Research ProposalPDF Available

Enhancing robustness of scale-free IoT networks against random and malicious attacks (MS Synopsis)

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In this synopsis, robustness of the Scale-Free Networks (SFNs) is enhanced against malicious attacks through optimization. To achieve this, the edge’s degree and nodes’ distance based edge swap operations are used in the proposed Improved Scale-Free Networks (ISFNs) scheme. In the edge’s degree based operation, nodes of similar degrees are linked. Moreover, connections of the nearest nodes are made in distance based edge swap. These operations help to achieve a better onion-like structure without changing the degree distribution of the network. Therefore, the network becomes robust against malicious attacks. Furthermore, to make the network robust against realistic attacks, the variable attacks are considered. Apart from that, a Network Topology Evolution Scheme (NTES) is proposed to prevent SFNs from random and malicious attacks. In this scheme, the network field is divided into two parts with uniformly distributed nodes. After the network’s evolution, the nodes are linked with each other through one-to-many correspondence. The division of the network field is made by considering that a network is robust if its size is small. Moreover, to study the hierarchical changes in the degree of nodes, k-core decomposition is used. In addition, nodes’ degrees and core based attacks are performed on the network to evaluate the performance of the proposed scheme. Furthermore, the network robustness is analyzed using three optimization techniques: Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Genetic Algorithm (GA). The techniques are compared with each other and a technique that efficiently optimizes the network to increase the robustness is selected. In the optimization process, we make use of three edge swap methods. Due to the edge swap, the network robustness is enhanced without changing the degree distribution, so the addition of nodes/links is not required to increase the robustness. In addition, the network robustness of SFNs is enhanced against the malicious attacks. For that purpose, initially, a parameterless optimization algorithm JAYA is used because it requires less computational efforts as compared to the heuristic techniques. Then, as the edge swap plays an important role to enhance the robustness of SFNs, therefore, the edge swaps are classified into three categories. For each category, effects on the network’s topological parameters such as average shortest path length, assortativity and clustering coefficient are analyzed. Next, the robustness is enhanced with the addition of nodes in the maximum connected subgraphs and the protection of bridge edges maintain the network connectivity. Moreover, optimized network is analyzed for different attack strengths.
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COMSATS University Islamabad, Islamabad Campus
Synopsis For the Degree of M.S/MPhil. PhD.
PART-1
Name of Student Muhammad Usman
Department Department of Electrical and Computer Engineering
Registration No.
CIIT/FA18-REE-016/ISB Date of Thesis Registration March 13, 2021
Name of
(i) Research Supervisor
(ii) Co-Supervisor
Dr. Nadeem Javaid
Dr. Sardar Muhammad Gulfam
Research Area Internet of Things (IoT)
Members of Supervisory Committee
1 Dr. Sardar Muhammad Gulfam
2 Dr. Nadeem Javaid
3 Dr. Mariam Akbar
4 Dr. Saif ur Rehman Khan
Title of Research Proposal MS Synopsis on enhancing robustness of scale-free IoT networks
against random and malicious attacks
Signature of Student:
Summary of the Research
In this synopsis, robustness of the Scale-Free Networks (SFNs) is enhanced against malicious
attacks through optimization. To achieve this, the edge’s degree and nodes’ distance based edge
swap operations are used in the proposed Improved Scale-Free Networks (ISFNs) scheme. In
the edge’s degree based operation, nodes of similar degrees are linked. Moreover, connections
of the nearest nodes are made in distance based edge swap. These operations help to achieve a
better onion-like structure without changing the degree distribution of the network. Therefore,
the network becomes robust against malicious attacks. Furthermore, to make the network robust
against realistic attacks, the variable attacks are considered. Apart from that, a Network Topology
Evolution Scheme (NTES) is proposed to prevent SFNs from random and malicious attacks. In
this scheme, the network field is divided into two parts with uniformly distributed nodes. After the
network’s evolution, the nodes are linked with each other through one-to-many correspondence.
The division of the network field is made by considering that a network is robust if its size is small.
Moreover, to study the hierarchical changes in the degree of nodes, k-core decomposition is used.
In addition, nodes’ degrees and core based attacks are performed on the network to evaluate the
performance of the proposed scheme. Furthermore, the network robustness is analyzed using
three optimization techniques: Artificial Bee Colony (ABC), Bacterial Foraging Optimization
(BFO) and Genetic Algorithm (GA). The techniques are compared with each other and a technique
that efficiently optimizes the network to increase the robustness is selected. In the optimization
process, we make use of three edge swap methods. Due to the edge swap, the network robustness
is enhanced without changing the degree distribution, so the addition of nodes/links is not required
to increase the robustness. In addition, the network robustness of SFNs is enhanced against the
malicious attacks. For that purpose, initially, a parameterless optimization algorithm JAYA is used
because it requires less computational efforts as compared to the heuristic techniques. Then, as the
edge swap plays an important role to enhance the robustness of SFNs, therefore, the edge swaps are
classified into three categories. For each category, effects on the network’s topological parameters
such as average shortest path length, assortativity and clustering coefficient are analyzed. Next,
the robustness is enhanced with the addition of nodes in the maximum connected subgraphs and
the protection of bridge edges maintain the network connectivity. Moreover, optimized network is
analyzed for different attack strengths.
1
Table 1 List of Abbreviations
Abbreviations Description
AI Artificial Intelligence
BA Barabási Albert
CF Cascading Failures
CPS Cyber Physical System
DALR Degree Addition Link Rewiring
DAO Degree Associativity Operation
DB Degree and Betweenness
DBA Degree Based Attack
DDO Degree Difference Operation
DN Degree of Node
ENC Empirical Necessary Condition
GA Genetic Algorithm
GDU Gene Distribution Unit
GPCR Gene Position Coverage Ratio
HC Hill Climbing
HDA High Degree Adaptive
HDO High Degree Operation
IoTs Internet of Things
MA Memetic Algorithm
MAGA Multi-Agent Genetic Algorithm
MCS Maximam Connected Subgraph
MIP Mixed Integer Programming
MOO Multi-Objective Optimization
MPGA Multi Population GA
RSF Robustness of Scale-Free network
SA Simulated Annealing
SFNs Scale Free Networks
WSNs Wireless Sensor Networks
1 Introduction
The Internet of Things (IoT) has become an essential part of many real-world applications due to
the availability of a vast range of sensor nodes and the internet. The IoT is part of healthcare [1],
smart grid [2], industry [3], etc., and with the passage of time, the number of IoT devices is
increased, therefore, the networks become dense. Moreover, due to the ease in the availability
of the internet, the concept of internet of everything has become common.
For the effective communication between IoT nodes, different network topologies are con-
sidered [4]. The two widely used topologies are Small World Topology (SWT) [5] and Scale-
Free Topology (SFT) [6, 7]. These topologies are part of complex network theory. In SWT,
the heterogeneous nodes are considered that have different communication ranges, bandwidths
and energy. Moreover, the topology has a high clustering coefficient and average shortest path
length. Furthermore, the SFT is formed by the homogeneous nodes [8] that have similar band-
width and communication range.
Being part of several critical applications, the IoT networks are subject to cyber attacks [9].
The attackers mostly affect the networks to take its controllability. In IoT, two types of attacks
are common: random and malicious. In random attacks, the attackers have less information
about the networks, so the nodes remove randomly. However, in malicious attacks, the attackers
have complete network information and the nodes are removed based on their properties [10].
The effect of these types of attacks on the IoT topologies is different [11]. The SWT is robust
against malicious attacks, whereas, the SFT is robust against random attacks. In SFT, the nodes’
degree follows the power-law degree distribution. In this distribution, the number of low degree
nodes is more as compared to the nodes with high degrees. Therefore, the probability of attacks
on low degree nodes is high against which the SFT is robust. However, the removal of high
degree nodes makes the SFT fragile.
2
In [6], Barabási Albert (BA) model is used to form a SFT based network by following the
growth and preferential attachment processes. During the network growth process, a new node
is added asynchronously while in preferential attachment, the local information of the already
connected nodes is considered. Based on the probability of nodes’ degree, the connections are
made with nodes that are already part of a network and have a high degree. Furthermore, the
SFT is represented by graph theory and unweighted and undirected graphs are considered [12].
The robustness, which is the resilience of a network against attacks [13], is calculated based
on percolation theory based measure proposed by Schneider et al. The network fragments into
multiple subgraphs because of important nodes removal. The Maximum Connected Subgraph
(MCS) is used to calculate the robustness [14] and a large number of nodes are required to make
the network robust. The sensor nodes have limited energy resources, therefore, the Scale-Free
Networks (SFNs) robustness decreases due to the nodes’ failure. Many researchers study the
methods to increase the lifetime of nodes [15–31].
