Sinkhole Attacks in Wireless Sensor Networks
LM386 - Master of Engineering in Electronic
and Computer Engineering
Project Interim Report
Philip Hogan
20133359
Supervisor: Dr Karl Rinne
24-10-2022
Abstract
A wireless sensor network is an autonomous network with decentralized management. The
sensing apparatuses are frequently referred to as nodes. These nodes are fairly affordable
and quite compact. These networks were mostly only set up in military zones to keep an eye
on the activities of opposing parties. Every adversary movement was tracked, and the system
utilized this crucial information to determine the best course of action. Because of the wide
geographies, it can be quite difficult to monitor suspicious actions and movements in specific
apps. In these kinds of applications, the utilization of wireless sensor networks is therefore
quite beneficial. Wireless sensor networks are employed in many different applications
nowadays. These networks are capable of carrying out various activities like information
sensing, processing, and sharing within the areas. The monitoring area is set up by the
wireless sensor network to check for a haphazard arrangement of nearby sensor nodes. In
areas that are unsuitable and don't require any sort of infrastructure, wireless sensor
networks are built. My project will be focusing on the implementation of the wireless sensor
networks in curbing the increased attacks in sinkhole attacks in recent years. In many cases,
the sinkhole intrusions mostly cause a lot of data loss and interference in the network thus
causing downtime in the network. A new technique is presented in this research paper for
the identification and exclusion of attacker nodes from the network. Identity validation is the
foundation of the new method. Thus, our implementation will focus on targeting these
intrusions and also carry out a comparison with other techniques so as to find out the
efficiency of wireless sensor networks. The proposed algorithm is applied in Network
Simulator 2, and the results are evaluated based on several criteria. In comparison to existing
systems, the examined given methodology outperforms other techniques like group
management, intrusion detection system, and secure data aggregation on every parameter.
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Declaration
This interim report is presented in part as the fulfillment of the requirements for the LM386
Master of Engineering in Electronic and Computer Engineering Masters Project.
It is entirely my own work and has not been submitted to any other University or Higher
Education Institution or for any other academic award within the University of Limerick.
Where there has been made use of work of other people it has been fully acknowledged
and referenced.
Name
Philip hogan
Signature
Date
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Table of Contents
ABSTRACT .........................................................................................................................................................I
DECLARATION .................................................................................................................................................III
TABLE OF CONTENTS....................................................................................................................................... V
LIST OF ACRONYMS AND ABBREVIATIONS .................................................................................................... VII
INTRODUCTION ............................................................................................................................................... 1
LITERATURE SURVEY ........................................................................................................................................ 3
MOTIVATION FOR THE PROJECT ...................................................................................................................... 7
BACKGROUND THEORY .................................................................................................................................. 10
ACTION PLAN ................................................................................................................................................. 12
CONCLUSIONS AND FUTURE WORK ............................................................................................................... 14
REFERENCES................................................................................................................................................... 16
APPENDICES.................................................................................................................................................- 1 APPENDIX A: PROJECT GANTT CHART ..........................................................................................................- 3 APPENDIX B: INTERIM PRESENTATION SLIDES .............................................................................................- 5 -
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List of Acronyms and Abbreviations
LEACH - Low Energy Adaptive Clustering Hierarchy
CH - cluster head
AODV - Ad-hoc On-demand Distance Vector
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Introduction
The wireless sensor network combines different sensing nodes or devices to gather data
about the surrounding circumstances in a specific area. The nodes, which are also known as
sensing devices, are very cheap and compact. These networks were mostly only set up in
military zones to watch over the actions of opposing forces. Every adversary movement was
tracked, and the system utilized this crucial information to determine the best course of
action. Because of the wide geographies, it can be quite difficult to monitor suspicious actions
and movements in specific apps [3]. Wireless sensor network exploitation is therefore quite
helpful in these kinds of applications as its use lately covers a wide range of implementations.
