Content uploaded by Priyadarshini Ramasubramanian
Author content
All content in this area was uploaded by Priyadarshini Ramasubramanian on Apr 05, 2022
Content may be subject to copyright.
Makespan of routing and security
in Cross Centric Intrusion
Detection System (CCIDS) over
black hole attacks and rushing
attacks in MANET
Rajendran N.
Department of Information Technology,
B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
Jawahar P.K.
Department of Electronics and Instrumentation Engineering,
B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India, and
Priyadarshini R.
Department of Information Technology,
B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
Abstract
Purpose –The purpose of this paper is to apply security policies over the mobile ad hoc networks. A mobile ad
hoc network refers to infrastructure-less, persistently self-designing systems; likewise, there is a noteworthy
innovation that supplies virtual equipment and programming assets according to the requirement of mobile ad
hoc network.
Design/methodology/approach –It faces different execution and effectiveness-based difficulties. The
major challenge is the compromise of performance because of unavailable resources with respect to the
MANET. In order to increase the MANET environment’s performance, various techniques are employed
for routing and security purpose. An efficient security module requires a quality-of-service (QoS)-based
security policy. It performs the task of routing and of the mobile nodes, and it also reduces the routing cost
by finding the most trusted node.
Findings –The experimental results specify that QoS-based security policy effectively minimizes the cost,
response time as well as the mobile makespan (routing cost and response time) of an application with respect
to other existing approaches.
Research limitations/implications –In this paper, the authors proposed an enhancement of Cross Centric
Intrusion Detection System named as PIHNSPRA Routing Algorithm (PIHNSPRA).
Practical implications –It maps the security with the secure IDS communication and distributes the
packets among different destinations, based on priority. This calculation is proposed for the purpose of
routing and security by considering greatest throughput with least routing cost and reaction time.
Social implications –When the concept is applied to practical applications. Quality of Service introduced in
the proposed research reduces the cost of routing and improves the throughput.
Originality/value –The proposed calculation is tested by NS2 simulator and the outcomes showed that the
execution of the calculation is superior to other conventional algorithms.
Keywords Routing, Security policy, Cross Centric Intrusion Detection System, Mobile makes span,
Routing algorithm, Self-configuring networks
Paper type Research paper
1. Introduction
Mobile ad hoc network is a connection-less as well as a permanent centralized serverless
network model for dynamically stipulated access of data in a remote controlled mode. As
scalable mobile nodes and routing services are operated through efficient routing security
techniques on the basis of data blockage and the communication time in routing path
International Journal of Intelligent
Unmanned Systems
Vol. 7 No. 4, 2019
pp. 162-176
© Emerald Publishing Limited
2049-6427
DOI 10.1108/IJIUS-03-2019-0021
Received 25 March 2019
Revised 24 April 2019
Accepted 13 May 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2049-6427.htm
162
IJIUS
7,4
(Li et al., 2014), scalable mobile nodes discovery is a significant technology behind mobile ad
hoc network that allows sharing of RREQ/RREP among multiple nodes for the utilization of
the source and destination to the complete communication toward the route (Hu et al., 2008;
Cheng et al., 2006). Node routing consists of attackers that provide a separated attacked
environment with respect to the malicious nodes (Chang and Kuo, 2009).
The whole functionality of MANET can be arranged into three huge modules in
particular: node formation, routing and security. First, node formation module is the process
in which the source requested RREQ/RREP is send over the nearby nodes for the
availability. Once the provisioning target node is replied, then the next step is data routing.
For the enhancement of Cross centric Intrusion Detection System (CCIDS) performance, both
modules, such as node formation and routing, should be given equal importance so that it
allows the destination to send the required data optimally as per the nodes routing security.
Second, in nodes security module, the RREQ/RREP packets are send to the destination,
and subsequently target node is replied. Hence, the node is approved for data transmission
over these two nodes without any interruption like black hole attack and rushing attacks in
MANET. This data communication is called security module.
The nodes security module targets the destination and route pattern over it for attacker
as per the routing cost, response time of the node communication and attacker involvement.
It basically balances the node communication and attacker involvement of the nodes
security module in order to minimize the routing cost, and it enhances the performance by
decreasing the response time.
Several challenges are faced in the routing nodes security environment. Out of the numerous
challenges faced in the security threats environment, one of the critical challenges is to
accomplish the attacker-free transmission so as to maximize the attacker. The attacker
maximization is a process of removal of attacker from the alive network by reducing the energy
of the node so as to maximize the attacker. So we can reduce the quality-of-service (QoS)-based
security policy like node energy and node lifetime of attacker (Al-Fuqaha et al., 2008).