In the network, some nodes are more densely connected internally nodes than the other
nodes in the network and form the community. The community in the SFNs has an impor-
tant role in the robustness enhancement. Therefore, in literature, the community structure in
analyzed against the random attacks, malicious attacks and cascading failures [33–36, 38–56].
1.1 Contributions
In this section, details of the contributions to enhance the robustness of SFNs is given. During
the optimization the degree is not changed, therefore, no extra cost is required.
A homogeneous nodes based method is represented for generating a scale-free network
that have the same communication range and energy. These nodes are robust enough to not
only random attacks but also to specific attacks. Moreover to enhance a SFNs robustness, a new
technique, Improved Scale-Free Networks (ISFNS) is proposed. ISFNs, enhances the robustness
of the scale-free network topology without changing the nodes’ degree distribution. ISFNs
consists of two phases: the edges degree based swap and nodes distance based edges swap to
obtain onion-like structure for the network. The onion-like structure makes the network robust
against the malicious attacks
Moreover, as the size of networks are increasing due to the advancement in the sensor tech-
niques, the networks are becoming dense. The size of a network has an effect on network ro-
bustness and with the increase in a network size the robustness decreases. To address the afore-
mentioned problem, a Network Topology Evolution Scheme (NTES) is proposed. In NTES,
the network evolution is started by dividing the sensor field into two parts. The networks are
evolved in each part and link with each other through one-to-many correspondence. The nodes
of network Aare linked with single or multiple nodes of network Bby following one-to-many
correspondence. To make the network robust, the nodes’ degree follow the power-law degree
distribution. The nodes’ degree changes hierarchically in each ring [59], therefore, the change
is calculated by k-core decomposition. Furthermore, the SFT is optimized to form long links
between the nodes because these links make the network robust against malicious attacks [60].
In addition, three optimization algorithms, Genetic Algorithm (GA), Bacterial Foraging Opti-
mization (BFO) and Artificial Bee Colony (ABC), are used to optimize NTES and the one that
has better performance is selected.
Furthermore, to optimize the SFNs the JAYA algorithm is used to find the optimal solution
with less computational cost and without the control parameters. It is the latest optimization
technique used to find the optimal results than the already existing techniques. According to
our knowledge, we are the first that are using JAYA for the optimization of SFNs. Furthermore,
to enhance the robustness of the networks edge swaps are performed. To analyze the effect of
edge swap on the network structure, the edges are classified into three categories: random edge
swap, degree based edge swap and distance based edge swap. The edge swap is performed
by keeping the nodes’ degree distribution constant. In addition, the robustness is enhanced by
3
adding the nodes in the MCS after the network fragments. Moreover, the network’s performance
is analyzed against different attack strengths.
2 Literature review
In real-world applications many networks are scale-free. These networks are robust against
random attacks because low degree nodes are present in a large number and they have a high
probability of selection. However, malicious attacks fragment these networks into multiple sub-
graphs. In malicious attacks, the important nodes based on their degree, betweenness centrality,
closeness centrality, etc., are removed from the network. Due to the presence of a small number
of important nodes, SFT are prone to malicious attacks. In this synopsis, the importance of a
node is calculated based on its degree.
The network topology has a key role in defining the robustness of SFT. In [57], based on the
graph theory, BA model is proposed. In this model, the processes that are required to construct
a network are presented. The model forms the network topology to an onion-like structure that
is robust against the malicious attacks. However, the limited communication range of sensor
nodes in Wireless Sensor Networks (WSNs) is not considered. Therefore, the nodes may die
due to their excessive usage. Moreover, the constraints of sensor nodes including limited en-
ergy and communication range is considered in [62]. The network is evolved by considering
nodes’ communication range and the threshold value of the nodes’ degree. The robustness is
improved through the Degree Difference Operation (DDO) and Angle Sum Operation (ASO).
However, the redundant operations increase the computational complexity. A similar work has
been performed in the [63]. Furthermore, based on the nodes’ fault probability a network topol-
ogy evolution scheme is introduced in [65]. In the network growth process, a new node joins the
network based on the fault probability and communication range of nodes, simultaneously. The
network robustness is enhanced against the malicious attacks due to the formation of onion-like
structure.
The optimization is performed to enhance the robustness of SFT. The optimized topol-
ogy has a better network structure and is robust against malicious attacks. Therefore, a local
rewiring based algorithm Hill Climbing (HC) is proposed [57]. In this algorithm, the rewiring
is performed by considering the local information of nodes. Although it provides a better net-
work after the rewiring, however, it traps into local optima due to the local rewiring process. A
similar work has been done in [58]. The authors in [66] extended the work proposed in [57]
by introducing the global edge swap method. A temperature variable is introduced to get the
optimal solution. Moreover, the authors in [67], use a deluge algorithm to get the optimal result
as compared to Simulated Annealing (SA). The edges are classified with the removal of nodes
from the network [68]. The swap operation is performed to increase the number of valid edges
in the network. The increase of nodes in MCS enhance the robustness of the network.
Different heuristic techniques are used to enhance the robustness of SFT. GA is mostly used
to optimize SFT, however, the classical GA traps to local optima due to premature convergence.
The low population diversity causes the premature convergence problem and for better results, a
global solution is required from the search space. To solve the less population diversity problem
[69, 70] introduces the multi-population based methods. Although the methods provide a high
diverse solution, however, due to the involvement of multi-population the computational cost is
high. Moreover, the involvement of multiple operations makes the method difficult to implement
on real-world networks. Qie et al. [71] solve the premature convergence problem by introducing
the self competition among the individuals of a population. Moreover, a Memetic Algorithm
(MA) is proposed [72] that uses the local search operator to avoid premature convergence. The
optimal solution is found from the search space by MA. A multi-agents based algorithm [73] is
proposed to get the optimal solution with less computational cost.
Single objective optimization techniques are discussed so far for the optimization of SFT.
4
However, in some problems, it is difficult to optimize the network with single objective op-
timization. Therefore, a Multi-Objective Optimization (MOO) technique is used to optimize
the problems that have more then one objective to be optimized. A Multi-Objective Evolution-
ary Algorithm (MOEA) is proposed in [74] that is used to optimize robustness, when multiple
attacks simultaneously happen on the network. In MOEA, the negatively correlated objec-
tive functions are found by Pearson correlation coefficient and are optimized. Due to MOO,
the computational cost to calculate robustness is high. A similar work has been done in the
literature [75–77] to enhance the robustness of SFNs against multiple objectives. Apart from
undirected network, a directed network also define the important network characteristics. There-
fore, a directed network with the emergence of cooperation and controllability of robustness as
two important features are discussed [78]. To find the correlation between network topological
features and their robustness a practical approach is required. Therefore, in [79], an empirical
approach is proposed to better control the network.
Moreover, the IoT based applications and importance of the networks other then the scale-
free is studied in the literature review. In robotic, the controlled movement has great importance
to complete the tasks. Therefore, the collision and obstacle avoidance control strategies are
studied in the [80, 81]. Moreover, the secure networks for the agri-food and the data sharing
based on the blockchain are studied in [82,83]. Using the blockchain network’s and data security
is increased. The WSNs are extensively used in the underwater communications. The effects of
sink mobility on the data gathering are analyzed in [84,85]. The sink nodes are considered static
and the sensor nodes are mobile before. However, the extensive research prove the importance
of sink position for the efficient communication.
In the communication, the delay should be kept minimum for the efficient sharing of data.
Therefore, a routing scheme for the delay sensitive applications proposed [86]. Moreover, the
efficient routing is performed in [87–89] The routing is made efficient by managing the energy
consumption of the sensors. Furthermore, to increase the network lifetime the protocols are
enhanced [90, 91]. These protocols use the proper routing to better utilize the energy. In the
same way, energy utilization and consumption algorithms to increase the network lifetime are
discussed [92, 93]. The network attack and repair strategy is analyzed in [94]. The fraction of
removed nodes and the average degree of nodes are experimentally studied.
Some other crossed domain topics are also studied in the literature review to make use
of different concepts. The deep learning is used to detect the brain tumor [95]. The data set of
normal and effected persons are analyzed and based on the selected features the model is trained
to detect the tumor. Furthermore, the communications related to the body area network based
protocol is studied [96]. The faults detection in the WSNs has a great importance. Therefore,
base on the random forest scheme the faults are detected [97]. Moreover, to increase the lifetime
of the networks the cooperation of nodes is very important, which is analyzed in [98].