Such networks are capable of carrying out various activities like information sensing,
processing, and sharing within the areas. The monitoring area is set up by the wireless sensor
network for the haphazard distribution of sensor equipment there. Numerous concerns arise
as a result of the extensive and unfavorable applications of these networks.
These sensor nodes are tiny and have a minimal amount of battery power. These networks
are set up for subversive uses. Because these areas are inhospitable to humans, it is
impossible to monitor their activities. These types of nodes are more expensive to deploy
than nodes in other places of the world. The diverse microphones and cameras are installed
inside the multimedia sensor networks using cheap sensor nodes [5]. Larger bandwidth, more
power, and higher quality of service are some crucial elements required for effective
information processing. To produce a sparse environment, sensor nodes are put in auditory
zones for the deployment of networks. Inhibitions on the wireless sensor network include
signal fading, delay, and propagation. In areas that are unsuitable and don't require any sort
of infrastructure, wireless sensor networks are built [7]. For the collection of information, a
certain region must have a properly distributed network. Monitoring such areas is essential
for the mutual collection of all pertinent information and is necessary for broad scrutiny. The
two important mechanisms operating inside wireless sensor networks are aggregation and
base station. The sensor nodes located in the surrounding surroundings are used to collect
the data. Other nodes receive this info via transfer. This information is transmitted to the
dominion by these nodes.
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Literature Survey
Noor Alsaedi [4] claimed that due to advancements in technology, wireless sensor networks
were used in a variety of applications. Due to the network's dynamic topology, sensor nodes
were placed randomly inside it. This network occasionally fell under the control of another
system due to problems like the sensor devices' insufficient processing power and battery
limitations. Due to the open communication environment this network offered, it was
susceptible to various types of intrusions. The wireless sensor network's performance was
impacted by several intrusions. The Sybil attack was regarded as one of the most significant
invasions among these [4]. Due to the occurrence of several identities generated from the
attacker node, intrusion presence caused a disruption within the entire setup. For the
purpose of minimizing all the issues when power was used as a metric parameter for
hierarchical wireless sensor networks, a lightweight trust system was presented in this study.
To determine how well the suggested strategy performed, several experiments were run. The
tested results showed how effective the suggested method was. This suggested method was
seen to reduce the network communications overhead.
BinZeng [12] claimed that a wide range of applications used wireless sensor networks. These
networks were more susceptible to breaches because wireless sensor networks offered
unfettered communication. Security was therefore viewed as being essential in these
networks, such as peer-to-peer networks. These networks were impacted by a number of
intrusions, including the Sybil attack. In this attack, the distributed database system generated
a large number of bogus identities and demonstrated the presence of various system nodes
that had been alienated. An edge between the Sybil node and the truthful node existed when
the attacker node attempted to trick the truthful node. In this work, a novel technique for
reducing the impacts of Sybil intrusion was put forth [12]. An algorithm called ant colony
optimization (Am) was used in this suggested approach. Nodes in this approach were
randomly distributed throughout the network and were free to leave or join at any time. On
each node, the traces from the first node left started to deteriorate. The suggested technique
allows for a successful and efficient limitation of the amount of edge incursion. As a result,
the suggested method made sure that the genuine node had a high probability of being
accepted and that the Sybil nodes were eliminated at a higher level.
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Salavat Marian [6] said that popular applications had made substantial use of wireless sensor
networks. Due to the open communication environment this network offered, it was
susceptible to various types of intrusions. Wireless sensor networks' ability to function was
impeded by several breaches. The Sybil attack was regarded as one of the most significant
invasions among these. Malicious nodes sent packets to several nodes with fictitious
identities during this incursion in order to establish their presence in the network. Once it had
gained access to the network, this intrusion led to a number of further attacks. An effective
safety counter was suggested in this investigation [6]. The suggested method made use of the
RSSI methodology for simple Sybil attack detection. The earlier methods that were
demonstrated relied on random key distribution. Two indicators known as RSSI and LQI were
employed in the network to measure quality connection-wise. Several tests were conducted
to assess the effectiveness of the suggested technique, and high-quality transceivers were
used to gauge its effectiveness in the stationary atmosphere. For use in nearby Sybil nodes,
the wireless channel models state that the acknowledged energy should depend on distance.
