the authors build up method which is used for
wormhole detection in every sensor devices with low costs. WHRT is an extension version AODV routing
protocol. The proposed method is to allow for calculating the wormhole presence probability (WPP) for a path
in addition to hop count information in the source node over the sensor networks. During the route discovery
process, per hop time delay probability (TDPH) and time delay probability (TDPP) is calculated for detecting
wormhole attacks. In the next part of the WHRT, another parameter is calculated, which is called per hop
packet loss probability (PLPP). The values of PLPP and TDPP are used for decision making ,whether a path P

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is affected by wormhole attacks or not. So that the routing protocol AODV is taking correct way for the
transmission over the sensors networks. We presented several wormholes attacks in WSN.Finally, by
evaluating the positive and negative aspects of all existing techniques, till date open research challenges studied
are required for detection wormhole attacks. In Tables 1, the most important detection methods and
requirements are elaborated in sequentially with respect to year.
Tables 1. The most important detection methods and requirements are elaborated in sequentially
with respect to year
Researcher Year Method Tools Protocol Requirements/Commentary
H. Lu, D.
Evans [27]
2003 Directional
Antenna
- Directional
neighbor discovery
protocol
Directional antennas on each node with
GPS
Y.C. Hu and D.B.
Jhanson [28]
2003 Packet leashes
and end-to-end
NS2 TIK protocol GPS Coordinator and Loosely
Synchronized clock.
L.lazos,
R. Poovendram [29]
2004 Localization - - Based on location aware ‘guard
nodes’(LAGNs), not applicable to MANET
W. Wang and B.
Bhargava [30]
2004 Network
visualization
- - Centralized control, seems promising,
works based on dense networks, mobility is
not studied
Issa Khalil, Saurabh
Bagchi, Ness B. Shroff
[31]
2005 LITEWORP NS2 Key management
protocol
Applicable only in static networks,
A. Baruch, R. Curmola,
C. Nita-Rotaru, D.
Holmer, H. Rubens [32]
2005 Time of flight NS2 ODSBR Hardware enabling one-bit messages and
immediate reply without CPU involvement
N. Song, L. Qian, X. Li.
[33]
2005 Statistical
Approaches
NS2 MR and DSR Works only with multipath on demand
protocol
H.S. Chiu and K. Lui [35] 2006 Delphi NS2 AODV Not considered
K.B. Rasmussen and S.
Capkun, [36]
2007 Radio
Fingerprinting
- - Fingerprinting Devices is needed.
Khin Sandar Win. [37] 2008 DAW NS2 DSR, LF analysis Delay Parameter
S. Choi, D. Kim, D. Lee,
J. Jung [41]
2008 WAP CBR DSR Maximum transmission distance is
calculated
H. Vu, A. Kulkarni, K.
Sarac, N. Mittal [43]
2008 WORMEROS - - Time synchronization is required.
Topological change is not considered
M.S. Sankaran, S.
Poddar,
P. Das, [44]
2009 SAW - AODV Not considered
H. Chen, W. Lou, X. Sun,
and Z. Wang [48]
2010 Secure
localization
NS2 Conflicting the set-based resistance
localization,
Distributed detection system
Gupta S, Kar S,
Dharmaraja [50]
2011 WHOP NS2 WHOP, AODV Not required any hard support and clock
synchronization
C.P. vandana, A.F.S.
Devraj [55]
2013 MLDW NS2 AODV Not required any specialized hard support
and clock synchronization
R. singh, J, singh,
Ravindar singh [61]
2016 WRHT NS2 AODV It based on the combination of two
techniques, i.e. Watchdog and Delphi.
5. CONCLUSION
Wormhole attacks in WSNs are one of the brutal attacks that can be implemented easily in sensors
networks. In this paper numbers of methodologies is discussed for detecting wormhole attack. However, it is
not less information. Therefore we believe that the analysis on this paper is helping us for developing the new
method to detect wormhole.
REFERENCES
[1] M. Tiwari, K. Veer Arya, R. Choudhari, K. Sidharth Choudhary. “Designing Intrusion Detection to Detect Black hole and Selective
Forwarding Attack in WSN based on local Information.” Fourth International Conference on Computer Sciences and Convergence
Information Technology” 2009.
[2] E. Nam Huh and T. Hong Hai. “Lightweight Intrusion Detection for Wireless Sensor Networks” iTech.2011.
[3] J. Du, J. Li, “A Study of Security Routing Protocol for Wireless Sensor Network.” International Conference on Instrumentation,
Measurement, Computer, Communication and Control, 2011.
[4] F. Bao, I. Ray Chen, M. Jeong Chang, and J.-Hee Cho. “Hierarchical Trust Management for Wireless Sensor Networks and its
Applications to Trust-Based Routing and Intrusion Detection.” IEEE Transactions on Network and Service Management, vol. 9,
no. 2, pp. 169-183, June 2012.