In cascading failure and robustness of the network, how they relate with each other needs to
be found [99]. By calculating these objectives comprehensively normalized robustness used that
has high computation cost. Connectivity and dependence links enhance the robustness [132],
however, these links are added randomly. If both networks follow scale-free topology then these
links may change the network structure. The dependence links are not following the power-
law distribution, therefore, stubborn attacks on these links cost more damage to the network.
Both networks contain high and low degree nodes and attacks on high degree nodes affect more.
Therefore, dependence links need to be added between these nodes to improve robustness. After
a node or edge having high load is removed its load is distributed among other nodes or edges.
However, there is a possibility of simultaneous edge and node removed due to the environmental
faults or extra load then how the network deals with that to minimize the Cascading Failures
(CF) is not discussed [100]. Secondly, due to overloading the robustness of a network decreases
therefore, when an attack or failure happens on one part of a network that should be removed
to protect the other part of the network to collapse. The proposed scheme better capture the
5
CF, however, no method is discussed to reduce the CF. The failure of nodes is not mentioned
either it is random or based on degree. In Cyber Physical System (CPS) Distributed Energy
Resources (DER) can help to maintain load that need to be discussed. A similar work is done
in [101–103]. Moreover, against the multiple nodes removal from the network, the cascading
failure is calculated in [104]. Using different parameters the network robustness is analyzed to
make a practical approach to enhance the robustness.
Although [59] gives new perspective of attack based on cores of network, however, when the
fraction of removed nodes is high core based attack is less destructive. In that case the between-
ness centrality attack becomes more vulnerable to the network. For SFNs onion-like structure is
not considered because in that structure same degree nodes are connected with each other. Shell-
min attacks can be considered as random attacks because they are done on low value of core
for these attacks SFNs are robust. By installing backups network performance increased [105]
however, installing backup increase cost. The key nodes usually are hub nodes so, installing
backup for these nodes is not an easy task. Secondly, after the attack, a key node is removed
because there is a high probability that a second attack may happen on the backup node. In
targeted attacks, the attacker has complete information of the network [105] how the proposed
scheme update the network information is not discussed. The links are added and removed to
implement the adversarial attack [106], however, the degree distribution is change that is not
considered. The addition of links have a cost that has not been discussed. Wandelt et al. [107]
propose Quick Robustness Estimation (QRE) for estimating the network robustness. QRE per-
formed better than the betweenness centrality because it is based on a cheap-to-compute network
matrix combination. The proposed system enhanced the robustness of scalable networks, there-
fore, it is not for SFNs. The methodology for managing the robustness of the Social Internet
of Things (SIoT) is proposed by the authors in [108]. SIoT is divided into multiple Enterprise
Systems (ESs). The state variable of these ESs are determined and the interaction between them
is anatomized. After that, the nonlinear dynamic model is developed. However, the applicability
of the proposed method is reduced at the operating stage of SIoT.
The entropy based network robustness optimization technique is proposed in [109]. In this
technique, the random failures of nodes are considered and the entropy of the network degree
distribution, scaling factor of power-law and nodes’ connectivity is studied. However, the mali-
cious attacks against which the SFNs are fragile is not considered. Apart from that, the network
robustness is enhance by adding links [110]. A similar work is performed in [120, 121], that
consider the critical network infrastructure and modification of edges, respectively. On the
other hand, the network robustness of SFNs against the removal of links is analyzed in [122].
The malicious attacks are happened on high degree nodes, therefore, the method is failed to
increase the robustness in that case. Moreover, the network robustness in enhance by decreasing
the assortativity coefficient against the malicious attacks [111]. The network robustness against
the random attacks by keeping the nodes’ average degree constant is enhanced in [112, 113].
Moreover, the network robustness is enhanced by modifying the network structural and charac-
teristics information in [114–117].
To enhance the network robustness against the malicious attacks, the parameterized net-
works are made that are robust against both random and malicious attacks [118]. A similar work
has been done in [119]. On the other hand, the limited energy of sensor nodes are analyzed to
deal with sensor nodes’ constraints to increase the robustness of large-scale WSNs [123]. To
increase the network robustness against the attacks on the edges is studied in [124]. The network
robustness is increased against the random and malicious attacks without increasing the number
of driver nodes. Moreover, to study the limited energy of the sensor nodes in the SFNs, the
network is partitioned and a reduced SFN is made [125]. Furthermore, the robustness structure
and the effects nodes removal are extensively studied in [126–130]
6
Table 2 Problems addressed by previous work.
Problem identified Proposed solutions Validations Limitations
SFNs survivability and ro-
bustness after cyber at-
tacks
• Topology evolution
based on fault probability
• Considered two opera-
tions i.e., HDO and DAO
• Four types of attacks
considered that make
TMSE resilient against
real life attack
• Results are validated by com-
paring with the existing algo-
rithms
• TMSE outperforms the exist-
ing techniques of different size
networks and different types of
attacks
• Preferential attachment may
be compromised due to fault
probability
• HDO changes connection of
high degree nodes which have
high attack probability
• DAO not considered the fault
probability that increase the ef-
fect of attack [65]
Multiple attacks on a net-
work simultaneously
Uses multi-objective opti-
mization to enhance the
robustness
• Effects of attack on topologi-
cal features validate the impor-
tance of MOEA
• Synthetic and real-life net-
works prove the importance of
MOEA
• High computation cost due to
calculation of multi-objectives
• Topology improvement for
simultaneous attacks not dis-
cussed
• Pareto set generation can be
difficult [74]
Premature convergence of
classical GA
Uses multi-populations
co-evolution to solve
premature convergence
Comparing with existing algo-
rithms MPGA enhances the ro-
bustness
• Optimal population size
should be chosen
• Local operator should be
used to make diverse popula-
tion [69]
Premature convergence of
classical GA
• Self-competition among
the individuals
• Calculate population di-
versity using GPCR and
GDU
• Mutation probability cal-
culated by adaptive adjust-
ment
• Performance is validated with
different algorithms and at-
tacks
• Less time cost as compared
to ROCKS which is multi-
population algorithm
• Diversity calculation is a
complex process at every oper-
ation
• GPCR and GDU have high
computation cost
• Uses multiple operations
which make computation cost
high [71]
Link attack based on be-
tweenness centrality has
high computational com-
plexity
• Link attack based on
shell has better perfor-
mance than betweenness
centrality
• Shell based attack has
less computational com-
plexity
• Importance of shell-based
attacks validated for different
types of network
• Shell-min, shell-max and
shell-pro attacks compared
with betweenness centrality
• Less destructive when the
fraction of removed nodes is
high
• Less effective for SFN due to
not following onion-like struc-
ture
[59]
No practical approach
available to understand
the relationship between
network topology features
and network robustness
• Empirical approach to
measure robustness
• Exhaustive search-based
technique on small world
and real-life networks
• ENC helps to converge
by shrinking search space
• RER helps to rectify
ENC
• Importance of RER is vali-
dated for different number of
nodes
• Network connectedness im-
proved by increasing number
of RER operations
• Exhaustive attack is not actu-
ally considered
• Not explain RER for undi-
rected network
• RER changes nodes degree
• Due to RER SFN not remains
scale-free [79]
The vulnerability of SFNs
due to malicious attack
• Constructed network
considering communica-
tion range of nodes
• DDO and ASO are used
to construct onion-like
structure
• Through ROSE topology con-
verted to onion-like structure
• Network becomes robust
against malicious attack
• Target all nodes in the net-
work may generate redundant
operations
• DDO and ASO are computa-
tionally expensive [62]
High computational com-
plexity of existing algo-
rithms is a hurdle in topol-
ogy self-optimization
For the self-optimization
AI based technique is used
• Efficiency and loss function
for training and testing vali-
dated
• 99% efficiency is achieved
• Loss function minimized in
35th iterations
• Not suitable for different size
of networks and edge densities
• After different attack how
self-optimization works not
discussed [64]
Continued on next page
7
Table 2 Continued from previous page
Problems identified Proposed solutions Validations Limitations
Due to random edge swap
without considering the
network structure redun-
dant operations are per-
formed
• After attack edges are
classified into three types
• By increasing the valid
edges robustness enhances
• For HDA attack algorithm
performance is validated for
different size of networks
• The heuristic algorithm main-
tains the robustness
• Edge swap between invalid
edges enhances the computa-
tional overhead
• When nodes fail the edges
with the nodes also removed
[68]
• Network robustness de-
creases when the nodes re-
moved
• Optimization algorithms
