MACHINE LEARNING TECHNIQUES FOR …...Learning Techniques for Wormhole Attack Detection Techniques in Wireless Sensor Networks, International Journal of Mechanical Engineering and

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 337–348, Article ID: IJMET_08_09_036

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=9

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

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MACHINE LEARNING TECHNIQUES FOR

WORMHOLE ATTACK DETECTION

TECHNIQUES IN WIRELESS SENSOR

NETWORKS

Er. Harpal

Research scholar, Shri Venkateshwara, University, Gajraula, India

Dr. Gaurav Tejpal

Professor, Shri Venkateshwara, University, Gajraula, India

Dr. Sonal Sharma

Assistant Professor, Uttaranchal University, Dehradun, India

ABSTRACT:

The wormhole attack in Wireless sensor networks (WSNs) decreases the network

performance by dropping the No. of Packets. Many techniques have been proposed to

so far reduce the impact of the wormhole attack by detecting and preventing it. But,

related work indicates that no technique is perfect for every kind of circumstances of

WSNs. Among the existing techniques, Watchdog technique has better performance in

preventing the wormhole attack. It utilizes the local knowledge of the next hop node

and eavesdrops it. If it gets that spending time of the Packet is more than the given

threshold, then it characterizes that node as wormhole attacker. However, this method

has several shortcomings that it does not track the link transmission errors, which

may be because of congestion in WSNs and also it not offers high mobility for

maximum No. of nodes, which eventually decreases the WSNs performance. In order

to handle this issue, a new multipoint relay based Watchdog monitoring and

prevention technique is proposed in this paper. Proposed technique utilizes the

dynamic threshold value to detect the wormhole attacker node, and then clustering

and the Watchdog based optimistic path is selected for communicating the Packets.

Thus, it reduces the overall Packet dropping, which improves the performance of the

WSNs.

Index Terms: black hole, wireless sensor networks, watchdog, multipoint relays.

Cite this Article: Er. Harpal, Dr. Gaurav Tejpal and Dr. Sonal Sharma, Machine

Learning Techniques for Wormhole Attack Detection Techniques in Wireless Sensor

Networks, International Journal of Mechanical Engineering and Technology 8(9),

2017, pp. 337–348.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=9

Er. Harpal, Dr. Gaurav Tejpal and Dr. Sonal Sharma


1. INTRODUCTION

Wireless sensor networks consist of individual sensor nodes, also called motes, deployed in a

given area that cooperatively collect and carry data to a main entity in order to monitor

physical or environmental conditions [1]. The main entity, also denoted as base station or

sink, can be connected to an infrastructure or to the Internet through a gateway, which allows

remote users to access the collected data [2]. The main advantage of wireless sensor networks

lies in the ability to deploy a lot of tiny autonomous motes without any pre-established

infrastructure [3]. After the deployment, motes gather information from the physical world,

and according to a defined communication protocol, they cooperate to deliver data towards

the sink through single-hop or multi-hop communications [4].

Figure 1 Typical wireless sensor network environment

The necessity for more effective security mechanisms for WSNs is increasing due to its

dynamic nature and continues growth in various fields. WSNs are organized in the

unfavorable environments. Different nodes in the WSNs have an unreliable communication

medium which makes it tough to deploy security mechanism [5]. Therefore, security of

different nodes in WSNs is a great challenge against various attacks. A variety of attacks are

possible in WSNs s including jamming, collision, wormhole, flooding, wormhole, sinkhole,

selective Packet drop, Sybil, cloning, denial-of-service, tampering etc. The Wormhole attack

is the most hazardous attack on WSNs [6].

