Tactical approach to identify and quarantine spurious node participation request in sensory application

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 3451~3459
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp3451-3459  3451
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Tactical approach to identify and quarantine spurious node
participation request in sensory application
Somu Parande, Jayashree D. Mallapur
Department of Electronics and Communication, Basaveshwar Engineering College, India
Article Info ABSTRACT
Article history:
Received Sep 6, 2018
Revised Jan 27, 2020
Accepted Feb 8, 2020
Securing Wireless Sensor Network (WSN) from variable forms of adversary
is still an open end challenge. Review of diversified security apprroaches
towards such problems that they are highly symptomatic with respect to
resiliency strength against attack. Therefore, the proposed system highlights
a novel and effective solution that is capable of identify the spurios request
for participating in teh network building process from attacker and in return
could deviate the route of attacker to some virtual nodes and links. A simple
trust based mechanism is constructed for validating the legitimacy of such
request generated from adversary node. The proposed system not only
presents a security solution but also assists in enhancing the routing process
significantly. The simulated outcome of the study shows that proposed
system offers significantly good energy conservation, satisfactory data
forwarding performance, reduced processing time in contrast to existing
standard security practices.
Keywords:
Attacks
Energy
Security
Spurios message
Wireless sensor network
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Somu Parande,
Department of Electronics and Communication,
Basaveshwar Engineering College,
Bagalkot, Karnataka, India.
Email: somuparande63@gmail.com
1. INTRODUCTION
The wireless sensor network (WSN) is developed by MEMS together with communication and
network systems. Previously, the WSN application started with small scale industry, next it is employed to
the large scale real-time application [1]. The WSN is the combination of various and different sensor nodes
which sense the data and collect the information based on the application requirement from the nearby
atmosphere then gathered collected data to its data center through a radio-link medium [2]. WSN utilizes
different types of sensors for sensing event such as temperature sensor, humidity sensor, pressure sensor,
sound sensor (Ultrasonic), air sensor, proximity sensor etc. are participats to senses the sorrounding
information [3]. The sensor, actuator, and computation nodes are the main compontents of the WSN [4].
There are different types of WSN that observed for deploying in many areas such as terrestrial WSN, under
ground WSN, under water WSN, multimedia WSN, mobile WSN [5]. The WSN are highly installed for
many critical and emergence applications such as aireborne, smart military, health monitoring, industrial
process control, security and servilence, habitate monitoring, fire in forest, smart road and smart building,
etc. The WSN technology is more essential for deploying a sensor node in harsh or hostile area where
the wired connection and human interation is not able to organize their specific task such as forest,
under ground, and under water etc. The WSN system is scalable and flexible which is the major advantage
and has some disadvantages that are limited in sensing range, low storage capacity and less power storage
capability [6].
The prime challenge of the WSN is dense sensor node deployement, limited energy capacity, sensor
location, limited hardware resources, random deployment, data aggregation, scalability. Hence, the various
limitation of WSN requires different security challenges to tackle such constraint problems. Big efforts are