fall into local optima
• NC is used to measure ro-
bustness
• Chaotic GA is used to op-
timized network
• To avoid local optima lo-
gistic maps based power
function carrier used
• Degree distribution re-
mains unchanged after op-
timization
• NC increases with increasing
iterations [136]
• NC and r have positive
correlation that proves enhance
robustness
• Onion-like structure is
achieved through optimization
• Considering different attacks
network is robust
N/A
• Community detection al-
gorithms reveals individ-
ual information
• Practical approach re-
quired to protect these in-
dividual’s privacy
• Network is optimized by
using heuristic technique
• CDA and HDA per-
formed to detect commu-
nity and high degree nodes
• Q-based attack based on
GA is performed
• Addition and removal of
links increase privacy on
individual
• Different P
cand P
mare used
to find optimal parameter for
GA
• For all networks Q-based at-
tack outperformed
• Decrease in Q value confirms
the privacy of individual
• Randomly addition and re-
moval of links affect SFN
• Addition of links have cost
• Onion-like structure is not
follow
• Directedness is an impor-
tance feature for a network
f(c)and Rdifficult to op-
timize simultaneously
• Correlation between f(c)
and Rproves the negative
correlation
• MOEA is used for these
objective
• r is used to set network
structure
• PD is used to calculate
f(c)
• MCS is used to calculate
R
• Pareto optimal solution
obtain for both objectives
• MOEA optimized both objec-
tives better than single objec-
tive algorithm
• Different networks are opti-
mized
Ris assortative whereas f(x)
disassortative therefore, onion-
like structure is obtained by
shuffling edges
• No comparison is made
with other multi-objective al-
gorithms [78]
• Only assortativity is consid-
ered
• Other topological parameter
may prove positive correlation
of these objectives
• SFNs are vulnerable to
malicious attacks
• Robust network structure
is require
• SFNs converter to onion-
like structure
• Random edge swap is
made
• Degree difference re-
mains same after opti-
mization
• Nodes increase in MCS
• Different networks are
used for validations
• Network performance im-
proved against malicious at-
tacks
• Swap edge enhances robust-
ness
• Increase in number of nodes
decreases network robustness
• Assortativity and clustering
increase
• Random edge swap effects
network structure [57]
• Average shortest path length
increased
• HC traps into local op-
tima
• Network optimization
against malicious attacks
required
• Network is generated by
using BA mode
• Global and local edge
swap performed for explo-
ration and exploitation
Tis used to avoid local
optima
αis used to get fast con-
vergence
• Both synthetic and real-world
networks considered
• Global edge swap and local
edge swap are compared
• Global edge swap has better
robustness due to exploration
• Network optimized by using
αis more robust
• No threshold value for edge
swap is set [66]
• No real-world network con-
sidered
Continued on next page
8
Table 2 Continued from previous page
Problems identified Proposed solutions Validations Limitations
• SFNs are not robust
against malicious attack
• No proper solution is
available to mitigate cas-
cading failure
Rcand Rare weakly
correlated
• Multi-objective opti-
mization is used
• High and low objectives
value based networks
generated by SA
• Optimal topology is
found by the MAGA by
exploration
• Both synthetic and real-world
networks are considered
• Normalized robustness is bet-
ter as compared to SA
• For different sized networks
both objective have improved
values
• Cascading failure proved to
be more damaging as com-
pared with intentional attack
• A single measure needs to be
used for these objectives
• Cascading failure does not
happens rapidly countermea-
sures reduce its effect
• A better network structure re-
quired that is robust against at-
tacks
• Robustness of interde-
pendent network needs to
enhance
• Connectivity and depen-
dence links are added to
enhance robustness
• Considering cost con-
strain optimal values of
these links need to be cal-
culated
• CPS is more vulnerable
and caused cascading fail-
ure in physical region
• One-to-many configura-
tion is used to connect
these networks
• Stubborn and smart at-
tacker are considered
• Defender add links by
calculating intra and inter
degree
• Both synthetic and real-world
networks are considered
• Intra and inter degree based
attacks are performed on net-
works
• Based on these attacks links
are added
• Different intra and inter de-
gree based networks are con-
sidered
• Links are added randomly
without considering degree dis-
tribution [132]
• Dependence links are not fol-
lowing power-law distribution
• Stubborn attack on depen-
dence links caused network to
fail
• GA has premature con-
vergence problem
• Population diversity
needs to be high
• ROCKS uses multi-
populations co-evolution
to deal premature conver-
gence
P
mand P
care different
for populations
• Populations coordinate
using migration operator
• Migration population
contains best individuals
from all populations
• Degree distribution
remains same after opti-
mization
• Onion-like structure is ob-
tained after optimization
• After ROCKS network is
more robust against random
and malicious attacks
• For different size networks
ROCKS outperform HC and
SA
• Due to multi-population com-
putation cost is high [70]
• After attack self-optimization
is difficult
• Difficult to implement on
real-life network due to com-
plexity
• After edge or node
removal their respective
edges and links also re-
moved
• Multiple nodes and links
removal caused cascading
failure
• Node and Edge based
model required to deal the
failure
• DB model proposed to
deal cascading failure
• Node importance is
calculated based on its
degree and betweenness
• Removal of important
node causes other nodes
and edges to become
overload
• Load is distributed
among node by consider-
ing their initial capacity
• Five metrics used to
evaluate robustness
• Initial capacity of nodes is
compared with critical value of
tolerance
• Cascading failure of edge and
node of DB model is less when
θ< 0.9
• DB model has better value of
SGand SCcompared to DN and
BN
• Load is distributed among
neighboring nodes which have
already high load
• Simultaneous edge and node
removal is not discussed
• Overload edges and nodes
should be removed to protect
remaining network
• Random edge swap is
performed in optimization
• Robustness improve by
compromising community
structure
• Optimizing performed in
a way that preserve com-
munity structure
• Three step strategy is
proposed to preserve com-
munity
• Onion-like structure is
introduced in every com-
munity
• High degree nodes are
connected with nodes of
their own community
• Considering cost links
can be added to better pre-
serve community
• Community structure is com-
pared before and after opti-
mization
• These are preserved with en-
hanced robustness
• After the node removal 3-
steps strategy performed better
compared with single step
• High degree node of different
communities are not connected
with each other
• After removal of high degree
node its load can not be dis-
tributed by remaining nodes
• High degree nodes must con-
nect to share their load
Continued on next page
9
Table 2 Continued from previous page
Problems identified Proposed solutions Validations Limitations
• Removal of important
node causes decrease in
robustness
• These nodes must be pro-
tected by taking counter-
measures
• No method is available to
protect these nodes
• Networks vulnerability
against key nodes removal
• Key nodes are found
based on MILP
• Network is optimized by
heuristic algorithm
• Countermeasures in the
form of installing backup
for key nodes
• Impact of node’s elimination
on topological parameter
• Throughput, network delay,
and flow is calculate
• End-to-end delay is maxi-
mized by node protection
• After removal of a node there
is high probability of backup
node removal
• Network information need to
be updated against smart at-
tacker
• Adversarial attack
change network informa-
tion
• Network robustness
decrease by these attacks
• Adversarial attacks are
considered in this network
• Two new attacks strate-
gies DILR and DALR in-
toduced
• RLR is less effective as
compared with DILR and
DALR
• SFNs are categorized
into strong to weakest
• Networks are generated by
considering Rand α
• Each network is attacked 200
times
• Average shortest path length,
clustering coefficient and diag-
onal distance is compared with
attacks
• Degree distribution is change
by edge swaps [106]
• Addition of links have cost
Cascading failures in inter-
dependent networks
• Novel method to capture
cascading failure intro-
duced
• CPS and PS considered
as interdependent net-
works
• One-to-One correspon-
dence between networks
• Fraction of survival
nodes is calculated after
each node removal
• Removal of a node in
PS caused overloading in
survived nodes
• Removal of a node in
CPS decreases MCS size
• Networks are validated ac-
cording to asynchronous fail-
ure propagation model
• Intra and inter dependencies
considered
• At even stage nodes removed
from CPS and at odd stage
nodes are removes from PS
• For load and space of PS
different distributions are fol-
lowed
• Interdependent network is
more vulnerable to node re-
moval as compared to single
network
• How to reduced cascading
failure is not discussed [100]
• DER impacts to reduce load
needs to be discussed
Importance of SFN robust-
ness
• Memetic algorithm to op-
timize network is used
• To enhance robustness
global and local searches
used
• Population is generated
by swapping edges of ini-
tial network
• Crossover is performed
by changing links of par-
ents
• Offsprings has same de-
gree distribution as parents
• Local search operator is
used for exploitation
• Optimal solution is found
by 2-tournament selection
• Optimal value of edge swap
• Different sized network MA
RSFMA outperformed
• Proposed crossover improve
network robustness
MA RSFMA performed bet-
ter against random and mali-
cious attack
• Onion-like structure is pro-
duced
• Population has less diversity
[72]
• Crossover changes degree of
nodes
3 Problem statement
This section presents the problem statement of the synopsis. Moreover, the main problem is
divided into three subproblems which are presented below.