Ruixia Liu, et.al (2014) claimed that a number of applications had made substantial use of the
developing technology known as body sensor networks (BSN). Technology had a significant
impact on people's way of life. Data on the client's mental health and privacy were two factors
connected to this network. Security was therefore seen as one of the key threats in these
networks. Because so many node identities were used in this network as messaging mediums
for the delivery of information, Sybil found it simple to interfere with the network's ability to
function [10]. In this experiment, a novel RSSI was introduced to identify every Sybil node that
was already present in the network while it was adjusting its broadcasting energy. Thus, when
compared to other methods currently in use, the proposed strategy displayed improved
performance. Each node kept a copy of its identity certificate. Therefore, no symmetric key
encoding method was needed for this strategy. To assess the effectiveness of the suggested
technique, numerous simulations were run. These simulations evaluated the suggested
technique's effectiveness using a high finding rate and low running expense.
Imran Makhdoom [11] showed how the unfettered information sharing offered by wireless
sensor networks made them more susceptible to various types of invasions. The performance
of this network was affected by a number of intrusions, including wormhole, Sybil, black hole
assault, and others. The conventional cryptographic method offered protection against
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external incursion, but it was unable to limit internal intrusions caused by nodes that were
hacked [11]. Due to the presence of malicious nodes, the Sybil breach was considered one of
the most significant intrusions among all other intrusions. All suggested methods were
evaluated for their ability to reduce this kind of infiltration. The One-Way Code Attestation
Protocol employed in wireless sensor networks was looked at in the paper so that it can
identify the benefits and drawbacks and a great success was met based on its suggestion as a
strategy since it reduced several significant intrusions in addition to the Sybil intrusion which
was prevalent in this network.
Annie Mathew [13] claimed that the presence of a significant number of sensor devices
provides a direct conduit for intelligence exchange from source to target. The sensing process
was made easier with this method. Due to the networks' promotion of open communication,
security was the key concern in these networks. This network's ability to function was
compromised by a number of incursions, including sinkhole, wormhole, and gray hole
attempts. Due to the occurrence of sinkhole intrusion into the network, the sinkhole node
developed a direct route between the sink or destination node [13]. There have been a
number of approaches developed thus far for the efficient detection of sinkhole intrusion.
This paper explored sinkhole invasion, including its classifications and methods for the
discovery of this invasion using specific criteria.
Mahmood Alzubaidi [14] reiterated that numerous internal breaches of various kinds
happened in a wireless network, it was revealed. The major goals of this study were to identify
sinkhole incursion and determine how it affected the RPL. In this work, many IDS
methodologies and procedures were suggested for the quick detection of sinkhole attacks.
The true alarm rate and resource usage, along with each method's benefits and drawbacks,
were highlighted in this study [14]. In this study, a chart was shown to provide an earlier
illustration of sinkhole invasion detection techniques. To determine which method was the
most effective, several comparisons were made.
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Motivation for the Project
In recent years, there is a rise in the utilization of wireless networks all over the world and the
invasions specifically sinkhole attacks on the wireless networks have become a major concern
in terms of network breaches. Sinkhole attacks involve the attacker luring nearby nodes with
faked routing information, followed by selective relaying or data manipulation. The attacking
node asserts that it is providing a very alluring link. As a result, this node is skipped by a lot of
traffic. The sinkhole method can be paired with other threats besides straightforward traffic
monitoring, such as selective forwarding or denial of service. A sinkhole is created in the
middle of the node that has been hacked by luring all the traffic from its surrounding area. All
of the neighboring node's data is drawn to the intruder or hacked node [15]. The assailant
makes an effort to project the image of being the neighborhood's most appealing relay. In an
AODV-based VANET, a reputable solution against sinkhole attacks was put out to identify
sinkholes. In this technique, the type of association between nodes is utilized to determine
the nodes' route once the node receives route reply messages from its neighbors. As a result,
it will result in the discovery of sinkhole nodes for which no preferred route may be chosen.