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Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3451 - 3459
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applied by many researchers to overcome the security issues in WSN and other constraints of WSN [7-8].
Also, many security protocols are suggested by the researchers and practicioner such as cryptography, secure
routing, and keymanagement scheme and etc [9-10]. Therefore, the proposed paper presents a completely
new security approach that uses non-conventional approach in order to address threats from majority of
the attacks in WSN and imposes cost effective solution with better performance in WSN. Section 1 (a-c)
discusses about the existing literatures where different techniques are discussed for detection schemes used in
power transmission lines followed by discussion of research problems and proposed solution. Section 2
discusses about algorithm implementation followed by discussion of result analysis in Section 3. Finally,
the conclusive remarks are provided in Section 4.
a. The background
This section discusses the previous research work on security, privacy in WSN and network
protocols. The work carried out by Ying Liu [11] have uses S-dLMS (Secure diffution Mean Square)
Algorithm for security over distributed network against various attacks. Tayebi et al. [12] presents a chaotic
DS-SS (Direct Sequence Spread Spectrum) method for improving the security in WSN. Luo et al. [13],
introduced a well-organized Access Control system for security in the cross domain setting of internet of
things. Illiano et al. [14] introduce an Anomal detection scheme with optimization algorithm for WSN event
integrity. Hajji et al. [15], presents an efficient multi routing protocol for maximizing the lifetime of the WSN
by increasing the critical node life. Sun et al. [16], adapts an intrusion detector model based on V- detector
scheme for detection of interference in WSN. Zhang et al. [17], introduces an anonymous key exchange
protocol based on the cryptographic technique (ECC) for addressing security and privacy measures.
Kumar et al. [18], addressed a localization algorithm to secure the sensor node attacks and detection of
malicious anchor node of privileged the network. Li et al. [19], adapts a DV (Distance Vector) hop algorithm
which based on vector routing that offers security against wormhole attacks. Umar et al. [20], presents a trust
based cross layer framework (TruFix) using trust based fuzzy mechanism for security against different types
of attacks in WSN. Guan and Ge [21], addressing the distributed network security in WSN against jamming
attacks with the help of markov jump model. Shen et al. [22], presents an ID (Identity) based comprehensive
signature system for the protection of data integrity in WSN and also for reducing the bandwidth.
Zhang et al. [23], introduces an interference detection based on trust mechanism and state context for
resoiurce overhead and malicious identification in WSN. Rana [24], adapts a convex state approximation
(CSA) algorithm for smart vehilcle cyber attacks. Nurellari and McLernon [25], presents a distributed
detection system using optimum fusion rule for mitigating the security against attacks in WSN. Shafiee [26],
discusses a different comparative method with spectrum sensing (SS) algorithm in cognitive WSN for
the usage next generation WSN. Liu and Dong et al. [27], adapts an active trust system for trustable, efficirnt
and secure routing in WSN which improves the data security. Mehmood et al. [28], introduces a knowledge
based CAS (Context Aware Scheme) for identifying the attacks and dealing the interruption occur in
the malicious node. Li et al. [29], introduces an authentication or security protocol for privacy protection of
internet of things (Industrial feild) using optimal technique. Prasanta Gope [30], presents an authentication
protocol for security in data acces in real time environment using cryptographic techniques.
Faisal et al. [31] have created a novel scheme based on receive signal strength mechanism to counter
the identity replication attack on the IEEE 802.11 based wireless ad-hoc network. The study of
Tayebi et al. [32] have presented improved chaotic based direct sequence spread spectrum (DSSS) technique
in order to enhance the security of chaotic-DSSS dependent WSN. Tian et al. [33] have presented a modified
version of mixed integer and nonlinear programming and gave a joint approach of full duplex and security by
considering cross-layer optimization to improve the energy utilization, spectrum efficiency and to enhance
the security level. The work of Hajji et al. [34] have introduced a novel multiobjective secure routing
protocol for optimizing overall network resource in order to get quality aware data processing, network
reliability and maximum life-span of WSN. Alshinina et al. [35] offers an advanced approach based on deep
learning technique to provide a secure interface between end-user and WSN. The experimental effects
display that it allows for secure data transmission from WSN to end-user with utilizing optimum
network resources.
Kumar et al. [36] focused on the issue related with secure localization of sensor nodes and presented
a secure localization algorithm to protect the sensor nodes from the outsider attack and as well as it also
monitors the insider node to detect compromised node in the network. Luo et al. [37] have presented a secure
and robust Access control design based on certificate-less and id-based cryptography technique for WSN in
the cross Domian framework of IoT. Guan and Ge [38] have designed Markov chain and level switching
based secure model to perform a safe estimation operation under a jamming attack.