10
3.1 Details of problem statement
The SFNs are more suitable for IoT networks because they are resilient to random attacks. In
recent years, a significant attention is paid to enhance the robustness of these networks against
malicious attacks [12, 68, 131]. For the topology of SFNs, BA model is proposed in [57], that
explains how the nodes are connected to form a network. Furthermore, for the calculation of
robustness, a mathematical equation based on the percolation theory is proposed in [139]. One
way to increase robustness is by adding edges however, it adds the cost, which is solved by edge
swap. Therefore, by global edge swap, the degree of nodes remains constant and robustness
enhances without increasing cost [62]. In addition, an onion-like structure is proposed that
contains nodes, whose degree decreases hierarchically and are more robust against malicious
attacks. However, the solution falls into local optima that is solved in [57] with local edge swap.
Moreover, due to redundant operations, it has less efficiency. In [68], the network is constructed
based on the communication range and the threshold of nodes degree. It converts the network
into an onion-like structure, however, due to redundant operations, the network’s efficiency is
reduced.
Critical networks, including healthcare, military, and Internet, etc., have scale-free nature.
These networks should be robust against attacks however, the existing algorithms [68, 70, 138]
have high cost, therefore, self-optimization is used in [14]. Still, the problem of network robust-
ness is not solved against malicious attacks and these attacks make the network vulnerable.
4 Generation of scale-free networks
The construction of SFNs is based on the BA model. After the deployment of nodes in a sensor
field, the model considers the growth and preferential attachment steps. In the first step, at each
time interval, a single node is added to the network. However, in the second step, new nodes
prefer to join the network by considering the degree of the existing neighboring nodes. The new
nodes prefer to join the nodes that initially have high degree in the network. Therefore, due to
the limited resources of the sensor nodes, the chance of network failure is increased.
To deal with that issue, the connection probability based solution is used. The probability
Πdi of a new node iis calculated as follows.
Πdi=di
jdj
,(1)
where, diand djare the degree of node iand sum of the neighboring nodes degrees, respectively.
The network construction is depicted in Fig. 1. There are three nodes i,jand kthat want to
k
k1
k2
k3
k6
k4
k5
i
i1
i2
j1
j3
j4
j
j2
j5
Figure 1. Scale-free network’s construction
11
become part of the network. First of all, start with the example of node jthat wants to join the
network. There are five nodes in its neighborhood with degrees 1,1,3,1 and 2. Using Eq. 1, the
connection probabilities of the neighboring nodes are 0.125, 0.125, 0.375, 0.125 and 0.25. All
the nodes’ connection probabilities are placed into the roulette wheel. On average, the nodes
with a high degree have more area into the roulette wheel as compared to the low degree nodes.
So, there is more probability of a high degree node’s selection. In this study, the neighbor nodes
that have the highest connection probability are directly selected, however, for the remaining
nodes, the roulette wheel selection is followed. Furthermore, node idetermines the connection
with neighbor nodes according to the edge density m. In the neighbor of node i, their are two
nodes i1and i2with degrees 2. If m= 2 then node imakes connection with nodes i1and i2
directly. Otherwise, depending on the value of m, the connections are made. For node k, there
are six nodes and all these nodes are not connected with the network. Therefore, when the node
kbroadcasts a request message to make the connections with the nodes in the local community,
all the nodes will receive that message. After that, the nodes reply to node k. At that point, the
node kfollows the First Come First Serve (FCFS) [140] approach because it provides a simple,
efficient and less computational expensive solution. Therefore, the node that responses early
makes the connection with node k. These steps mentioned above are followed until the network
is completely evolved.
5 Network model description
The complete details of the proposed Improved Scale-Free Networks (ISFNs) are given in this
section. The description of the operations as part of the ISFNs is presented in detail. However,
before being familiar with these operations, the knowledge of independent edges is essential.
5.1 Independent edges
The topology of the SFNs is represented as a graph G={N,E}. The set of nodes and edges are
given as N={N1,N2, ..., NN}and E={E1,E2, ..., EN}, respectively. The following conditions
should be met to confirm that the edges ei j and ekl are independent.
1. Nodes as part of edge pairs should be in the same communication range.
2. There should be no extra edge except eil and ejk .
In Fig. 2a, the original topology with edges ei j and ekl are shown. By following the above
mentioned conditions, the selected edges are independent. Moreover, the first edge swap (eik,
ejl ) and the second edge swap (eil ,ejk ) are represented in Fig. 2b and Fig. 2c, respectively.
ij
kl
ij
kl
ij
kl
ab c
Figure 2. Edge swap mechanism a: First connection method b: Second connection
method c: Third connection method
12
5.2 Edges’ degree based edge swap
For the specific edge ei j, the edge degree di j is calculated from the degrees of its respective
nodes. The edge degree is derived from the nodes degree [74] and is defined as,
di j =pdi×dj,(2)
where, degrees diand djare for nodes iand j, respectively. Higher the value of edge degree,
more important is it in the network.
After finding the independent edges from the network, the edge degree is calculated with
Eq. 2. After that, based on the Eqs. 3, 4 and 5, the degree difference is calculated against each
edge swap. The degree difference of all the edge swaps is compared and the pair of edges having
minimum difference is selected. If a new pair of edges increases the robustness and the network
connectivity is not destroyed, then the adjacency matrix is updated with the acceptance of edge
swap.
DIF0=|dij dkl|,(3)
DIF1=|dik djl|,(4)
DIF2=|dil djk|.(5)
The edge swap based on the difference of degree is motivated from [62], where random pairs
are selected from the network. However, in this synopsis, the edges are selected based on the
degrees of their respective nodes. The edge degree based swap helps to connect the similar de-
gree nodes. Moreover, the edge degree difference operation helps to achieve high robustness by
increasing the assortativity. Using the edges’ degree difference operations, the degree distribu-
tion of the original topology is not changed. Therefore, no extra cost is required to optimize the
network.
5.3 Nodes distance based edge swap
It is the second operation of the ISFNs scheme. This edge swap helps to link the nodes that are
near to each other. In the network, the robustness is improved against malicious attacks when
these links exist. As the preferential attachment is followed by the SFNs in the growth process,
the new nodes make the connection with the existing nodes based on a high degree. Therefore,
the low degree nodes have a chance to connect with the high degree nodes, through this edge
swap.
To perform the nodes distance based edge swap, nodes i, j, k and lare selected from the
graph G. There are edges ei j and ekl between the nodes (i, j) and (k, l), respectively. The average
distance is calculated between the nodes of the independent edges using the Euclidean distance
formula. The average distance of two edges is required for each connection method because
there is a possibility of a distance mismatched between the edges. Therefore, the pair of edges
is selected that has a small distance. Fig. 3 shows the nodes distance based edge swap. D1
and D2represent the nodes’ Euclidean distances for the edge ei j and ekl , respectively. In Fig.
3a, the original topology is given and the robustness is calculated against the malicious attacks.
Moreover, in Fig. 3b, the first edge swap of the independent edges eik ,ejl is performed and D1
and D2are calculated. The same approach is followed in the second edge swap as shown in Fig.
3c. The robustness is calculated against these edge swaps and the pair of edges that provides the
highest value of robustness is selected to update the adjacency matrix.
5.4 Measuring network robustness
After the malicious attacks on the SFNs, the network is divided into multiple subgraphs, result-
ing in the reduction of the network performance. To study the relationship between robustness
and the number of removed nodes, Schneider et al. [62] and [131] proposed a mathematical
13
D1
D2D1D2D1D2
ij
kl
ij
kl
ij
kl
ab c
Figure 3. Nodes distance based edge swap a: First connection method b: Second
connection method c: Third connection method
equation. According to the Schneider observation, the robustness Rof a network having N
nodes can be represented using Eq. 6.