With the expansion of the MANET, the complexity of security environment also increases
against the attacker node. Furthermore, the PIHNSPRA Routing Algorithm (PIHNSPRA) is
categorized as trusted and attacker node for Enhanced CCIDS. In trusted node, the data
communication process is distributed equivalently throughout the network region range. But
in attacker node involvement, the data communication process may not be distributed
equivalently throughout the network region range. Even though attacker node performs the
task of routing through malicious mobile nodes, it causes the routing security environment
(routing cost and response time). To improve the Enhanced CCIDS, there is a need to improve
the (routing cost and response time), whenever there is increase in routing cost by the existing
algorithms response time are presence in the mobile node routing there is a possibility of
attacker to evade. To avoid the attacker in the network, the data communication process might
be calculated within the network region range. This region is also used to detect the trusted
node in the network region. The PIHNSPRA Routing Algorithm (PIHNSPRA) is useful to
detect the attacker node on the Enhanced CCIDS. This is used to reduce the attacker node
involvement and increase the data communication process throughout the network region
range. Also, it increases the routing against malicious nodes. To enhance the security with the
secure IDS communication and distribution of the packets, the proposed research helps to
point out the routing and security threats over the network by maximizing throughput and
minimizing routing cost. In the PIHNSPRA Routing Algorithm (PIHNSPRA), the security is
enhanced among participating nodes and the process is performed with the help of various
algorithms such as QoS-based security, PIHNSPRA Routing Algorithm (PIHNSPRA) and
secure IDS communication (Fapojuwo et al., 2004). In this paper, QoS-based security policy
techniques are discussed, and this technique is named as Enhanced CCIDS. The CCIDS works
on the basis of PIHNSPRA Routing Algorithm (PIHNSPRA). PIHNSPRA process is used to
163
Makespan of
routing and
security in
CCIDS
perform the task of routing of the mobile nodes for routing cost and response time. As per the
security policy, many algorithms were proposed by various researchers, but no one discussed
about the QoS-based security policy. In that technique, we used the routing cost and response
time. These two factors are increasing the security threads in the routing over black hole
attacks and rushing attacks. These systems as an interim measure are called as the Enhanced
CCIDS. The PIHNSPRA Routing Algorithm (PIHNSPRA) is applied to the CCIDS for
increasing the QoS-based security policy threads. Finally, secure IDS communication
distributes the packets to improve routing, and it also ensures the security by maximized
throughput and minimized routing cost (Rezaiesarlak and Manteghi, 2015; Jin et al., 2018).
2. Literature review
2.1 QoS support in MANETs
Li et al. (2014) talked about the routing protocol for hybrid wireless networks (Li and Shen, 2014).
It combines two systems, for example, MANET and wireless infrastructure network (Meng and
Song, 2013). They proposed the distributed routing convention as for the quality-of-service,
namely, overhead, portability flexibility, versatility and transmission delay (De Rango et al.,
2012). This proposed system is used to resolve the resource scheduling problem. Finally, they
concentrated on the neighbor selection for transmission delay and transmission time. Also, it
increases the transmission throughput and data redundancy. In addition, Bouk et al. (2012)
presented the QoS and non-Qos path parameters between the mobile node and gateway node for
passage determination.
This selection criterion focused on the end-to-end delay, link capacity, node speed,
number of hops, path availability period and residual energy. Finally, they are used to
introduce a feedback system for updated path. Hanzo et al. additionally examined about the
routing for MANET regarding the QoS affirmations (Hanzo and Tafazolli, 2011). They
presented the two conventions, for example, QoS routing protocol and a confirmation
control protocol (Kumar et al., 2018). At last, their proposed framework was used to enhance
the execution of their protocols as far as mobility, shadowing and link. Finally, their
outcome gives the productivity in transmission rates, routing reliability and throughput
(Goudarzi and Hosseinpour, 2010). Calafate et al. (2009) declared the roles and responsibility
of QoS in MANETs. Bandwidth (Chen et al., 2017), delay and jitter are taken as the
parameters in QoS architecture. Utilization of these parameters accomplished system loads,
hub portability degrees and routing in MANETs. Zhang and Lee (2005) talked about
security of MANET in their exploration section ( Jiang et al., 2004).
2.2 Routing security
Yang et al. (2004) examined about the security difficulties and their answers in MANET.
They suggested the remote medium and system topology for stringent asset limitations
issue. Finally, they identified the security issues against the security design in the MANET
link. The complete security solutions in mobile ad hoc networks should cover link layers and
network layer security components such as prevention, detection and reaction. Hanzo et al.
and Surendran et al. discussed about the QOS enabled routing in MANETs (Surendran and
Prakash, 2015). Hanzo focused on the QoS routing metrics; resources are interactions with
the MAC protocol (Hanzo and Tafazolli, 2007). Surendran discussed about the various
attacks in MANET and gave the solution with respect to the trust and reputation
(Surendran and Prakash, 2015). Tolerant protocols were the main objective of their research.