Two game theoretic strategies—cooperative and non-cooperative—have been discussed for
handling VANET security. In this study, game theoretic methods for resolving security
challenges in VANET, such as cooperative and non-cooperative games, are described.
The information in the wireless sensor network is sensed by the detecting devices. The sensed
data is then sent to the sink from there as it is a type of distributed network that lacks
centralized management. This circumstance is to blame for attacker nodes entering the
network. Diverse active and passive network incursions are started by these attacker nodes.
The infiltration of the sinkhole reduces the effectiveness of the network in terms of
throughput, power consumption, and packet loss. Through connection, the sinkhole incursion
floods the attacking nodes in the conduit [17]. The finding of an attacker node, which further
initiates the sinkhole incursion into the network, is the basis for this inquiry. the attacker node
must be isolated. This node is in charge of starting the network's sinkhole intrusion. to gauge
the wireless link's threshold for data packet transmission. to put into practice the suggested
strategy and evaluate it against earlier strategies in terms of several aspects.
Conventional wireless systems merely scratch the surface of what is possible when low-power
connectivity, detection, power storage, and processing are combined. Typically, when people
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think of wireless gadgets, they picture things like cell phones, PDAs, or laptops with 802.11
wireless technology. These items demand considerable pre-deployment infrastructure
assistance, cost hundreds of dollars, and target specialist applications. On contrary, wireless
sensor networks use modest, affordable connected systems for a range of purposes without
relying on any which was before infrastructure and these devices cost less than a dollar [16].
Instead of communicating with the closest high-power command center or central node, as
is the case with the ancient wireless devices, the implementation of this method of using
wireless sensor nodes imposes an engagement only with the local peers. Thus each sensor
integrates as part of the overall infrastructure rather than relying on a pre-deployed
infrastructure. This also implements a peer-to-peer communication protocol that furnishes a
mesh-like interface for multi-hop data transfer among the many small embedded devices.
The envisioned adaptive mesh designs can expand to permit the coverage of a wider area or
the addition of new nodes. To compensate for link failure, the network can also dynamically
make adjustments.
Strength in numbers is the foundation of the mesh networking concept. Unlike cell phone
networks that block off communication if there are excessive operational phones on a
minimal containment, a wireless sensor network's connectivity grows gradually with the
addition of new nodes provided the density is accommodative leading to an indefinite growth
of one network of nodes. Since the cost of an individual node is less than a dollar and has a
communication range of 50 meters, creating a sensor network around the equator can
accumulate to a cost of fewer than one million dollars. It presents the use of precision
agriculture as an active field of practical study [16]. A field of hundreds of dispersed nodes
comes together resulting in routing topology formation and sending data rearwards to the
point collection. A wireless sensor network fully satisfies the application requirements for
reliable, scaleable, affordable, and simple-to-deploy networks. The data transmission will
continue to be carried out by the overall network regardless of any failed nodes, instead, a
new architecture would be chosen. More nodes in the field just increase the number of
potential routing possibilities. Therefore, with the implementation of my solution with regard
to wireless sensor networks will not only curb the cost but also provide a more efficient and
more direct approach to mitigation of sinkhole intrusions.
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Background Theory
Wireless sensor networks are an evolving topic that integrates sensing, processing, and
connection into a single compact device. These gadgets create a pool of connectedness
responsible for expanding into the real world the boundaries attributed to cyberspace and all
thanks to sophisticated mesh communications protocols. The mesh networking
interconnectivity will look for and use any available network connection through data hopping
between nodes in quest of its target, much like a situation where a sunken ship is filled by
water flow. The powers of any one gadget are extremely limited, but the combination of many
devices opens up completely new technological possibilities [18]. The capacity to deploy a
huge number of micro nodes that self-assemble and construct wireless sensor networks gives
them their power. These devices can be used for a variety of applications, including real-time
tracking, surveillance systems, cloud computing, and on-site inspection of machinery or
infrastructure.