R=1
N+1
N1
n=0
MCSn
N,(6)
where, Nis the number of nodes and 1
N+1is the normalization factor and MCS are formed
after the nodes with high degree are removed. Moreover, the robustness lies in the range of [0,
0.5]. The maximum value of the robustness is less than 0.5, which means that the maximum
number of subgraphs are connected while the minimum value of the network robustness is ap-
proximately zero, which means that a single high degree node present in the network is affected
by the malicious attacks.
5.5 Variable attacks
To study the effect of variable nodes removal from the network in an instant of time, the variable
attacks are performed. In these attacks, the number of nodes is removed from the network
and its connectivity is analyzed. By knowing these attacks, the defender can easily optimize
the network to increase its lifetime. Variable attacks in this study are performed by randomly
selecting the number of removed nodes in the range of 1 to 10. The number of nodes in the
MCS is calculated after each attack. Due to multiple nodes are randomly removed, therefore,
the effect on network connectivity with multiple nodes removal in a single instant is analyzed.
6 Scale-free model
For the SFT, the details about the network evolution and the scale-free property of the network is
verified in this section. Initially, the SFT is constructed using the BA model. Then, to enhance
the network robustness, three types of edge swapping methods are discussed. Moreover, to
calculate the robustness of the SFT, the metric of robustness is studied.
6.1 Construction of Scale-Free Network
The operation of dense networks are difficult as they are highly vulnerable to the attacks that
occur on the network links or nodes. The real-world examples of dense networks are hospitals,
military, transportation, etc. The drawback of these networks is that their operational efficiency
deteriorates once the attacks happen. Therefore, the networks are divided into smaller networks
by the graph partition concept. The small networks’ operations are easy, processing is fast,
efficiency is high and the failures or removal of nodes have less effect on the overall networks.
In this study, we have made a synthetic network by assuming that small-sized networks are
more robust and easy to maintain than large-sized networks. To make a small-sized network,
14
the network field is divided into two equal parts and nodes are randomly deployed. In both
parts, the network evolves with equal number of nodes. The node at the center of the network
broadcasts a request message to its neighboring nodes. Based on the response time, the initial
nodes connect to the center node. After that, the remaining nodes join the network based on the
preferential attachment, as in [57]. The complete process of network evolution is shown in Fig.
4. The dotted line represents the partition of the network, and Ncu and Ncl are the center nodes
NA
NB
NM
CL
ML
C1
C2
C3
C4
Ncu
Ncl
Figure 4. Network evolution by adding edges
of the upper and lower networks, respectively. The network growth starts from the center nodes.
After the network evolves, its both parts are linked by one-to-many correspondence. Moreover,
to increase the network robustness edge swap is performed.
6.2 Details of edge swap
The graph theory is used to represent the SFT. With the help of graph theory, the network is
converted to a graph (G) in which the nodes are represented as a set of vertices V={1,2, ..., N},
whereas, the links between the nodes are shown as edges E=ei j|i,jVand i6=j. So, the
graph is G= (V,E)and it is undirected and unweighted graph used to evaluate robustness of
SFT. The edge swap of independent edges is performed to enhance the robustness. Two edges
are independent if all the nodes of these edges are in the same communication range and they
have no extra edge.
In Fig. 5, the original topology with the possible first and second edge swaps are given. As
seen in Fig. 5(a), the initial topology’s nodes i,j,kand lhave independent edges ei j and ekl . In
Fig. 5(b) and Fig. 5(c), the first and second possible edge swaps are represented, respectively.
Against all the possible edges, the robustness is calculated and a pair of edges is selected that
gives the highest robustness.
To enhance the robustness, the edge swap is important because no extra cost is required to
add new node or edges. Therefore, the following types of edge swap methods are implemented.
1. Edge swap of randomly selected nodes
2. Edge swap of degree based selected nodes
3. Edge swap of distance based selected nodes
6.2.1 Edge swap of randomly selected nodes
Nodes iand jare chosen at random from the network to perform a random edge swap. Then the
nodes kand lare selected in the neighborhood of iand j, respectively. Edges ei j and ekl should
15
be independent to make the edge swap. The robustness is calculated for each edge swap. If the
robustness of the network is increased, then the network is updated. However, new independent
edges are found in case of robustness is not improved. Since, there are many nodes in the
network with a low degree, therefore, in this edge swap mechanism, the probability of these
nodes selection is high.
i
l
j
k
i
l
j
k
i
l
j
k
(a) (b) (c)
Figure 5. Edge swap mechanism
6.2.2 Edge swap of degree based selected nodes
The degree of the nodes is being used to perform a degree-based edge swap. Initially, from the
network, a high degree node is selected, then a low degree node from its neighboring region is
chosen. The same method is followed for the other pair of nodes. If the edges are independent,
then the edge swap is made, as in Fig. 5. This edge swap connects similar degree nodes.
Against all possible edge swaps, the robustness is calculated and the edge swap that enhances
the network robustness is selected. To reduce the possibility of similar edge selection, the edges
are marked. So, in the next edge swap, these edges are not selected, hence, computational cost
is reduced.
6.2.3 Edge swap of distance based selected nodes
In this edge swap method, independent edges in the network are marked and Euclidean distance
is calculated against all the nodes. The edge swap is made in such a way that longer links
are formed between network nodes. Against the attacks, the existence of long links make the
network robust. After making the long links, the network robustness is calculated. If the network
is fully connected and the robustness is increased the edge swap is accepted and vice versa.
7 Network Topology Evolution Scheme overview
In this section, the NTES is proposed to enhance the robustness of SFT. NTES provides so-
lutions for the decentralized system. The scheme is designed to be robust against malicious
attacks by forming onion-like structure. In this structure, the center nodes of the network have a
high degree. The nodes’ degree decreases hierarchically when we move away from the center.
Considering the importance of the onion-like structure for the robustness of SFT, the net-
work topology is constructed. The NTES consists of the following operations: network topol-
ogy evolution, networks connection by one-to-many correspondence, SFT attacks, core based
attacks and a comparison of heuristic algorithms to optimize the NTES’s robustness is made.
7.1 Evolution of network topology
For malicious attacks, a small sized network is robust. The results can be observed from [62],
[70], [72]. Therefore, the network field is divided into two parts and nodes are uniformly dis-
tributed in it. During the evolution of both parts, the power-law is followed. The connection of
16
nodes have a major role in network robustness. The one-to-many correspondence is better as
compared to one-to-one correspondence [132]. Therefore, the connection of both parts is made
by one-to-many correspondence. The high degree nodes of one part connect with low degree
nodes of the other part of the network. Thus, the degree of the edges becomes smaller, therefore,
the effect of the malicious attacks on the links decreases. Two networks’ topology evolution fol-
lowing the power-law distribution are shown in Fig. 4. The network field division is indicated
by the dotted line and the two portions have the same number of nodes. The blue nodes (NA)
and black nodes (NB) represent the network A and B, respectively. Whereas NMdenotes the
mutual nodes of the network that exists in both parts. The black solid lines and double dashed
lines denote connectivity links CLand the mutual links ML, respectively. During the network
evolution, the nodes are added asynchronously in both parts.
7.2 k-core based nodes’ degree distribution
Different rings based on the degree of the nodes are presented in the onion-like structure. In
each ring, the nodes with the same degree are connected [62]. The computational cost is high to
collapse the network with malicious attacks based on degree. Therefore, due to the availability
of information about a specific node in the core, less computational cost is incurred. As a result,
in each ring, k-core decomposition determines the nodes’ degree and a node is removed from it
respective ring based on its importance. For these nodes, the core based onion-like structure is
shown in Fig. 4.
In k-core decomposition, the cores are created by removing the nodes from the network.
In Fig. 4, core C4 contains the IDs of nodes having a low degree, which are initially removed
from the network. Then the other low degree nodes are removed after recalculating the degrees
and their information is stored in the next core C3. The node removal process is repeated until
all of the high degree nodes have been removed and placed in the internal core C1. Due to the
power-law, a long tail of nodes with low degree presents in SFT. Hence, the removal of a high
degree node from the network causes a specific part of the network to collapse. Therefore, in
that case, less computational cost is required to damage the network.