Wormhole attack is the most hazardous attack in the network layer of WSNs. In this type

attack, malicious node acts as a wormhole in the universe [7]. A malicious node drops all of

the data Packets received from source node without transferring to the target node. In

wormhole attack, malicious node introduces itself as a node having the smallest route to the

destination node. Wormhole attack in WSNs is performed by an internal malicious node

which fits in the routes from the source node to destination node [8]. As soon as this

malicious node gets route request from the source node, it introduces itself as a node having

the shortest path to the destination by showing the minimum hop sum No. and maximum

sequence No. By performing this, the malicious node gets the chance to make it an active data

route element [9]. After this malicious node, capable of introducing wormhole attack in

WSNs by dropping all of the data Packets received from the source node [10]. Kim et al. [5]

have proposed a novel wormhole monitoring technique, also known as the algebraic

Watchdog. It allows nodes to monitor selfish behaviors probabilistically and also utilized

overheard Packets to regulate their neighbors locally. In this technique senders play an active

Machine Learning Techniques for Wormhole Attack Detection Techniques in Wireless Sensor

Networks


role in the inspection of the node downstream. Baadache and Belmehdi [6] demonstrated a

novel approach to detect the wormhole attack by using an authentic end-to-end acceptance

based technique to evaluate the exact transmission of data Packets by transit nodes. However,

it has not considered the local information of nodes, therefore, unable to detect those

malicious nodes which have started dropping the Packets in currently.

Yang et al. [7] discussed an Anti-Wormhole Mechanism in order to detect the malicious

behavior of nodes. This technique utilizes wormhole monitoring nodes to detect the wormhole

attacker nodes. These nodes stay in sniff mode in order to monitor the mistrustful value of a

node depends upon the anomalous disparity among the communication Packets transferred

from the node. When a mistrustful value goes beyond a threshold, wormhole monitoring

node(s) will transmit a block Packet, informing other nodes to cooperatively isolate the

wormhole node. Poongodi and Karthikeyan [8] have proposed a method called Localized

Secure Architecture for WSNs. This method utilized security monitoring nodes which will be

activated if threshold value exceeded from the predefined value. If wormhole nodes are

monitored, then security monitoring nodes inform other nodes about the selfish node.

Banerjee et al. [9] proposed an AODV based wormhole attack mitigation technique in

WSNs without modifying the Packet format of AODV and without introducing any wormhole

monitoring Packets. Dasgupta et al. [10] provide a colored petri net model for monitoring and

prevention of wormhole attack in WSNs network. This model modifies a No. of properties

and provides better results as simulated through a CPN tool. Kurosawa et al. [11] in their

work provide a dynamic learning based technique for detecting wormhole attack in WSNs.

This technique is based on using dynamically updated training data for the isolating malicious

node. Jain et al. [12] make use of AODV’s sequence No. for mitigation of wormhole attack in

WSNs without modifying the Packet format of AODV. All the monitoring and prevention are

performed by an originating node without relying on other nodes in the network. Yong et al.

[13] makes use of neighbor set based along with the communication recovery technique for

mitigating wormhole attack in WSNs. Simulation results show that this technique reduced the

overhead of the network. Li et al. [14] in their work present a trust based on demand multipath

communication for isolating wormhole attack. A node’s trust is based upon its Packet

forwarding ratio. In this method, a source node creates numerous reliable paths to a

destination in solitary path discovery.

Namdeo et al. [15] provide an enhanced Watchdog based solution for protecting WSNs

against wormhole attack. In this, malicious node is detected on the basis of Packet flooding

and dropping parameters. Cai et al. [16] proposed a distributed wormhole monitoring system

for adversarial WSNs. This mechanism is used for preventing the networks from numerous

forms of wormhole attack. Packet delivery ratio is enhanced by using this distributed

approach. Imran et al. [17] provide monitoring and prevention technique for isolating

wormhole attack in WSNs. In this technique, DPS nodes are deployed in WSNs, that

uninterruptedly monitors the performance of their neighbor nodes. These DPS notice the

RREQs broadcasted by its neighbor nodes. After checking the No. of parameters of its

neighbor nodes, DPS node declares that suspicious node as the wormhole node and then

broadcast threat Packet on the network. Chatterjee et al. [18] in their work present a technique

for isolating wormhole attack in WSNs by using node stability system. This proposed

mechanism can successfully identify and isolate singular and co-operative wormhole nodes

from the network. Ghathwan et al. [19] introduce an artificial intelligence based technique for

preventing from the cooperative wormhole attack in WSNs. This is an integrated approach

based on both A* and Floyd-Warshall’s procedures. This mechanism works on the basis of

finding a shortest secure path for AODV (SSP-AODV).