They might control actuators that stretch cyberspace-based management further into the real
world and are frequently referred to as wireless sensor networks. From our daily living, the
most common use of wireless sensor network technology is the tracking of low bandwidth
patterns in remote environments. For instance, a chemical factory might be equipped with
effective leakage monitoring by its implementation with the use of hundreds of sensors that
instantly initiate a network connection that is non-wired and instantly report any incident of
chemical leaks. Costs of installation would be significantly lower than for traditional wired
systems. At each sensing point, installers simply need to insert a quarter-sized gadget rather
than miles of cable flowing through shielded infrastructure. By merely putting more devices,
the network may be expanded progressively without any more work or complicated settings.
The system will be able to monitor abnormalities for several years using the gadgets described
in this thesis and a single set of batteries [17]. Wireless sensor networks may constantly adapt
to changing conditions and significantly cut installation costs. Network topologies can change,
and adaptation mechanisms can react to those changes by forcing the network to operate in
fundamentally different ways. For instance, the embedded network that tracks leaks in the
chemical sector could be changed into a system that tracks harmful gas leaks and determines
their source. The infrastructure could then direct employees appropriately in case of an
emergency.
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Action Plan
I will be carrying out my project by carrying out simulation with various techniques that entails
the mitigation and remedy oh sinkhole attacks in wireless networks. This will provide a view
on the great significance that the wireless sensor networks have over the other techniques.
Pre-processing is the initial step, during which a certain number of sensing devices are
configured into the wireless sensor network. The entire network uses locality-based
clustering. The suggested LEACH methodology accurately verifies the strength and distance
of each device. The node with the greatest power and the shortest distance is chosen as the
CH. The CH will receive information from all of the network's nodes. With the assistance of
other CHs, CH further forges a conduit and directs this information toward the sink. The
gateway from the origin to the target is made using the AODV routing protocol [19]. The
source node protocol known as AODV floods the route feedback packets. The source node
chooses the best path to the destination based on the highest sequence number and hops
count. The information is forwarded by the source node to the desired location. Using the
chosen route, the attacking node starts the mid-directional incursion.
After that, we carry out attacker node detection, where a number of methods have been put
forth in recent years to find attacker nodes. The monitor mode method was the previous
approach. With the aid of this technique, the surrounding node's activity can be seen. This
approach performs poorly when it comes to identifying the attacking node. The delay
tolerance method was the second technique used in the earlier inquiry. For the purpose of
finding attacker nodes, this approach requires additional hardware and software. This makes
the arrangement more complex and expensive. The node localization method is used by the
base station to identify and separate attacker nodes. The node localization method collects
information about the existing route. With the use of the node localization method, the sink
is able to gather all of the data from sensing devices [20]. The position of the sensor node and
its delay during information transmission can both be gathered by the sink. The restrictions
on service quality are examined by the sink. The base station responds to the discovery
attacker when the network throughput falls below a predetermined threshold level. Every
hop of the network is examined by the base station for the presence of the attacker node.
The node that falls below the threshold level in throughput is labeled as an enemy. The
distance between each node and the sink is included in the data collected. Every hop count
that is present on the constructed route experiences a delay due to the distance. This delay
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is noticed by the base station. Every hop's delay is evaluated in order to determine which
node will increase network delay and identify attacking nodes.
The attacker node is identified when the estimated delay is greater than the anticipated delay.
The anticipated delay is the threshold delay. The network's threshold delay is 2 milliseconds;
if a sensor node increases its latency above this threshold value, which is 2 milliseconds, then
it is considered to be an adversary. For the purpose of removing attacker nodes from the
route, the multipath routing mechanism is used. The proposed solution adheres to the
existing used methods and the threshold scheme for attacker node discovery. Since this
approach is favored for the finding of attacker nodes, the proposed approach does not require
any further hardware or software elements.
Finally, we perform the separation of malicious nodes, which is the process by which the
assailant node is eliminated from the path created between the source and the target. The
attacker node is eliminated using the multipath routing technique. The attacker node is
segregated from the network by the multipath routing method. This method involves flooding
the network with RREP, to which any node nearby the destination responds with RRPs. Based
on the count of hop and sequence figure, the origin node selects the best path from source
to destination. The attacker node does not choose the path that the source node takes.