Int J Elec & Comp Eng ISSN: 2088-8708 
Tactical approach to identify and quarantine spurious node… (Somu Parande)
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b. The research problem
The significant research problems are as follows:
− Existing approaches are bit inclined towards using encryption techniques that is either mathematically
complex or is iterative in its operation.
− The degree of spruriousness in the control messages are not offered much importance in the existing
approaches causing massive fatalities in the network breach.
− The existing mechanism of trust computation demands updated information which is a bigger challenge
for large scale network.
− Emphasis on practicality as well as computational complexity over processing time is something that is
not much found in existing system.
Therefore, the problem statement of the proposed study can be stated as “It is quite a difficult task to
offer simple and potential security solutions to resist lethal threats in WSN using cost effective computational
approach”. The next section discusses about the proposed system to address this problem.
c. The proposed solution
In order to ensure better robustness towards majority of the security threats, it is essential to identify
the degree of legitimacy of the traffic system. As there are various forms of nodes joins and leaves
the network, it is essential to develop a dynamic trust system. The core ideology of the proposed study is to
evolve up with a simple and progressive strategy to capture all forms of request generated by the adversary as
the first step to stop intrusion over WSN. Adopting analytical research methodology, the proposed system
presents a simple scheme shown in Figure 1.
Figure 1. Analytical scheme of proposed methodology
The proposed system introduces a novel coordinator node system that is meant for minimizing
the network overhead by assisting in enhancing the data transmission rate. Basically, coordinator node is
a special responsibility of a clusterhead that finally takes a decision of finally forwarding the data packet to
the neighboring node or to the sink.It also takes a decision of many other essential operations and hence its
capabilities can be used maliciously by the adversary. Hence, it is essential that coordinator node should be
elected and that rights are retained only within the currently operational clusterhead. The control message has
been worked upon to split the operation of route request for bearing the specific information associated with
updates using routing matrix. A new adversarial model has been introduced by generalizing majority of
the lethal threats in WSN who are more targeting to attack network with higher links and connectivity.
The proposed system introduces a mechanism where the malicious request is identified and then diverted into
different direction thereby wasting resources of the adversarial node. The significant contribution of
the proposed solution is that it offers capability to stop the intrusion in its first attempt itself. The next section
elaborates about the system design.
2. ALGORITHM IMPLEMENTATION
This section offers illustration to the algorithm implemented for the purpose of resisting spurious
network participation request. The design of the proposed algorithm is based on the agenda that any form of
request that is directed to the clusterhead must be subjected to assessment for any form of security breach.

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However, this is absolutely not an easy task and hence the proposed system introduces a new secure
communication scheme for addressing this problem. The proposed system introduces three distinct
algorithms that is responsible for incorporating security as discussed below:
2.1. Algorithm for trust computation
The proposed system constructs a memory in the form of routing table that retains all the successful
occurance of routes most recently as well as in the start of current data aggregation in WSN. This table is
called as trust table and it occupies only 5% of memory consumption irrespective of any size of network as it
only stores information of set of nodes that has prior history of successful delivery of data. The steps
involved in the algorithm are as follow:
Algorithm for trust computation
Input: p, n, Tx
Output: tvalue
Start
1. init p, n, Tx
2. For i=1: n
3. p1p
4. dED(p, p1)
5. If (d>0 && d≤Tx)
6. ttable(i)=1
7. End
8. End
9. For j=1: n-1
10. shfind(ttable(j)==1)
11.End
12. tvalue=[f(sh, ttable)]
End
This algorithm takes the input of p (position of sensor), n (node population), and Tx (transmission
range) which after processing leads to tvalue (trust value). The algorithm considers all the sensors (Line-2) and
obtains the information of positions (p and p1) of any two communicating sensors (Line-3 and Line-3).
The trust table is built only for the communicating nodes and its associated neighboring nodes (condition
highlighted in Line-5). A binarized mechanism of allocating the trust of the node is carried out. It should be
known that such trust computation is carried out by any forms of sensors (clusterhead and member node).
The next task is to obtain the information associated with the node that holds single hop sh neighboring nodes
only (Line-10). Although, the source node is linked with all the forms of node that is connected with both
single and multihop networks, but only the single hop information is retained within the trust table.
This operation has two benefits i.e. i) consumption of lower memory and ii) heuristic information of only
positive trust retained by sensors. An explicit function f(x) is used for taking the inputs from single hop sh
and all connected trust table ttable of other neighboring nodes in order to formulate final trust
2.2. Algorithm for constructing coordinator node list
A coordinator is a special form of sensory role where some of the best clusterheads are selected in
order to perform two operations i.e. i) makes a decision to perform packet forwarding to the selected
clusterhead in its vicinity or sink in vicinity and ii) implements a decision of topological changes when
demanded after every cycle of data aggregation. Therefore, this algorithm maintains a list of all the selected
clusterhead that will be acting as coordinator node and hence they attract the attention of adversary node who
wants to gain an illegitimate access in order to experience the priviledge of coordinator node. The prime
concept of building this algorithm is that an adversary node will be more interested in invoking maximum
attack and for this they will be more curious about nodes with connected multihop network whereas proposed
algorithm will restrict itself to only single hop. That will mean that for any request of network participation,
coordinator node will offer access to a node using single hop only. The benefit of this idea is that even if
the request is found to be malicious afterwards it will inflict its damage over single node and not on complete
network and there by 98% of the network is protected. The significant steps of the algorithm are as follows:
The algorithm takes the input of n (sensor node) and tvalue (trust value). The algorithm considers all
the sensors and extracts information of all the sensors information retained with the memory of ttable in order
o look for multi-hops (Line-2). A multi-hop matrix mh is formed where m=2, 3, 4….This operation is further
followed by exploring single hop links that are found within multihop matrix (Line-4). This operation links