They improved the better routing in MANET with the help of QOS enabled routing.
Burmester et al. explored the Security of Route Discovery in MANETs. They examined
security in route revelation, for example, routing protocols security, disseminated systems
plan security, PC correspondence systems security, processing and data frameworks
security and MANET security (Burmester and De Medeiros, 2009).
164
IJIUS
7,4
2.3 Routing security with QoS
Hejmo et al. (2006) examined about the DoS-resistant QoS. This DRQoS keeps away from the
capacity of per-stream state and QoS provisioning and DoS insurance. Chen et al. discussed
about the QoS-aware routing in MANET. They focused on the traffic in the routing with the
help of QoS by bandwidth estimation (Chen et al., 2017; Chen and Heinzelman, 2005).
Finally, they achieved the packet delivery ratio, packet delay, energy dissipation,
end-to-end throughput. Chintalapalli et al. proposed the secure routing model named as lion
algorithm and whale optimization algorithm (M-LionWhale) for optimization in MANET
(Chintalapalli, 2018). They concentrated the data forwarding optimal route by hybrid
optimization algorithm. They proposed the multi-objective optimization model for
improving the QoS such as energy, distance, link lifetime, delay and trust.
Tsuda et al. (2016) proposed the malicious node identification (data replacement attacks)
by k-query processing (Gao et al., 2018). This system is used to detect attacks on the time of
message exchanges (Tsuda et al., 2016). Anand et al. (2016) discussed about the secure and
reliable routing in MANET. They proposed the partially distributed dynamic model,
which ensures the secure routing scenario. Finally, they concentrated the dynamic timeout-
based mechanism to block misbehaving nodes. Dynamic credit allotment mechanism is used
for finding behavior of a node; moreover, the research addresses the problem of
misbehaving nodes.
2.4 Black hole attack in mobile ad hoc networks
Chengetanai et al. proposed the black hole attacks minimizing process in MANET
(Chengetanai, 2018). Dhende et al. (2018) took survey about black hole attack in mobile ad
hoc networks. They took various schemes for attacker detection ( Jung et al., 2018; Dhende
et al., 2018), such as goal-based reply and next hop information scheme, neighbors opinion,
sequence number, shared hop and sequence number, packet delivery information, 2-ACK,
sequence number and neighbors voting, DRI table and cross checking, ignorance, extended
data routing information table. These schemes are purely reducing the black hole attack
with an effective manner. Sangeetha et al. discussed about security in mobile ad hoc network
by selective acknowledgment with adaptive acknowledgment. It is used for detecting
malicious nodes (Sangeetha and Kumar, 2018). They added intrusion detection mechanisms
for providing the bonus security to the MANET.
2.5 Packet loss in MANETs
Khan et al. in their paper build the impedance, line flood, and hub versatility against the
packet loss examination (Khan et al., 2017). They utilized the optimized link state routing
convention with the fine-grained investigation of packet loss (Cardenas et al., 2004).
Hurley-Smith et al. proposed new analysis called SUPERMAN (Hurley-Smith et al., 2017).
This convention assesses the hub validation, network access control and secure
correspondence in a network layer.
2.6 Trust-based security management
Cho et al. (2011) took survey for trust management in MANET. They discussed about the
multidisciplinary concepts of trust such as autonomic computing, economics, industrial
engineering, organizational management, philosophy, psychology, sociology and system
engineering. They explained trust, trustworthiness and risk of individual concepts. Especially,
trust properties in MANETs are context dependent, subjective dependent, fully distributed
dependent, highly customizable dependent and selfishness dependent (Betances et al., 2016;
Veeraiah and Krishna, 2018). These are the factors are evaluated in their trust-based security
management. Nguyen et al. (2016) and Movahedi et al. discussed about the impact of
165
Makespan of
routing and
security in
CCIDS
trust-based security in MANET (Movahedi et al., 2016). They mainly concentrated the
epidemic broadcast delay in the mobility and trust models with respect to the depoissonization
(Cho et al., 2011).
2.7 Efficiency, reliability and security concern
Sheikh et al. (2014) proposed the protocol in mobile ad hoc network against the efficiency,
reliability and security. Their workbench depends on the voice over internet protocol (VoIP)
for following the strict timing constraints. Finally, they gave the protected ongoing VoIP
benefit on MANET. The after effect of their examination set up a mobile ad hoc network for
quickly deployable VoIP correspondence with survivable, productive, dynamic and secure
systems administration.