Babu et al. [20] discussed a novel honeypot based monitoring and isolation approach for

preventing from wormhole attack in WSNs. This proposed approach reduces the overhead,

Packet drop ratio and the communication load of the network. Kamatchi et al. [21] introduce a

new mechanism based on secret sharing and random multipath communication for preventing

from wormhole attack in WSNs. This Packet reduces the Packet delay and Packet drop ratio

in the network. Mohammed et al. [22] proposed a leader election based wormhole monitoring

system for mitigating wormhole attack in WSNs. For an optical leader election VCG model,

Cluster dependent and cluster independent concepts are used. Ritchie et al. [23] have

demonstrated a COB communication model by using complexity polynomial for preventing

against wormhole attack in WSNs. Performance of this technique is much better when

compared with Dynamic Source Communication. Chang et al. [24] proposed a Cooperative

bait monitoring scheme for protecting WSNs from collective attacks. This scheme works on

the basis of reverse tracing approach for modifying the performance of the network. Djenouri

et al. [25] have utilized Bayesian and social based techniques for mitigating malicious

attacker nodes in WSNs. This approach works on the basis of judgement to isolate the guilty

nodes from the network. Kaushik et al. [26] provide a solution for preventing the network

from both Wormhole and cooperative wormhole attacks. The drawback of modified AODV is

increased overhead.

Gong et al. [27] in their work presented a cooperative immune system for prevention of

WSNs against collective attacks. The concept of probability is used for analyzing and

detecting attacks. Ying et al. [28] discussed the threshold based wormhole monitoring system

for selective wormhole attack in WSNs. In this mechanism IDS nodes are set to sniff mode

for estimating the suspicious value of nodes. Arathy et al. [29] provide the Collaborative

Wormhole procedure for detecting single and collective attacks in WSNs. The proposed D-

MBH and D-CBH mechanisms are used for generating list of single, multiple and collective

wormhole attacker nodes. This approach reduces the computational overhead.

But, the review has shown that [1] - [29] have not focused on finding the link transmission

errors. The link transmission errors may occur in the WSNs because of the packets flooding

and due to maximum No. of nodes. Therefore, existing techniques have certain short coming,

which eventually decreases the WSNs performance. In order to handle this issue, a new

multipoint relay based Watchdog monitoring and prevention technique is proposed in this

paper. The proposed technique utilizes the dynamic threshold value to detect the wormhole

attacker node, and then clustering and Watchdog based optimistic path is selected for

communicating the Packets. Thus, it will reduce the overall Packet dropping, which will

improve the performance of the WSNs.

This paper is organized as: In section 2, the proposed wormhole monitoring technique is

discussed. In section 3, wormhole attack detection for different network layers is discussed.

The Simulation results of proposed technique using the MATLAB 2013a simulator are

discussed in section 4. The comparisons of the proposed technique with available state of the

art techniques are provided in section 5. In the last section conclusion and future directions

are also demonstrated.

2. PROBLEM STATEMENT

From the designed systems, the actual WSN AODV standard technique is adopted from [5].

The clustering based process is utilized in accordance with the good service quality, in which

every single node elects by itself along with the nodes in their transmission range. On

deciding upon cluster head, it is responsible to monitor the No. of multipoint relays (MPRs). It

determines the actual cost for every ith node by the following,


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wpl�a� = sc�a� × 1u�a�

�� (1)

Where sc�a� is actually the remainder of the mileage to get out of this path i.e. may be the

1-hop neighbor nodes in the similar route, Thus, the typical quickness from the ith node.

In comparison to [5] it is considered that the nodes for election simply for elected CHs of

which discuss its going direction. As soon as the attached CHs usually are determined, exactly

same solution is utilized in [5] to find the MPRs. As said before, wormhole strike reasons

package lower throughout the vast majority of the navigation techniques. From the put into

practice method, them goals MPR nodes, creating a considerable effect on multi-level

connectivity. For example, it is assumed that about 10% from the MPRs being malicious. It is

remarked that there were totally associated clustering with the direction-finding view prior to

assault took place, and everything the particular nodes can talk collectively easily.