[5] M. A. Rassam, M.A. Maarof and A. Zainal. “A Survey of Intrusion Detection Schemes in Wireless Sensor Networks.” American
Journal of Applied Sciences, vol. 9, no. 10, pp. 1636-1652, January 2012.

41
[6] W. Ribeiro Pires J´unior, T. H. de Paula Figueiredo H. Chi Wong, A. A.F. Loureiro. “Malicious Node Detection in Wireless Sensor
Networks.” Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS’04), IEEE, 2004.
[7] V. K. Jatav, M. Tripathi, M S Gaur and V. Laxmi. “Wireless Sensor Networks: Attack Models and Detection.” IACSIT Hong Kong
Conferences IPCSIT, IACSIT Press, Singapore, vol. 3, 2012.
A. Paula R. da Silva, M.H.T. Martins Bruno, P.S. Rocha, A. A.F. Loureiro, L. B. Ruiz, H. Chi Wong. “Decentralized intrusion detection
in wireless sensor networks.” Proceedings of the 1st ACM International Workshop on Quality of Service and Security in Wireless
and Mobile Networks, 2005.
[8] G. Saravanan, P. R. Patil, M.R. Kumar. “Survey on Intrusion Detection System in Heterogeneous WSN Using Multipath Routing.”
IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, no. 2, pp. 26-31, 2014.
[9] U Ghugar, J Pradhan, M Biswal. “A Novel Intrusion Detection System for Detecting Black Hole Attacks in Wireless Sensor
Network using AODV Protocol.” IJCSN International Journal of Computer Science and Network, vol. 5, no. 4, August 2016.
[10] Y. Sabri, N. El Kamoun. “GRPW-MuS-s: A Secure Enhanced Trust Aware Routing against Wormhole Attacks in Wireless Sensor
Networks.” Communications on Applied Electronics (CAE), pp. 2394–4714, 2016.
[11] Singh, M., Das, R. Sahoo. “A Survey of Different Techniques for Detection of Wormhole Attack in Wireless Sensor Network.”
International Journal of Scientific and Engineering Research. vol. 3, no. 10, 2012.
A. Bharathidasan and V. A. S. Ponduru. “Sensor Networks: An Overview.” Technical Report, Dept. of Computer Science, University
of California at Davis, 2002.
[12] M. Tubaishat and S. Madria. “Sensor networks: an overview.” IEEE Potentials, vol. 22, pp. 20-23, 2003.
[13] Priya Maidamwar and Nekita Chavhan. “A Survey on Security Issues to detect wormhole Attack in Wireless Sensor network.”
International Journal on Ad Hoc Networking Systems (IJANS), vol. 2, no. 4, October 2012.
[14] R.H. Khokhar, Md. A. Ngadi, S. Manda. “A Review of Current Routing Attacks in Mobile Ad Hoc Networks.” International
Journal of Computer Science and Security, vol. 2, no. 3, pp. 18-29, 2008.
[15] Jali, Kamarularifin Abd, Jamalul-Lail Ab Manan Zaid Ahmad. “Mitigation of Black Hole Attacks for Aodv Routing Protocol.”
International Journal of New Computer Architectures and Their Applications, vol. 1, pp. 336-343, 2011.
[16] Kumar, V. “Simulation and Comparison of AODV and DSR Routing Protocols in MANETs.” Master Thesis, 2009.
[17] S. Lu, L. Li, K. Lam and L. Jia. “SAODV: A MANET Routing Protocol that can Withstand Black Hole Attack.” 2009 International
Conference on Computational Intelligence and Security, Beijing, pp. 421-425, 2009.
[18] Manel Guerrero Zapata,” Secure Ad hoc On-Demand Distance Vector Routing.” in ACM SIGMOBILE Mobile Computing and
Communications Review, vol. 6, no. 3, pp. 106-107, June 2002.
[19] Chunhui Zhu, Myung J. Lee, Tarek Saadaw. “RTT-Based Optimal Waiting Time for Best Route Selection In Ad Hoc Routing
Protocols.” IEEE Military Communication Conference, vol. 2, pp1054-1059, Oct 2003.
[20] K.U.R Khan, A.V. Reddy, R.U. Zaman, K.A Reddy, T.S Harsha. “An Efficient DSDV RoutingProtocol for WirelessMobile Ad
Hoc Networks and its Performance Comparison.” SecondUKSIM European Symposium on Computer Modeling and Simulation,
India, pp. 506-511, 2008.
[21] B Kannhavong, H Nakayama, Y Nemoto, N Kato. “A Survey of Routing Attacks In Mobile Ad Hoc Networks.” IEEE Wireless
Communication, vol. 14, no. 5, October 2007.