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Conclusions and Future Work
Conclusion
The LEACH technique has been found to be the most effective method for cutting the amount
of power used in wireless sensor networks in this study. Using sensor nodes that are located
inside them, this network can detect ecological conditions. The lifespan of these networks is
shortened by these tiny sensor nodes. It is determined that the sinkhole assault is an ongoing
incursion that reduces the effectiveness of the LEACH protocol. This study offers mutual
authentication as a method of identifying and separating sinkhole incursion. Based on the
15% reduction in packet loss, the 18% reduction in power consumption, and the 25% increase
in network throughput, network efficiency is evaluated. The method put out in this paper is
utilized to identify and separate enemies from these networks. Sink assesses the hop-by-hop
delay in accordance with the threshold value. Regarding the delay, the attacker node is found.
The attacker node is the node that delays communications the most. This decreases time
delay, enhances network performance, and lowers power consumption.
Future Work
The suggested method can be used to find various types of network intrusions, including Sybil
intrusions. For the purpose of evaluating the proposed approach's dependability, several
comparisons with different secure approaches will be made. Due to the lack of centralized
control, secure routing and power usage problems arise in wireless sensor networks. The
Sinkhole attack is a type of active intrusion that affects the network's performance depending
on a number of variables. With the aid of information gathering, a structure can be created
in the future to lower the network's power consumption. The suggested multi-hierarchal
LEACH protocol serves as the foundation for this prospective strategy. In order to verify the
validity of this suggested protocol, it is also compared to existing data gathering protocols.
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References
[1]
David J. Malan, Matt Welsh, Michael D. Smith, “A Public-Key Infrastructure for Key
Distribution in TinyOS Based on Elliptic Curve Cryptography”, Division of Engineering and
Applied Sciences, Harvard University, Dec 2007.
[2]
Dr. G. Padmavathi, Mrs. D. Shanmugapriya, “A Survey of Attacks, Security Mechanisms and
Challenges in Wireless Sensor Networks”, International Journal of Computer Science and
Information Security, Vol. 4, No. 1 & 2, 2009.
[3]
I.F.Akyildiz et al., “A Survey on Sensor Networks”, IEEE Commun.Mag.,Vol. 40, No. 8,
pp.102-114, Aug. 2002.
[4]
Noor Alsaedi1, 2, Fazirulhisyam Hashim, A. Sali, “Energy Trust System for Detecting Sybil
Attack in Clustered Wireless Sensor Networks”, 2015 IEEE 12th Malaysia International
Conference on Communications (MICC), Kuching, Malaysia (23 - 25 Nov 2015).
[5]
E.Shi and A.Perrig, “Designing Secure Sensor Networks”, Wireless Commun. Mag., Vol. 11,
No. 6, pp.38-43, Dec 2004.
[6]
Salavat Marian, Popa Mircea, “Sybil Attack Type Detection in Wireless Sensor Networks based
on Received Signal Strength Indicator detection scheme”, 10th Jubilee IEEE International
Symposium on Applied Computational Intelligence and Informatics • May 21-23, 2015.
[7]
Culler, D. E and Hong, W., “Wireless Sensor Networks”, Communication of the ACM, Vol.
47, No. 6, pp. 30-33, Jun. 2004.
[8]
Sepide Moradi, MeysamAlavi, “A distributed method based on mobile agent to detect Sybil
attacks in wireless sensor networks”, 2016 Eighth International Conference on Information and
Knowledge Technology (IKT).
[9]
Al-Sakib Khan Pathan, Hyung-Woo Lee, Choong Sean Hong, “Security in Wireless Sensor
Networks: Issues and Challenges”, Proc. ICACT 2006, Volume 1, 20-22, pp. 1043-1048, Feb.
2006.