2.8 Intrusion detection system (IDS) security in mobile ad hoc networks
Nadeem et al.,Buet al. (2011), Mohammed et al. (2011) and Liu et al. (2009) talked about the
interruption recognition framework security in mobile ad hoc networks (Nadeem and
Howarth, 2013). Nadeem et al. concentrated on the different attacks in network layer; they
gave the overview of the network layer attacks and interruption identification and its
security instruments. They proposed the point location calculations for IDSs attack
( Jung et al., 2018). Bu et al. (2011) talked about the prevention action-based way, which deals
with ensuring high-security MANETs by continuous client validation. Likewise, it identifies
malicious activities in IDSs. This ideal strategy fixes auxiliary issue for a huge system with
hubs. Mohammed et al. (2011) learned about the leader election in interruption.
They redressed the two hindrances, for example, selfish resources and resource
utilization. So, this obstacle causes the flooding of system. They tended to tackle this
issue of selfish nodes, in view of Vickrey, Clarke and Groves model. This model gives the
ideal optimal election issue by nearby decision calculations. This calculation is a blend of
Cluster-dependent Leader Election and Cluster-independent Leader Election. Finally, they
advocated the adequacy of the leader election in interruption discovery plans. Liu et al.
(2009) proposed the counteractive action-based methodologies and location-based
methodologies. This is a system of consolidating interruption identification and ceaseless
validation in MANETs. These methodologies are utilized for persistent verification and
interruption discovery to distinguish framework security state. They likewise utilized
powerful programming-based concealed Markov demonstrate booking calculations in
interruption discovery and persistent verification.
3. QOS-based security policy
A QoS-based security module is described where the source node and destination node are
facilitated for the better communication and avoids malicious activities. The implementation
of QoS-based security policy enables nodes to analyze history as well as previous interaction
with the neighbor mobile nodes. A security and routing application negotiates packets
transmission in QoS-based security module. Based on this negotiation, QoS-based security
module can further make measures for node discovery, node communication, packet
forecast and so on (Awwad et al., 2012). This QoS parameters (such as node discovery, node
communication and packet forecast) policy ensures the execution of data transformation
with QoS limitation. The limitation is listed in the following policy parameters ethics:
(1) policy parameters ethics ensure understanding of the communication process and
distribution equivalently with respect to reduction of malicious activities;
(2) ensure the delivery of data and its communication history as per QoS-based security
policy; and
166
IJIUS
7,4
(3) performance evaluation and data communication as per QoS-based security policy
by minimizing routing cost and response time.
Enhanced CCIDS works on the basis of the PIHNSPRA Routing Algorithm. It was
developed and analyzed for the purpose of routing cost and response time in the MANET.
PIHNSPRA is a simple algorithm used for security with the secure IDS communication and
distribution of the packets. PIHNSPRA was implemented and combined with past
interaction history based nearby Path Secure Routing Algorithm. Also, it is simulated and
compared on various metrics such as attacker detection ratio, cluster distance (CE distance),
overhead measure, throughput measure:
(1) PIHNSPRA Algorithm solves the following problems:
•PIHNSPRA Routing Algorithm mainly solves the data communication process
deficiency around most IDS system.
•By using the path origin selection, it resolves the problem occurs in shortest
route formulation.
•PIHNSPRA Algorithm solves separation/travel time problem. It causes the
network route options. This is preventing the black hole attack, as well as travel
time and node location.
•PIHNSPRA Algorithm solves route formation (routing) data transparent rate.
•PIHNSPRA Algorithm rectifies these concerns against optimal routes that are
also saved in past interaction history. The PIHNSPRA results provide the
different shortest path that does not consider any black hole attack.
PIHNSPRA can be improved so as to enhance its performance, if following security and
routing issues are focused:
•PIHNSPRA explains the scenario of routing of one sender node to another receiver
node. Maybe, the idea of security and routing can likewise be added to it, which
promptly enhances the general execution of the MANET communication. Hence, the
discovery, node communication and packet forecast increase.
•In addition to this, an effort can be made to automate the PIHNSPRA algorithm, in
order to develop and organize MANET environment.
Meanwhile, QoS-based security policy focuses on above-stated issues; another IDS, is
narrative intrusion detection system (N_IDS). This works on the basis of the cross centric
attacks framework. This framework covers Preset Time Interval Algorithm (PTIA). In the
proposed N_IDS algorithm, rushing attacks are resolved by broadcasting the messages over
the nearby neighbor node. This message broadcasting is used to find flagging nodes
between two participating nodes. In this era, the time period and packet delivery ratio are
chosen for individuals conversation. Time period and packet delivery ratio are integrated
with N_IDS, which is based on the PTIA of the respective cluster nodes.