Nevertheless, the moment vicious nodes are available; the particular amount of shut off

groupings retains escalating provided that quantity of nodes increases. This can be just

because which, if circle grows more packed, the particular nodes grow to be nearer together

plus associated by means of additional MPRs. For that reason, the quantity of opponents will

increase, which degrades the particular circle connectivity. This particular occasion illustrates

the necessity to establish a discovery process that may diagnose arsenic intoxication vicious

vehicles. Watchdog based mostly techniques usually are put in place inside related work [17].

The fact is that this kind of tracking tactics has got 2 key drawbacks. Very first, they

cannot separate whether or not some sort of small fortune falling function is a result of several

vicious assaults or merely legit causes, like small fortune collision. The 2nd challenge takes

place if Watchdogs have got troubles, although hearing panic or anxiety attack, as well as

accuses simple nodes to generally be misbehaving. To handle these issues, multipoint relay

based Watchdog monitoring and prevention technique is proposed in this paper. The proposed

technique will utilize the dynamic threshold value to detect the wormhole attacker node, and

then clustering and then Watchdog based optimistic path will be selected for communicating

the Packets. Thus, it reduces the overall Packet dropping, which improves the performance of

the WSNs.

In an effort to address the impact of the wormhole attack in WSNs, it demands to enhance

the performance of the well-known Watchdog monitoring scheme in tackling the collision

problem while monitoring. It should be noted that our main threat model assumes malicious

behavior from the MPR nodes during a Packet exchange among several nodes in the network.

The classical Watchdog technique fails to differentiate between collisions and attacks. A

remedy to this problem is the use of CL-layering, where different monitoring nodes from

different layers cooperates to enhance the performance of the single monitoring scheme.

Thanks to the cooperative and Cross Layer (CL)-layering features, any falsely reported

attacks can be handled. In this work, our main focus is to propose the cooperative CL layer

monitoring framework, where the Watchdogs are selected randomly. Finally, attacks prior to

Packet transmission specifically during broadcast transmissions can be detected utilizing the

Watchdog without the need to adopt any CL layering schemes as proposed in [17].



3. MACHINE LEARNING BASED ATTACH WORMHOLE ATTACK

DETECTION

Table 1 Nomenclatur used

��

�� 10 � !! "!#$ℎ

& 2 () )* $ !!+

&,-_/$ 100 1,-/&2& /$! ,$/)�+

3))# 1 4))# +$, $/�5 * )& 1

61 2.1 Velocity factor 1

62 2.05 Velocity factor 2

-_#!,9 100 1,-/&2& ,�5! :,32!

;1, ;2 6 ;,�")& �2&=! +

>$!# 1: − 4)," ′,$$,B9_C+�. D **′ ",$, */3! /�$) $ℎ! &!&) E. >$!# 2: − D##3E 2�+2#! :/+!" */3$! /�5 )� $, 5!$ ,$$ /=2$! ,�" B)�:! $ /$

* )& �2&! /B $) �)&/�,3��; >$!# 3: − ()C +#3/$ )#$/)�+ , ! B)&! /� ,B$/)� $) */3$! $ℎ! ",$, /�$) 30