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almost all the nodes and assists in offering much better accessibility while performing routing. In order to
resists forming any form of redundant data, the algorithm extracts only unique information of the both single
and multi hop nodes (Line-7). The proposed system also determine the strength of the coverage of both single
and multihop nodes that is further sorted (Line-10).One interesting fact to be seen in this algorithm
construction is that it is all about retention and accessibility of single and multihop routes so that when
demanded it can offer accessibility as well as retriction too. Accessibility is offered by giving neighboring
information of single hop and restriction is maintained by not furnishing any information of multihop
network to new node nnew.
Algorithm for constructing coordinator node list
Input: n (node population),
Output: cnode (coordinator node list)
Start
1. For i=1:n
2. mhfind(tvalue(i)==m)
3. For j=1:length(mh)
4. ix2unique[find(tvalue(j) = =1)]
5. For k=1:length(ix)
6. hidfind(tvalue(k)==1)
7. covlength(inter(h2, h1)
8. End
9. End
10. cnodesort(cov)
11.End
End
2.3. Algorithm for identifying and isolating attacker
This algorithm is responsible for identifying and isolating the adversary from the entire network.
The algorithm introduces a ghost node that performs diversion of the spurious request originated from
the attacker node. The algorithm takes the input of rreq (route request) that after processing leads to
the identification of the type of the newly participating sensor nodes. The significant steps of the proposed
system are as shown below:
Algorithm for identifying and isolating attacker
Input: rreq (route request)
Output: type(nnew)
Start
1. nnewbroadcast(rreq)
2..For all rreq (nnew)
3. If rreq(nnew)≤1-hop
4. add rreqtemp
5. If rreq(tvalue)~cum(tvalue)
6. tempnew entry in ttable
7. Else if
8. Forward request to gnode
9. gnoderrep (fake_routes)
10. End
11. declare type(nnew)malicious
12. Update tvalue & ttable
13. End
End
The attack mitigation strategy of the proposed system is very unique. The first level of protection
involves formulating a routing table that retains information of diversified hops for active connections only.
After receiving the malicious request of an adversary, the source node compares it with that of local updates,
which will never match. In such case, the source node appoints a non-existing node using IP masking called
as primary ghost node who forwards a fake profiles of IP of node that never exists in the existing network.
As the primary ghost node forwards information in such a way that adversary believes the information
forwarded by ghost node and allocates its resources to achieve it in second level of protection. In third level
of protection, the adversary makes communication with secondary ghost node that is again virtually

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developed by primary ghost node. One interesting point to be observed is that proposed system doesn’t
implement any form of encryption mechanism and it performs simple heuristic-based comparisons to find if
the incoming request is legitimate or not. In case it is found illegitimate than it appoints a non-existing set of
nodes that is means for generating a confusive and non-existing network that leads to degradation of
resources by the attacker and thereby discourage them from any further attacks.
The algorithm initiates with the broadcasting operation of route request (rreq) of the new node nnode in
order to participate in the network (Line-1). At this point, it is yet not known if nnode is genuine node or
malicious node. If the request generated by the nnew is found to be looking for single-hop (Line-3) than
the source node allows an access only to its single hop neighboring nodes provided if the trust value of
the nnew is equal to that maintain in ttable. Otherwise, it doesn’t give the access. However, if the request type
of the nnew is found not to be matching with that of ttable (Line-7) than it is considered as a malicious
node and soon that request is forwarded to the ghost node gnode which then constructs fake nodes as well as
fake routes and allocate them as a route response rrep to the malicious node (Line-11). The malicious
node has no other option than choosing that route request that cost them unwanted resources. Majority of
the existing routing scheme in WSN uses only four types of control message i.e. route_request, route_reply,
route_acknowledgement, and route_error. However, the proposed system makes slight changes in
route_reply by forwarding the reply of controlling the node’s action as consequences of data aggregation.
The route_request message performs declaration of the information related to neighboring nodes of a target
node. The severity of the attack is judged by the intrusion level over the route_request control message.
The broadcasting of the route_reply is carried out by coordinator node periodically and such messages are
only forwarded via coordinator node thereby minimizing the overhead. It was seen from the existing
approaches that they are highly specific to a single form of attack which renders inapplicability of
the existing approaches. Therefore, a new adversarial model is designed in such a way that it incorporates
malicious behaviour of majority of the lethal threats in WSN. In this process, the vulnerable node will try to
perform less broadcasting in order to ensure that they are not much exposing their location and resources to
too many of the sensors. The attacker node, on the other hand, will try to extract more information from such
vulnerable node in order to obtain information of large chain of networks which exists in multihop. For this
purpose, the attacker node broadcast a falsified control message of route_request just to show that they are
available to act as a coordinator node for the target source node. It is because once they become coordinator
node than they will gain rights to use route_reply and start controlling the topology maliciously. Figure 2
presents a schemes to divert malicious node.
Adversary
Update
Routing Table
Attacker Source Node Intermediate Node
Malicious Code
Fake msg
Primary Ghost Node Secondary Ghost Node
Figure 2. Schemes to divert malicious node
3. RESULT ANALYSIS
This section outlines about the simulation outcomes carried out in the proposed research work.
In order to assess the performance of the proposed system, it is essential to understand that proposed study
introduces a novel mitigation strategy for security. It is necessary to ensure that security performance should
be ensured not at the cost of data delivery performance. Therefore, the analysis of security approach is
carried out using some practical parameters e.g. residual energy and throughput. The simulation has been
carried out considering 500-600 sensors bearing MicaZ mote property randomly spread over 1000x1100 m2
simulation area. Analysis of dependency of ghost node is shown in Figure 3.