For future research, the proposed algorithm can be combined for the more effective and
efficient communication, and hence it improves the intrusion detection attacks in MANETS by
cross centric attacks framework. In this paper, QoS-based security policy of cross centric
attack framework will be focused along with the PIHNSPRA Algorithm and PTIA to monitor
the overall functioning of the MANET by removing the black hole attack and rushing attacks.
4. QoS-based security policy narrative IDS
Enhanced CCIDS mimics the IDS activity of the participating nodes on the IDScommunication
system. In MANET, the source node acts as the routing communication initiator and need
167
Makespan of
routing and
security in
CCIDS
destination, that is receiver node. From this source node and receiver node, we can calculate
the communication efficiency of each exchange with respect to the trusted node as well as the
attacker nodes.
4.1 Communication efficiency of routing cost assignment
In context of the source node, similar to the evaluation of the source node for its communication
efficiency, routing path is also evaluated for its data exchange (including RREQ/RREP) on the
basis of the node energy, that is energy spending in each transaction. Similarly, the way the
destination node, that is receiver nodes, has communication efficiency evaluation, the
communication efficiency evaluations of trusted node as well as attacker node are also evaluated
for their network nodes size. Communication efficiency of routing cost is improved by the
Enhanced CCIDS; this also improves the routing cost and response time in the mobile node
routing. To increase the communication efficiency against the attacker in the network, the data
communication process might be used. PIHNSPRA Routing Algorithm (PIHNSPRA) is
proposed, which helps to detect the attacker node on the Enhanced CCIDS. Depending upon the
participatory strength of the nodes, the PRN of node is assessed with the help of response time,
efficiency and consistent time taken in each transaction among the nodes. In order to sustain this
principle, the overall communication efficiency of the nodes is calculated, which, in turn, depends
upon the response time and also the trust factor of the node. The response time in different levels
is calculated to conclude whether the node is trusted node or not. To prove the same, the
following equations in this section are formulated on the basis of the maximum communication
response time, minimum communication response time and fixed interval time for PRN between
the source and destination. Due to these policy-based factors, the proposed system reduces the
attacker node involvement and increases the data communication process throughout the
network region range. Also, it increases the communication efficiency of routing cost assignment
against malicious nodes.
Hence, to calculate the communication efficiency assignment of source and destination
and for maximum communication routing efficiency of trusted node:
CenðÞ¼ 1
DmaxFTðSrc;RevrÞ
Ns
;(1)
C
e
(n) is the communication efficiency of node; D
max
the maximum data communication rate;
FT
(Src,Revr)
the fixed time interval for source and receiver nodes; N
s
the network node size.
For minimum communication routing efficiency of attacker node:
AenðÞ¼ log 1
FTðSrc;RevrÞTmin
Ns;(2)
A
e
(n) is the attacker efficiency of node; T
min
the minimum data communication rate;
FT
(Src,Revr)
the fixed time interval for source and receiver nodes; N
s
the network node size:
FTðSrc;RevrÞ¼X
k
x¼1
PRNðSrc;RevrÞ
;(3)
FT
(Src,Revr)
is the fixed time interval between source and destination; kthe total number of
nodes in the network; PRN is participating route nodes (PNR) between source and destination.
4.2 Communication efficiency of response time assignment
This section is used to calculate the PNR of the nodes: again, in context of the source node,
similar to the evaluation of the source node for its response time efficiency, response time is
168
IJIUS
7,4
also evaluated by time taken for their data exchange (including RREQ time/RREP time)
interval on the basis of the node response, that is time taken in each transaction. Similarly,
the way the destination node, that is receiver nodes, has communication efficiency response
time evaluation, the communication efficiency response time evaluations of trusted node as
well as attacker node are also evaluated for their network nodes size. Hence, to calculate the
communication efficiency response time assignment of source and destination and for
maximum communication response time efficiency of attacker node:
AenðÞ¼ log 1
FTðSrc;RevrÞTmin
Ns;(4)
A
e
(n) is the attacker response time efficiency of node; T
min
the minimum data
communication rate; FT
(Src,Revr)
the fixed time interval for source and receiver nodes; N
s
the network node size.
For minimum communication response time efficiency of trusted node:
CenðÞ¼ Dmax FTðSrc;RevrÞ
Ns;(5)
C
e
(n) is the communication response time efficiency of node; D
max
the maximum data
communication rate; FT
(Src,Revr)
the fixed time interval for source and receiver nodes; N
s
the
network node size:
FTðSrc;RevrÞ¼X
k
x¼1
PRNðSrc;RevrÞ
K
;(6)
FT
(Src,Revr)
is the fixed time interval between source and destination; kis the total number of
nodes in the network; PRN is the participating route nodes between source and destination.