/70 *) &,$ /. !. 30 ",$, ,+ $ ,/�/�5 ,�" 70 ",$, *) $!+$/�5. >$!# 4: − ()C +!$ !K2/ !" B)�+$,�$+ >$!# 5: − L�/$/,3/M! ,�")& =!!+ ,�" 53)=,3 =!+$ ,�" 3)B,3 =!+$ :,32!+. N_O>P = ,�"��, &�; Q = ,�"��, &�; 3)B,3 =!+$ = ,�"��, &�; 53)=,3 =!+$ = ,�"�1, &�; >$!# 6: − ;!#!,$ $ℎ! +$!#+ 6 $) 10 Cℎ/3!�3))# <= &,-_/$� *) , = 1 $) �� *) = = 1 $) & ;1 = ,�"; ;2 = ,�"; >$!# 7: − T:,32,$! :!3)B/$E :_�$ = Q�/, U� + ;1 ∗ 61 ∗ �3)B,3 =!+$�/, U� − N_O>P�/, U�� + ;2 ∗ 62 ∗ �53)=,3 =!+$�1, U� − N_O>P�/, U��; >$!# 8: − 6ℎ!B9 =!+$ =!!+ 2+/�5 *)33)C/�5 B)�"/$/)�+: − /* :_�$ > :_#!,9 Q�,, =� = :_#!,9; !3+!/* :_�$ < −:_#!,9 Q�,, =� = −:_#!,9; !3+! Q�,, =� = :_�$; !�" N/�$! = N_O>P�,, =� + Q�,, =�; /* N/�$! > -_#!,9 N_O>P�/, U� = -_#!,9; !3+!/* N/�$! < −-_#!,9 N_O>P�/, U� = −-_#!,9; !3+! N_O>P�/, U� = N/�$! ; !�" >$!# 9: − ()C B,33 &!$, ,35) /$ℎ& /. !. >Q1�−O 100 − 4 − 1.7976931348623157T308 − [ 1.0 − \ 3.0 −P 1 − T 1 − > 1 − L 10 − ] ^!B/+/)�>$2&# ′� �; >$!# 10: − ()C 2#",$! >Q1 :,32!+ 2+/�5: − *) / = 1: �� # !+_N = */$_&�N_O>P�/, : ��; # !+_3)B,3 =!+$ = */$_&�3)B,3 =!+$�/, : ��; # !+_53)=,3 =!+$ = */$_&�53)=,3 =!+$�1, : ��;


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/* # !+_N < # !+_3)B,3 =!+$ 3)B,3 =!+$�/, : � = N_O>P�/, : �; !�" /* # !+_N < # !+_53)=,3 =!+$ 53)=,3 =!+$�1, : � = N_O>P�/, : �; !�" !�" ! �3))#� = # !+_53)=,3 =!+$; /* # !+_53)=,3 =!+$ <= 10^�−30� 3))# = &,-_/$; !3+! 3))# = 3))# + 1; !�" !�" O>P_/"- = B!/3�# !+_53)=,3 =!+$�; >$!# 11: − ()C "!*/�! ,$$ /=2$! $E#!+ ,+: − /. !. ′�2&! /B′, ′�)àbc ′, ′+$ /�5′, ′",$!′ ,�" ′ !3,$/)�,3′. >$!# 12: − ()C !:,32,$! �)àbc ,$$ /=2$! Qdef �)àbc3Qdef = B!33�1: �ghhi , O>P_/"-�; *) / = 1: �ghhi

/* +$ B&#/�,$$ /=2$!�E#!+{/}, ′�)àbc ′� �)àbc3Qdef{/} = , ,E*2��@�9� Bℎ, �^. ,$$ /=2$!�/ − 1�. Qdef�9 − 1��, 1: ^. ,$$ /=2$!�/ − 1�. �Qdef�; !�" >$!# 13: − ()C "!*/�! /�+$,�B!+ ",$, = 0; *) / = 1: �ghhi ",$,�: , /� = ^. ,$$ /=2$!�)D ,E�/ − O>P_/"-�; !�" >$!# 14: − B3,++m $!+$ abnh

�2&L�+$ = $!+$. �2&L�+$,�B!+��; #ifo = 0; #ifo Oipqn = 0; *) / = 1: �rbnh

# !"�/� = B3,++m /�+$ �$!+$ abnh�/ − 1� �;

#ifo Oipqn�/, : � = "/+$ /=2$/)�_*) _ /�+$ �$!+$ abnh�/ − 1� �; !�" >$!# 15: −()C B,33 ,�")& *) !+$ D35) /$ℎ&�B 3,+$ − 6 .9 − 1 2 ′� � >$!# 16: − B3,++m $!+$ abnh

�rbnh = $!+$. �rbnh��; #ifo = M! )+��rbnh , 1�; #ifo Oipqn = M! )+��rbnh , $icab_". �sdcnn��; *) / = 1: �rbnh

# !"�/� = B3,++m . B3,++m /�+$ � $!+$ abnh�/ − 1� �;

#ifo Oipqn�/, : � = B3,++m . "/+$ /=2$/)� t) L�+$,�B!� $!+$ abnh�/ − 1� �;

>$!# 17: − ()C !:,32,$! */�,3 )2$B)&!+. ;!$2 � */�,3 +)32$/)�

4. PERFORMANCE ANALYSIS

In order to assess the efficiency and competence of the proposed technique, i.e. proposed

technique and other some well-known wormhole monitoring techniques, MATLAB based

simulation is done for WSNs coding organizations and run wormhole monitoring and

prevention techniques. The existing and proposed wormhole monitoring techniques are

implemented on a Windows (2.4 GHz Intel i7 processor with 4 GB RAM and 1 TB memory).