[10]
Ruixia Liu, Yinglong Wang, “A New Sybil Attack Detection for Wireless Body Sensor
Network”, IEEE, 2014.
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[11]
Imran Makhdoom, Mehreen Afzal, Imran Rashid, “A Novel Code Attestation Scheme Against
Sybil Attack in Wireless Sensor Networks”, 2014 National Software Engineering Conference.
[12]
BinZeng, Benyue Chen, “SybilACO: Ant colony optimization in defending against Sybil
attacks in the wireless Sensor Network”, 201O International Conference on Computer and
Communication Technologies in Agriculture Engineering
[13]
Annie Mathew and J.Sebastian Terence, “A Survey on Various Detection Techniques of
Sinkhole Attacks in WSN”, International Conference on Communication and Signal
Processing, April 6-8, 2017
[14]
Mahmood Alzubaidi, Mohammed Anbar, Samer Al-Saleem, Shadi Al-Sarawi, Kamal Alieyan,
“Review on Mechanisms for Detecting Sinkhole Attacks on RPLs”, 2017 8th International
Conference on Information Technology (ICIT)
[15]
Gauri Kalnoor, Jayashree Agarkhed, “QoS based Multipath Routing for Intrusion Detection of
Sinkhole Attack in Wireless Sensor Networks”, 2016 International Conference on Circuit,
Power and Computing Technologies
[16]
Jianpo Li1 , Dong Wang1 , Yanjiao Wang, “Security DV-hop localisation algorithm against
wormhole attack in wireless sensor network”, IET Wirel. Sens. Syst., 2018, Vol. 8 Issue 2, pp.
68-75, the Institution of Engineering and Technology 2018
[17]
RanuShukla, Rekha Jain, P. D. Vyavahare, “Combating against Wormhole Attack in Trust and
Energy Aware Secure Routing Protocol (TESRP) in Wireless Sensor Network”, Proceeding
International conference on Recent Innovations is Signal Processing and Embedded Systems
(RISE -2017) 27-29 October,2017
[18]
Bharat Bhushan, Dr. G. Sahoo, “Detection and Defense Mechanisms against Wormhole
Attacks in Wireless Sensor Networks”, IEEE, 2017
[19]
Swati Bhagat, TrishnaPanse, “A Detection and Prevention of Wormhole Attack in
Homogeneous Wireless Sensor Network”, IEEE, 2016
[20]
Mostefa BENDJIMA, Mohammed Feham, “Wormhole Attack Detection in Wireless Sensor
Networks”, SAI Computing Conference 2016 July 13-15, 2016
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Appendices
Appendix A: Project Gantt chart
Appendix B: Interim presentation slides
Appendices - 1 -
Appendices - 2 -
Appendix A: Project Gantt chart
Sinkhole Attack in Wireless Sensor Network
Thesis 2022/23
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Motivation of the Project
Introduction
Literature Review
Project Proposal
Resource Gathering
Sourcing Papers
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Action Plan
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Abstract
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May 1, 2023
F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S SMTWT F S S
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 # 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8 9 10 11 12 # 14 15 16 17 18 19 20 21 # 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 6 7
Sep 5, 2022
Thesis Start
Fri, 09/09/2022
Enter the name of the Project Lead in cell B3. EntDater the Project Start date in cell E3. Pooject Start: label is in cell C3.
START
Display Week
PROGRESS
9/9/2022 9/29/2022
END
100%
9/29/2022 10/4/2022
TASK
Project Outline
100%
Project Identification
Supervisor
Background Theory
50%
11/27/2022 1/26/2023
Project Planning & Resource Gathering
Action Plan
40%
Reading & Re-reading
0%
Initial Research
Experimental results
Instructor Review
11/8/2023 11/14/2023
Presenting Project
Final Review & Submit ion
0%
Project Development
Buffer Room
Appendices - 3 -
Appendices - 4 -
Appendix B: Interim presentation slides
Appendices - 5 -
Appendices - 6 -
Appendices - 7 -
Appendices - 8 -
Appendices - 9 -
Appendices - 10 -
Appendices - 11 -