4.3 Security policy narrative
4.3.1 Routing cost assignment. Once the fixed time (FT) interval evaluation of the entire
network is performed, the next step is to transfer the data to the destination as per QoS-
based security policy. The new transaction is allocated by policy narrator called routing cost
assignment. This routing cost assignment considers the total energy of the transaction at
iteration of routing efficiency of trusted node. Such total maximum communication routing
efficiency of trusted node is computed on the basis of the three factors:
(1) the maximum communication routing efficiency with respect to the nearest neighbor
to destination, which has higher routing efficiency with high data communication
rate;
(2) the maximum communication routing efficiency with respect to the best route of the
entire network node Kwith high data communication rate; and
(3) random node selection of trust value, a uniform random number generated to
participating node within the range (0, 1).
Finally, if C
e
(n) is higher than A
e
(n), an maximum communication routing efficiency is
granted by the network; otherwise, a response time is checked.
4.3.2 Response time assignment. Response time assignment is the part of response time
improvement; this consists of auto routes arrangement. This automatic route arrangement
works on the basis of the FT interval and response time, transaction history and energy of
the transaction. According to this, such total maximum communication response time
efficiency of trusted node is computed with respect to the remaining energy. The destination
169
Makespan of
routing and
security in
CCIDS
and route pattern over it for the attacker is based on the routing cost, response time of the
node communication and attacker involvement. In order to decrease the response time and
the routing cost, the routes are arranged according to their network size of the MANET.
For response time, the routes are arranged according to their network size of the
MANET. This response time assignment considers three parts of the process flow. However,
the higher routing efficiency with higher response time causes the data transaction by:
(1) the higher value if fixed time interval FT
(Src,Revr)
is greater than response time
PRN
(Src,Revr)
, the process data transaction will get maximum communication
response time efficiency;
(2) meanwhile, remaining FT intervals are processes for next transaction; and
(3) the total energy of the transaction should be calculated at iteration of response time
efficiency of both trusted and attacker node.
According to this, such total maximum communication response time efficiency of trusted
node is computed with respect to the remaining energy.
5. Benchmark functions
A set of 500 nodes participated to evaluate the performance of QoS-based security policy.
Sections 3 and 4 present the functions used in the proposed research. The optimization of
performance evaluation (Chintalapalli, 2018) of QoS-based security policy is done with possible
combinations of various routing paths, for selective paths are chosen from the widely adopted
network (Bouk et al., 2012). The security analysis of the PIHNSPRA Routing Algorithm and
PTIA is performed. It shows that PIHNSPRA Algorithm removes the black hole attack and
PTIA removes the rushing attacks, and also both algorithms monitor the overall functioning of
the MANET by removing the malicious activities, hence outperforming in comparison with
other algorithms. The benchmark functions are calculation of communication efficiency ratio
(CER), average transmission delay (ATD), average throughput (AT), destination utilization index
(DUI), Neighbor Utilization Index (NUI). All these calculations are consolidately called as the
benchmark functions; these individual functions are discussed in detail in the following section.
The performance of QoS-based security policy is evaluated through extensive simulation
experiments conducted on the widely used Network Simulator-2. The AT and AT delay are
calculated by total transaction delay divided by total number of packets. The end-to-end
delay and average time interval with the throughput are calculated by simulating 100 nodes.
The time interval is noted to be 0.20‒0.45 nano seconds. The performance evaluation and
analysis is done by the PIHNSPRA Routing Algorithm and PTIA. These two algorithms
increase the throughput and reduce the routing distance and cost. At the same time, they
also remove the black hole attack and rushing attack. To conclude, malicious activities are
detected, and at the same time, AT increases. To support the same, trust value is calculated
for the correct selection of nodes in a route path.
The following execution measurements are utilized to assess the execution of QoS-based
security arrangement parameters.
5.1 Communication efficiency ratio (CER)
CER represents the lost packet competence of the network. It is the percentage of delay
sensitive completed successfully within their respective FT:
CER ¼Number of successful transferred packets
Total number of packets 100
%:(7)
170
IJIUS
7,4
5.2 Average transmission delay (ATD)
ATD also represents the lost packet ability of the network. It is the average delay observed
by all the delay sensitive transmission:
ATD ¼Total transaction dely
Total number of packets:(8)
5.3 Average throughput (AT)
AT represents the capacity of a network to deal with a developing measure of exchange and
its potential to be broadened to accommodate that development. It is computed as the total
number of transactions (both involvement of normal node and the attacker nodes) divided
by the average execution time (Chen et al., 2018). Let qbe the total number of tasks, then ST
will be given as:
AT ¼Total number of transaction
Transaction time w:r:t:normal nodeþTransaction time w:r:t:attacker nodeðÞ=Fixed time
!100
!%:
(9)
5.4 Destination utilization index (DUI)
DUI represents the consumption of the nearby nodes during simulation. It is computed as
the total active time of the entire source divided by a total number of transaction time (both
involvement of remaining transaction time (RTT) of a normal node and the RTT attacker
nodes). The DUI will be given as:
DUI ¼RTT w:r:t:normal nodeþRTT w:r:t:attacker node
Total number of transaction time :(10)
5.5 Neighbor utilization index (NUI)
NUI represents the utilization of the neighbor nodes with respect to the response time
interval. It is computed as the ratio of the total execution time of all the transaction over the
entire active time of all the neighbor nodes:
NUI ¼Total execution time
Total active time of all neighbor nodes:(11)
Simulation settings are used in the experiments as shown in Table I.