7.3 Attacks on the proposed topology
It is assumed that the attackers carry complete network topology information and can execute
any attack to collapse the network. Therefore, having the knowledge about the specific type of
attack make the defender capable to manage it. To increase the effectiveness of the proposed
NTES, nodes’s core and degree based attacks are considered. In core based attack, the nodes
are removed from their respective core. The node of the inner core are removed first then the
nodes of the outer cores are removed. The core based attack is shown in the Fig. 6, where the
nodes NRare removed from the inner core. The network is not disturbed by removing these
nodes. However, as the number of removed nodes are increased the network is fragmented into
multiple subgraphs as shown in Fig. 7. Three subgraphs S1, S2 and S3 are made after the core
based attacks are happened on the network. Furthermore, in each subgraph, the high degree
nodes NMCS are present. These nodes are removed to fully collapse the network.
Moreover, the High Degree Adaptive (HDA) attack is considered to remove the nodes based
on the degree. In this attack, the degree of nodes’ present in the network is calculated and the
highest degree node is removed. Again, the highest degree node is removed by recalculating the
degree. This process is repeated until all the nodes are removed from the network.
7.4 NTES’s optimization by heuristic algorithms
The NTES is optimized by three heuristic algorithms including GA, ABC and BFO. In GA,
the edge swap is performed by considering the exclusive edges [69]. However, in both ABC
17
NA
NB
NM
NR
CL
ML
Figure 6. Nodes’ attack based on core
NA
NB
NM
NR
NMCS
CL
ML
S1
S2
S3
Figure 7. Attacks on NMCS of the network
and BFO, for the better exploration and exploitation a random position change is required. In
the proposed scheme, the nodes are stationary, therefore, it is not possible to change positions
at random. To deal with this problem, random and degree based edge swaps are used for the
exploration and exploitation, respectively. The exploitation is performed by exploiting local
information of nodes that is the degree of nodes to perform edge swap. However, when the
solution traps into the local optima the exploration by random edge swap is performed.
Table 3 presents the complete details of the limitations that are identified through the litera-
ture review. Then their proposed solutions and how they are validated is given.
8 Details of the proposed model
In this section, the details of solutions that are used for the enhancement of network robustness
are given. Initially, the overview of JAYA algorithm is discussed. Then how the JAYA algorithm
is used to enhance the robustness of SFNs is studied. Afterwards, the metric of robustness and
the process of optimizing the SFNs are given. Then the complete details of edge swaps and
their effects on the topological parameters are analyzed. Moreover, addition of nodes in MCS,
network connectivity protection by bridge edges and the effects of different attack strengths are
studied.
18
Table 3 Mapping the identified limitations, their proposed solutions with validations
Identified limitations Solutions proposed Validations done
L1: The effects of mali-
cious attacks are severe on
large-sized networks [57,
66]
S1: NTES is proposed
in which small-sized net-
works evolve
V1: The small-sized net-
works will be evolved to
validate that they are ro-
bust to random and mali-
cious attacks.
L2: The links degree that
connect the networks do
not follow the power-law
[132]
S2: Using the concept of
the interdependent links,
the networks are con-
nected
V2: The power-law de-
gree distribution will be
validated for the mutual
nodes.
L3: The change of node’s
degree in each ring con-
sidering onion-like struc-
ture is not known [57]
S3: The same degree
nodes are found using k-
core decomposition
V3: The nodes will be re-
moved based on their de-
grees, and degree based
cores will be created.
L4: Random edge swap
increases the number of
redundant operations [66]
S4: Long links are cre-
ated through distance
based edge swap
V4: Against the long
links network robustness
is calculated.
8.1 Overview of JAYA
JAYA algorithm proposed by R. Rao [133] is a Sanskrit word that means victory. The name
is given to the algorithm because in each iteration, it improves the best individual along with
the worst one more efficiently than the other optimization algorithms. It is a parameterless
algorithm, therefore, no algorithm-specific control parameters are required that make it easy to
implement. Moreover, it is a population based algorithm and the individuals are selected based
on the fitness values and are updated as in Equ. 7.
A(i+1,j,k) = A(i,j,k)+r1(i,j,1)(A(i,j,best)−|A(i,j,k)|)r2(i,j,2)(A(i,j,worst)|A(i,j,k)|),
(7)
where, A(i,j,k)is the candidate solution, i,jand krepresent that in an ith iteration the value
of jth variable of the kth individual, respectively. The A(i,j,best)and A(i,j,worst)are the best
and worst individuals in the search space, respectively. The random numbers r1and r2are used
to generate the diversity in the population and avoid the solution to stuck into the local optima.
The value of these numbers lie between 0 and 1. In JAYA, at each iteration, each individual in
the population is updated. After that, the individuals are compared with their previous values
and according to the required optimization, they are updated.
8.2 JAYA for the scale-free networks
The SFNs is optimized by JAYA algorithm. It is a population based algorithm, therefore, for
each topology, an adjacency matrix (A) is constructed and converted to a binary coded chro-
mosome as shown in Fig. 8. After each iterations, individual is updated based on the best and
the worst characteristics of the individuals present in the population. It is achieved by making
the exclusive edges of one individual into the other individuals. Moreover, at each iteration the
results are compared and based on the highest robustness of the individual, the population is
updated.
In Fig. 8, the topology of four nodes i,j,kand lforming a complete network is shown. The
adjacency matrix of the topology consists of binary numbers i.e., ai j =1, if node iis connected
with node jand vice versa. In the same way, if ai j =0 then no link is present between the
nodes iand j. Moreover, the usage of whole adjacency matrix in the formation of chromosome,
19
i
j
l
kAdjacency matrix
Topology
Chromosome
i j k l
i
j
k
l
0 1 1 1
1 0 0 1
1 0 0 1
1 1 1 0
0 1 1 1 0 0 1 0 1 0
Figure 8. Chromosome is obtained from the adjacency matrix
required extra storage space. The adjacency matrix is symmetric that means its upper and lower
triangles represent the same connections of nodes. Therefore, only the upper triangle is consid-
ered to form the chromosome because it has the complete network information and requires less
memory.
In the process of optimization, the degree distribution of the network remains same. There-
fore, the network is optimized by keeping the initial degree of the network same. The Equ. 8 is
obtained by modifying the Equ. 7, to get the best results.
Ai+1=Ai+r(Abest − |Ai|)r(Aworst − |Ai|),(8)
where, Aiis the adjacency matrix of the ith topology, whereas Abest and Aworst are the best and
worst topologies, respectively, present in the population based on fitness value. ris the number
of exclusive edges required to update the it h topology and ris selected randomly. When the
operations are performed on the current topology, the updated form is presented as Ai+1.
In Fig. 9, the complete process of implementing the JAYA algorithm is shown. The adja-
1
23
4
5
6
1
23
4
5
6
1
23
4
5
6
1
23
4
5
6
1
23
4
5
6
(a) (b)
(c) (d) (e)
Figure 9. JAYA for the optimization of SFNs (a) Current individual (b) The best
individual (c) Finding the neighbors (d) Selecting the nearest node (e) The updated
individual
20
cency matrix of current topology Aiis presented in Fig. 9(a) while the best topology Abest in the
population is given in Fig. 9(b). The difference between the topologies is calculated by consid-
ering the exclusive edges. As the exclusive edges are defined as the edges which are presented
only in one topology. Therefore, in Fig. 9(b), the edge between nodes 4 and 5 is not present
in Fig. 9(a), therefore, it is an exclusive edge. To enhance the robustness, the exclusive edges
present in the Abest needs to be made in Ai. In Fig. 9(c), the neighbor nodes are found to make
an edge of the Abest into the Ai. Here, the dotted circle represents the neighbors of node 4. The
green dotted lines show the distance of node 4 with two of its neighboring nodes. The nodes
that are in the same communication range and have independent edges as shown in Fig. 9(d)
are selected to make the exclusive edge in the Ai. Nodes 3, 4, 5 and 6 are in the same commu-
nication range and have independent edges, so in the edge swap, these nodes are considered.
The exclusive edge that is part of the Abest is made in the Aiand termed as Xexclusive1. The same
method is repeated for the worst and current individuals and represented as Xexcl usive2. After
making these exclusive edges the Equ. 8 become,
Ai+1=Ai+Xexclusive1Xexclusive2(9)
The process of exclusive edge is repeated for the Xexclusive1and Xexclusive2and the Equ. 9 is
updated to the following form.
Ai+1=Ai+XexclusiveT(10)
here, XexclusiveTis the adjacency metric obtained by exclusive edges of the best and worst topolo-
gies with the current topology. At that point, the two matrixes are adding so, the exclusive edges
are made to obtain the Ai+1. The process is repeated for all the individuals of the population. Af-
ter the completion of one iteration, the fitness value of Ai+1is compared with the Ai. If the Ai+1
has more fitness than the Ai, the population is updated with the individual having the highest
robustness.