[22] Abhishek Verma and Neha Bhardwaj. “A Review on Routing Information Protocol (RIP) and Open Shortest Path First (OSPF)
Routing Protocol.” International Journal of Future Generation Communication and Networking.vol. 9, pp. 161-170, 2016.
[23] https://en.wikipedia.org/wiki/Open_Shortest_Path_First.
[24] E Kaffashi, Ai Mousavi, H Rahvard. “A new attack on link-state database in open shortest path first routing protocol.” Journal of
Electrical and Electronic Engineering, vol. 3, no. 2-1, pp. 39-45, 2015.
[25] L. Hu, D. Evans. “Using Directional Antennas to Prevent Wormhole Attacks.” 14 Proceedings of the 11th Network and Distributed
System Security Symposium, pp. 2003.
[26] Y. C. Hu, A. Perrig, and D. B. Johnson. “Packet Leashes: A Defense against Wormhole Attacks in Wireless Networks.” In Proc.,
of IEEE -INFOCOM, vol.3, pp. 1976-1986, 2003.
[27] Lazos, Loukas & Poovendran, Radha. “SeRLoc: Secure Range-Independent Localization for Wireless Sensor Networks.” ACM
Transactions on Sensor Networks, vol. 1, pp. 21-30, 2004.
[28] [30] W. Wang, B. Bhargava. “Visualization of wormholes in sensor networks.” Proceedings of the 2004 ACM workshop on Wireless
Security, pp. 51-60, 2004.
[29] Khalil, Saurabh Bagchi and N. B. Shroff. “LITEWORP: a lightweight countermeasure for the wormhole attack in multihop wireless
networks.” 2005 International Conference on Dependable Systems and Networks (DSN'05), Yokohama, Japan, pp. 612-621, 2005.
[30] B. Awerbuch, R. Curtmola, D. Holmer, H. Rubens and C. Nita-Rotaru. “On the Survivability of Routing Protocols in Ad Hoc
Wireless Networks.” First International Conference on Security and Privacy for Emerging Areas in Communications Networks
(SECURECOMM'05), Athens, pp. 327-338, 2005.
[31] N. Song, L. Qian, X. Li. “Wormhole Attacks Detection in Wireless Ad Hoc Networks: A Statistical Analysis Approach”. In
Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, pp. 8-15, 2005.
[32] M.A. Gorlatova, P.C. Mason, M. Wang, L. Lamont, R. Liscano. “Detecting Wormhole Attacks in Mobile Ad Hoc Networks through
Protocol Breaking and Packet Timing Analysis.” In IEEE Military Communications Conference, pp. 1-7, 2006.
[33] H.S. Chiu and K. Lui. “DelPHI: Wormhole Detection Mechanism for Ad Hoc Wireless Networks”. In Proceedings of International
Symposium on Wireless Pervasive Computing, pp. 6-11, 2006.
[34] K.B. Rasmussen and S. Capkun. “Implications of radio fingerprinting on the security of sensor networks.” Third International
Conference on Security and Privacy in Communication Networks and the Workshops, pp. 331-340, Sep. 2007.
[35] Khin Sandar Win. “Analysis of Detecting Wormhole Attack in Wireless Networks”, World Academy of Science, Engineering and
Technology, vol. 48, pp. 422-428, 2008.
[36] F. Nait-Abdesselam, B. Bensaou and T. Taleb. “Detecting and avoiding wormhole attacks in wireless ad hoc networks.” in IEEE
Communications Magazine, vol. 46, no. 4, pp. 127-133, April 2008.
[37] S. Özdemir, M. Meghdadi, and Ý. Güler. “A time and trust-based wormhole detection algorithm for wireless sensor networks.”
(manuscript in Turkish), in 3rd Information Security and Cryptology.
[38] Krontiris, T. Giannetsos, and T. Dimitriou. “Lidea: A distributed lightweight intrusion detection architecture for sensor networks.”
in SECURECOMM ’08: Fourth International Conference on Security and Privacy for Communication Networks, Istanbul, Turkey,
September 22- 25 2008.
[39] S. Choi, D. Kim, D. Lee, J. Jung. “WAP: Wormhole Attack Prevention Algorithm in Mobile Ad Hoc Networks.” In International
Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, pp. 343-348, 2008.

 ISSN: 2722-3221
42
[40] S Özdemir, M Meghdadi, Ý Güler. “Detection algorithm for wireless sensor networks” in 3rd Information Security and Cryptology
Conference (ISC’08).
[41] H. Vu, A. Kulkarni, K. Sarac, N. Mittal. “WORMEROS: A New Framework for Defending against Wormhole Attacks on Wireless
Ad Hoc Networks.” In Proceedings of International Confernce on Wireless Algorithms Systems and Applications, LNCS 5258, pp.