5.6 Packet transmission deadline
Deadlines for packet transmission are estimated concerning the average processing
energy of the respective source and destination. No deadline is considered for the
attacker transmission.
5.7 Packet arrival rate
For simulation workbench, packet arrival follows various time interval distributions with
average response time. For real simulation, packet arrival rate is taken according to the
arrival time given in the current route path.
171
Makespan of
routing and
security in
CCIDS
5.8 Attacker failure rate
Attacker failure is drawn using packet transmission deadline. It is defined as attacker
inference/sec on the current route path.
Results are obtained by varying one of the three parameters (i.e. packet
transmission deadline, packet arrival rate and attacker failure rate) while keeping the FT
interval set.
6. Conclusion
Makespan of routing and security in CCIDS is new security outlines that keep away the
black hole attack and rusting attacks from MANET. This paper, first, focused on secure
routing to a destination on the opened network, and it also ensures the convenient and
reliable communication with QoS parameters. This proposed research addresses
authenticity services and security dimensions in MANETs. This proposed system
fulfilled QoS-based security policy such as communication efficiency of routing cost
assignment and communication efficiency of response time assignment.
Moreover, the security policy narrative is intended to provide a secure environment
between routing cost assignment and response time assignment. Namely, it extends
protection to routing services with high-cost security. It also provides benchmark
functions such as CER, ATD, AT, DUI, NUI, packet transmission deadline, packet arrival
rate, attacker failure rate. These measures are used to self-estimate the proposed
authenticity services and security dimensions in MANETs. Enhanced CCIDS provides
security across all MANETs nodes with lower routing cost and response time in their
respective routing protocols. The future scope of the proposed routing is to extend the
routing in wired and wireless artificial medium. Also, the number of mixed region was
taken for the routing. The regional preserved number of nodes may be used in the future;
the node dissimilarity might be reduced even if it has the similar interaction history. The
Enhanced CCIDS is proposed in MANETs nodes for reducing the routing time and cost
with the response time. This framework can be extended for the different protocols and
many more malicious attacks. The attacks considered in the protocol can be removed and
the efficiency of routing, packet transmission, delivery ratio, throughput and performance
can be increased by adopting different security policies and factors. In case of zone
routing protocol, the DoS attacks and Sybil attacks can be considered with the hybrid
Simulation parameter Value
Channel and capacity Wireless mobile channel with 2 Mbps
Mac 802_.11
Transmit power 0.3 J
Receiver power 0.1 J
Initial energy 11 J
Antenna Single omni antenna
Max packet 500
Number of nodes simulated 100
Cp&Sc ./cbr& Nodes100
Simulation time 200 ns
Routing Routing model
Initial routing node count 100
End routing nodes 192.168.0.100
Node pattern Trust value (sender, receiver)
Time interval 0.20 ns ~ 0.45 ns
Routing PIHNSPRA Routing Algorithm
End-to-end delay 0 ns ~ 0.0.15 ns
Table I.
Simulation details
172
IJIUS
7,4
attacks such as the black hole and warm hole attack in multi layers. The efficiency and
routing time and cost can be increased if these attacks are prevented with various security
policies, which will be scope of the future work.
References
Al-Fuqaha, A., Khan, B., Rayes, A., Guizani, M., Awwad, O. and Brahim, G.B. (2008),
“Opportunistic channel selection strategy for better QoS in cooperative networks with
cognitive radio capabilities”,IEEE Journal on Selected Areas in Communications, Vol. 26 No. 1,
pp. 156-167.
Anand, A., Aggarwal, H. and Rani, R. (2016), “Partially distributed dynamic model for secure and
reliable routing in mobile ad hoc networks”,Journal of Communications and Networks, Vol. 18
No. 6, pp. 938-947.
Awwad,O.,Al-Fuqaha,A.,Khan,B.andBrahim,G.B.(2012),“Topology control schema for better
QoSinhybridRF/FSOmeshnetworks”,IEEE Transactions on Communications, Vol. 60 No. 5,
pp. 1398-1406.