8.3 Effects of edge swap on topological parameters
Against the edge swap methods discussed above, the topological parameters are studied and the
results are evaluated to confirm the usefulness of the specific type of edge swap. The following
topological parameters are studied to prove the efficiency of these edge swaps.
Global communication efficiency
Average clustering coefficient
Average shortest path length
Assortative coefficient
8.3.1 Global communication efficiency
The Global Communication Efficiency (GCE) is a network measure that describes the efficient
exchange of information in the network [135]. It is defined as follows.
C(G) = 1
N(N1)
i6=j
1
di j
,(11)
where, 1
N(N1)is the normalization factor and di j is the shortest path between node iand j.
Higher the value of GCE makes the network more efficient.
21
8.3.2 Average clustering coefficient
In a network, the clustering coefficient Ckdefines the node’s characteristics to form a cluster
[136]. It is defined as follows.
Ck=2Ek
k(k1)Nk
,(12)
where, Ekand Nkare the edges between the nodes and the total number of nodes, respectively,
that have degree k. The value of Ck lies in the range of [0, 1]. Where, 0 means a node has no
connection with its neighbors, when the nodes has connections with its neighboring nodes has
value 1. The overall clustering coefficient ¯
Cof a network is calculated as,
¯
C=1
N
kmax
k=1
Ck,(13)
where, kmax is the maximum value of the node degree.
8.3.3 Average shortest path length
The shortest path is defined as the minimum number of edges between two nodes [74]. More-
over, the average shortest path length is the length between all pairs of nodes. To calculate the
average shortest path length D(G), the following equation is used.
D(G) = 1
N1
i6=j
di j.(14)
8.3.4 Assortative coefficient
Assortativity is defined as the links between nodes based on properties like degree, betweenness
centrality, etc. Newman [137] first gives its concept based on the Pearson correlation coefficient
to define the characteristics of nodes that are linked with each other. It has a value in the range
of [-1, 1]. Where, 1 means that the network is highly assortative i.e., similar degree nodes are
connected. Whereas, -1 proves that the network is disassortative and 0 means nodes have no
relation i.e., they have no connection. The assortative coefficient γis calculated as,
γ=M1N
i=2i
j=1ai jkikjζ2
M1N
i=2i
j=1
1
2ai j(k2
i+k2
j)ζ2
(15)
ζ=M1
N
i=2
i
j=1
1
2ai j(ki+kj)(16)
where, Nis the total number of nodes, kiand kjare the degree of node iand j, respectively. M
is the total number of edges.
8.4 Addition of nodes in maximum connected subgraphs
In this section, nodes are added in the MCS to achieve high robustness. The malicious attacks
fragment the network into multiple subgraphs. The number of nodes in each subgraph is not the
same, some have a high number of nodes and some have less. To get a high number of nodes
in the MCS, the nodes present in the small-sized subgraphs broadcast a request message to the
nodes present in its neighboring large-sized subgraphs. If the degree threshold of the nodes
is not achieved the nodes present in the larger subgraphs receive the request message and reply
with a confirmation. After the links are formed between the nodes of small-sized and large-sized
subgraphs, the robustness is calculated. Due to an increase in the number of nodes in the MCS,
the robustness of the network is increased. The communication between the nodes is performed
through a wireless medium, therefore, no extra cost is added in the form of adding the edges.
22
8.5 Effects of bridge edge on the network
In the networks, some edges are important for the network connectivity because the removal
of these edges effects the network structure. These edges are termed as the bridge edges and
they are protected to maintain the network connectivity. These specific edges are initially found
in the network by continuously removing the edges from the network. An edge is marked as
a bridge edge if its removal fragments the network into multiple subgraphs. The nodes in the
neighbors of the bridge edge are selected and the edge swap is performed against the nodes that
form the bridge edge. The procedure to enhance the network robustness with the protection
of bridge edge is shown in Fig. 10. The edge eki is the bridge edge and is removed from the
Figure 10. Effects of bridge edge on the robustness of SFNs
network. After the removal, the neighbors of node kand iare selected. The edges ekl and ei j are
independent edges, therefore, the edge swap is performed. The edge swap increases the network
robustness by increasing the number of edges in the MCS.
8.6 Effects of different attacks strength on the network
In this section, to evaluate the network robustness, the nodes are removed based on the attacks
strength. In the particular attacks strength, specific nodes are removed in an instance. To evalu-
ate the network robustness, the attacks of the following strengths are implemented.
Attack strength 1
Attack strength 2
Attack strength 3
In the attack strength 1, 5% nodes are removed from the network. These nodes are selected
randomly and after each removal of nodes, the robustness is calculated. After multiple attacks,
the network is analyzed. Moreover, 10% and 15% nodes are removed in the attack strength of 2
and 3, respectively. The higher the attack strength paralyzes the network more efficiently.
Table 4 presents the details of the limitations that are identified through the literature review.
Then their proposed solutions and how they are validated is given.
23
Table 4 Mapping table
Identified limitations Proposed solutions Validations
L1: Heuristic algorithms
are used to optimize SFNs
L1.1: Require number of
algorithm specific control
parameter [65, 69, 70]
L1.2: They have high
computational time
S1: JAYA is implemented
that is a parameter less al-
gorithm
V1: The results will be
compared with existing
schemes to prove JAYA
effectiveness.
L2: The changes in
the topological parame-
ters against the edge swap
are not known [62,66]
S2: Edges are classified
and analyzed for the topo-
logical parameters
V2: Different topological
parameters are validated
against the edge swaps
L3: The removal of nodes
makes the network vul-
nerable [65, 69, 70]
S3: After attacks, nodes
can make connections
with the neighboring
nodes
V3: Robustness is calcu-
lated after adding nodes in
MCS
L4: To maintain the
network connectivity no
bridge edge is considered
[74]
S4: The edges are
swapped to protect bridge
edge
V4: Edges swap will
be performed, after the
bridge edge removal, to
reduce the effects
L5: Attack on a single
node is considered [62,66,
70]
S5: Network robustness
is calculated against dif-
ferent attacks strength
V5: The results will be
validated against different
attacks strength
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33
Tentative Time Table
Serial
No.
Activity Date
1 Background study and detailed literature review
2 Formulation of problem and proposing solution
3 Analysis and dissemination of results
4 Thesis Writing
34
Conference Proceedings
1Usman, M., Javaid, N., Abbas, S. M., Javed, M. M., Waseem, M. A., & Owais, M. (2021,
July). A novel approach to network’s topology evolution and robustness optimization
of scale free networks. In Conference on Complex, Intelligent, and Software Intensive
Systems (pp. 214-224). Springer, Cham. Download
2 Abbas, S. M., Javaid, N., Usman, M., Baig, S. M., Malik, A., & Rehman, A. U. (2021,
July). An Efficient Approach to Enhance the Robustness of Scale-Free Networks. In
International Conference on Innovative Mobile and Internet Services in Ubiquitous Com-
puting (pp. 76-86). Springer, Cham. Download
3Usman, M., Javaid, N., Khalid, A., Nasser, N., & Imran, M. (2020, June). Robustness
Optimization of Scale-Free IoT Networks. In 2020 International Wireless Communica-
tions and Mobile Computing (IWCMC) (pp. 2240-2244). IEEE. Download
35
PART II
Recommendation by the Research Supervisor
Name: Dr. Nadeem Javaid Signature:_____________________ Date: March 19, 2021
Recommendation by the Research Co-Supervisor
Name: Dr. S. M. Gulfam Signature:_____________________ Date: March 19, 2021
Signed by Supervisory Committee
S.# Name of Committee member Designation Signature & Date
1 Dr. Nadeem Javaid Associate Professor
2 Dr. Mariam Akbar Assistent Professor
3 Dr. Saif ur Rehman Khan Lecturer
Approved by Departmental Advisory Committee
Certified that the synopsis has been seen by members of DAC and considered it suitable for
putting up to BASAR.
Secretary
Departmental Advisory Committee
Name: _____________________________
Signature: _____________________________
Date: _____________________________
Chairman/HoD: ____________________________
Signature: _____________________________
Date: _____________________________
36
PART III
Dean, Faculty of Information Sciences & Technology
_____________________Approved for placement before BASAR.
_____________________Not Approved on the basis of following reasons
Signature_____________________Date________
Secretary BASAR
_____________________Approved for placement before BASAR.
_____________________Not Approved on the basis of following reasons
Signature_____________________Date________
Dean, Faculty of Information Sciences & Technology
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________
Signature_____________________Date________
37
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