491-502, 2008.
[42] M.S. Sankaran, S. Poddar, P.S. Das, S. Selvakumar. “A Novel Security Model SaW: Security against Wormhole attack in Wireless
Sensor Networks.” In Proceedings of International Conference on PDCN, 2009.
[43] B. Triki, S. Rekhis, and N. Boudriga. “Digital Investigation of Wormhole Attacks in Wireless SensorNetworks.” Eighth IEEE
International Symposium on Network Computing and Applications, 2009.
[44] H. Chen, W. Lou, and Z. Wang. “Conflicting-set-based wormhole attack resistant localization in wireless sensor networks." Book
Chapter Lecture Notes in Computer Science−Ubiquitous Intelligence and Computing, vol. 5585/2009, pp. 296−309, 2009.
[45] D.Barman Roy, R. Chaki, N. Chaki,” A New Cluster-based Wormhole Intrusion Detection algorithm for Mobile Adhoc Networks”,
International Journal of Network Security & Its Applications (IJNSA), vol 1, no 1, April 2009.
[46] H. Chen, W. Lou, X. Sun, and Z. Wang. “A secure localization approach against wormhole attacks using distance consistency.”
EURASIP Journal on Wireless Communication and Net- working- Special Issue on Wireless Network Algorithms, Systems and
Applications, vol. 1, pp.22−32, 2010.
[47] Prasannajit B, Venkatesh, Anupama S, Vindhykumari K, Subhashini S R, Vinitha G. “An approach towards Detection of Wormhole
Attack in Sensor Networks.” First International Conference on Integrated Intelligent Computing (ICIIC), pp.283-289, 2010.
[48] S. Gupta, S. Kar and S. Dharmaraja. “WHOP: Wormhole attack detection protocol using hound packet.” 2011 International
Conference on Innovations in Information Technology, Abu Dhabi, pp. 226-231, 2011.
[49] Z. Zhao, B. Wei, X. Dong, L. Yao and F. Gao. “Detecting Wormhole Attacks in Wireless Sensor Networks with Statistical
Analysis.” 2010 WASE International Conference on Information Engineering, Beidaihe, Hebei, pp. 251-254, 2010.
[50] Bintu Kadhiwala and Harsh Shah. “Exploration of Wormhole Attack with its Detection and Prevention Techniques in Wireless Ad-
hoc Networks.” IJCA Proceedings on International Conference on Recent Trends in Information Technology and Computer Science
2012 ICRTITCS, no. 9, pp. 1-6, February 2013.
[51] K. Patel, T. Manoranjitham. “Detection of wormhole attack in wireless sensor network.” International Journal of Engineering
Research & Technology (IJERT), 2013.
[52] D.S Kushwaha, A. Khare, J. L. Rana. “Improved Trustful Routing Protocol to Detect Wormhole Attack in MANET.” International
Journal of Computer Applications, vol. 62, no. 7, pp. 21-25, January 2013.
[53] C.P. Vandana, A.F.S. Devraj. “MLDW-a Multilayered Detection mechanism for Wormhole attack in AODV based MANET.”
International Journal of Security, Privacy and Trust Management, vol. 2, no. 3, pp. 29-41, June 2013.
[54] S. Goyal, H. Rohil, “Securing MANET against Wormhole Attack using Neighbor Node Analysis.” International Journal of
Computer Applications, vol. 81, no. 18, pp. 44-48, November 2013.
[55] Y. Singh, A. Khatkar, P. Rani, “Wormhole Attack Avoidance Technique in Mobile Adhoc Networks”, Third International
Conference on Advanced Computing & Communication Technologies, Rohtak, 6-7 April 2013.
[56] P. Nayak, A. Sahay, Y. Pandey. “Detection and Prevention of Wormhole Attacks in MANETs using Detection Packet.”
International Journal of Scientific & Engineering Research, vol. 4, no. 6, June-2013.
[57] N. Choudhary, S. Agrawal. “Analysis of Worm-Hole Attack in MANET using AODV Routing Protocol.” SSRG International
Journal of Electronics and Communication Engineering (SSRG-IJECE), vol. 1, no. 10, Dec 2014.
A. Patel, N. Patel and R. Patel. “Defending against Wormhole Attack in MANET.” 2015 Fifth International Conference on
Communication Systems and Network Technologies, Gwalior, pp. 674-678, 2015.
[58] R. Singh, J, singh, Ravindar singh. “WHRT: A Hybrid Technique for Detecting of Wormhole Attack in Wireless Sensor Networks.”
Mobile Information Systems, Hindawi publishing Corpoartion, vol. 2016, no. 13, pp. 1-13, 2013.

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