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Figure 3 shows that there is a reduced dependency of Ghost node to divert the traffic with increase
of node density. Reduction in the percentage of the ghost node also means that there is a reduction in
an event of adversaries. The prime reason behind the reduction of the ghost node is that with the increasing
event of complying with the falsified traffic generated by the ghost node, the attacker waste lots of resources
in a process of performing routing. At the same time, this information of identified rogue nodes are
periodically updated in the routing table which is also accessible by frequently newly joined nodes.
This progressively minimizes need of the ghost node within the traffic system. Minimization of ghost node
will also means reduction of spurios traffic system. Comparative analysis of residual energy. Comparative
analysis of residual energy is shown in Figure 4.
The outcome shows that proposed system offers better energy retention capability as well as better
form of data delivery services as shown in Figure 5. As SecLEACH [39] carry out random key distribution;
hence it undergoes multiple checks just to authenticate the malicious request. Similarly, S-LEACH [40]
speed up this process using pairwise key distribution, however, when the attacks are dynamic, S-LEACH
slowly drains energy. For similar reason, performance of throughput is affected by existing approaches.
Although, they are claimed to be preventive against various threats, such prevention cost is more than
performance anticipated for existing system. The proposed system uses highly progressive approach of
confirming the legitimacy of incoming request to understand the threat level and uses coordinator node which
reduces lot of work load during data aggregation.
From Figure 6, it can be seen that proposed system offers highly reduced processing time as compared to
existing S-LEACH and SecLEACH with increasing simulation rounds. The prime reason behind this is
propose system offers increasing number of alterative which ensures faster secured route selection process
along with better resistivity while existing approaches uses encryption-based mechanism where level of
encryption fluctuates with increasing node over simulation trials.Hence, proposed system consumes
0.223 seconds while existing system consumes 1.75 seconds approximately for processing.
Figure 3. Analysis of dependency of ghost node Figure 4. Comparative analysis of residual energy
Figure 5. Comparative analysis of throughput Figure 6. Comparative analysis of processing time

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4. CONCLUSION
Majority of the attacks in WSN starts by compromising any of the weaker sensor nodes. Normally
that starts from broadcasting the forged information within the network. The proposed system is all building
a secure level of trust from the heuristics of successfully created routes and then it compares the entire
incoming request for network participation. The proposed design principle after finding the presence of
malicious request diverts the request using a ghost sensor. The benefit of this process is that an attacker
has no other option but to rely on the forged information passed on to them by a ghost node. Hence,
without using any form of encryption technique, the proposed system offers good capability to resist
malicious traffic.
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BIOGRAPHIES OF AUTHORS
Somu Parande, completed his B.E from Karnataka University, Dharward, India and M.Tech from
Mumbai University, India. He is pursuing PhD from VTU, Belagavi, Karanataka, India. His
research area is Wireless Sensor Network. He has 18 years of experience in teaching.
Jayashree Mallapur, She has done B.E from Karnataka University, Dharward, India and M.Tech
from Gulbarga University, India. She has completed her PhD in 2009 from VTU, Belagavi,
Karanataka, India. She has 25 years of experience in teaching.

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