The parameters used for simulation are shown in Table 1.



Table 1 Simulation parameters

Parameter Value

Simulator used MATLAB 2013a

Simulation duration 4000 seconds

Area (meter) 100X100

No. of nodes 200

Communication technique AODV

Channel type Wireless

Packet size 4000 bytes

Mobility model Two ray ground propagation models

This section represents the comparison between some well-known wormhole attack

detection techniques with the proposed technique. The Average download efficiency is taken

as primary quality metric for comparison. It represents that how many packets are

successfully delivered within a given time. End-to-end delay is the mean time taken by a data

Packet to travel from source node to the destination node. This average time includes any type

of delay due to route discovery process along with a queue in data Packet transmission. In

this, only those Packets are included which are successfully transferred to the destination

node. This is calculated as:

ᵟ=u�vwx�

u�y� (22)

Where λ =Arrive Time, μ = Send Time, and η = No. of Connections.

Thmorer value of the Average download success rate is an indicator of the better

performance of the technique. Figure 8 shows the Average download success rate delay

comparisons of proposed technique with some existing approaches for preventing WSNs from

wormhole attack. It demonstrates that the proposed technique results in the increase in

average download success rate.

Figure 2 Average download success rate of proposed technique

The arrival time of the final packet is defined as the additional time taken to deliver

Packets at the destination. The arrival time of the final packet in the mobile WSNs network is

increased due to malicious node. The proposed approach results in decreasing the arrival time


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of the final packet of WSN as compare to existing procedures used for isolating the wormhole

attack as illustrated in Figure 3.

Figure 3 The arrival time of the final packet of proposed technique

Accuracy is the failure of transferring Packets to reach the destination. It happens due to

network congestion or some attacker node in the network. Accuracy is responsible for

reducing the Packet delivery ratio. It is calculated as:

Accuracy= δ-ρ (24)

δ= No. of Packets send from source and ρ= No. of Packets received at the destination.

Figure 4 Accuracy of proposed technique

Figure 4 shows the Accuracy comparisons of proposed technique approach with existing

procedures used for preventing WSNs. The figure clearly shows that the proposed technique

results in the decrease in Accuracy.

The Average download efficiency may increase if the attacker node is detected as early as

possible.



Average download efficiency is defined as

Average download efficiency = u�

� (21)

Where ρ =No. of Packets received at the destination, ϒ=Simulation time

Figure 5 shows the Average download efficiency analysis of proposed technique with

some existing techniques. It depicts that the proposed technique after isolation of malicious

node results in the increase of Average download efficiency.

Figure 4 Average download efficiency comparison

5. CONCLUSION

The wormhole reduces the performance of the network a lot. Among the existing techniques,

Watchdog technique has better performance in preventing the wormhole attack. It utilizes the

local knowledge of the next hop node and eavesdrops it. In Watchdog technique, if the Packet

exchange time exceeds the threshold then node is marked as malicious. But, it has several

short comings; one of them is that it is unable to monitor link transmission error. In order to

handle this issue, a new multipoint relay based Watchdog monitoring and prevention

technique is proposed in this paper. The proposed technique utilizes the dynamic threshold

value to detect the wormhole attacker node, and then clustering and Watchdog based

optimistic path is selected for communicating the Packets. The proposed technique is

designed and implemented in the MATLAB 2013a tool. Comparisons have been drawn with

recently proposed techniques for monitoring and preventing against wormhole attack. The

performance analysis has clearly indicated that the proposed technique outperforms over the

available techniques. Thus, proposed technique has reduced the overall Packet dropping,

which improves the performance of the WSNs.

The authors declare no conflict of interest.

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