Betances, A., Hopkinson, K.M. and Silvius, M. (2016), “Context-aware routing management architecture
for airborne networks”,IET Networks, Vol. 5 No. 4, pp. 85-92.
Bouk, S.H., Sasase, I., Ahmed, S.H. and Javaid, N. (2012), “Gateway discovery algorithm based on
multiple QoS path parameters between the mobile node and gateway node”,Journal of
Communications and Networks, Vol. 14 No. 4, pp. 434-442.
Bu, S., Yu, F.R., Liu, X.P. and Tang, H. (2011), “Structural results for combined continuous user
authentication and intrusion detection in high security mobile ad-hoc networks”,IEEE
Transactions on Wireless Communications, Vol. 10 No. 9, pp. 3064-3073.
Burmester, M. and De Medeiros, B. (2009), “On the security of route discovery in MANETs”,IEEE
Transactions on Mobile Computing, Vol. 8 No. 9, pp. 1180-1188.
Calafate, C.T., Malumbres, M.P., Oliver, J., Cano, J.C. and Manzoni, P. (2009), “QoS support in MANETs:
a modular architecture based on the IEEE 802.11 e technology”,IEEE Transactions on Circuits
and Systems for Video Technology, Vol. 19 No. 5, pp. 678-692.
Cardenas, A.A., Benammar, N., Papageorgiouand, G. and Baras, J.S. (2004), “Cross-layered security
analysis of wireless ad hoc networks”, Technical report, DTIC document, Maryland Univ
College Park Dept of Electrical And Computer Engineering.
Chang, B.J. and Kuo, S.L. (2009), “Markov chain trust model for trust-value analysis and key
management in distributed multicast MANETs”,IEEE Transactions on Vehicular Technology,
Vol. 58 No. 4, pp. 1846-1863.
Chen, L. and Heinzelman, W.B. (2005), “QoS-aware routing based on bandwidth estimation for
mobile ad hoc networks”,IEEE Journal on Selected Areas in Communications, Vol. 23 No. 3,
pp. 561-572.
Chen, Y.H., Wu, E.H. and Chen, G.H. (2017), “Bandwidth-satisfied multicast is addressed by multiple trees
and network coding in lossy MANETs”,IEEE Systems Journal, Vol. 11 No. 2, pp. 1116-1127.
Chen, Y.H., Hu, C.C., Wu, E.H., Chuang, S.M. and Chen, G.H. (2018), “A delay-sensitive multicast
protocol for network capacity enhancement in multirate MANETs”,IEEE Systems Journal,
Vol. 12 No. 1, pp. 926-937.
Cheng, H., Cao, J. and Wang, X. (2006), “A heuristic multicast algorithm to support QoS group
communications in the heterogeneous network”,IEEE Transactions on Vehicular Technology,
Vol. 55 No. 3, pp. 831-838.
Chengetanai, G. (2018), “Minimising black hole attacks to enhance security in wireless mobile ad hoc
networks”,2018 IST-Africa Week Conference,May 9.
Chintalapalli, R.M. (2018), “M-LionWhale: multi-objective optimization model for secure routing in
mobile ad-hoc network”,IET Communications, Vol. 12 No. 12, pp. 1406-1415.
173
Makespan of
routing and
security in
CCIDS
Cho, J.H., Swami, A. and Chen, R. (2011), “A survey on trust management for mobile ad hoc networks”,
IEEE Communications Surveys & Tutorials, Vol. 13 No. 4, pp. 562-583.
De Rango, F., Guerriero, F. and Fazio, P. (2012), “Link-stability and energy aware routing protocol in
distributed wireless networks”,IEEE Transactions on Parallel and Distributed Systems, Vol. 23
No. 4, pp. 713-726.
Dhende, S.L., Shirbahadurkar, S.D., Musale, S.S. and Galande, S.K. (2018), “A survey on black hole
attack in mobile ad hoc networks”,2018 4th International Conference on Recent Advances in
Information Technology (RAIT), IEEE, pp. 1-7.
Fapojuwo, A.O., Salazar, O. and Sesay, A.B. (2004), “Performance of a QoS-based multiple-route ad-hoc
on-demand distance vector protocol for mobile ad hoc networks is analyzed”,Canadian Journal
of Electrical and Computer Engineering, Vol. 29 Nos 1-2, pp. 149-155.
Gao, B., Maekawa, T., Amagata, D. and Hara, T. (2018), “Environment-adaptive malicious node
detection in MANETs with ensemble learning”,2018 IEEE 38th International Conference on
Distributed Computing Systems,July 2, pp. 556-566.
Goudarzi, P. and Hosseinpour, M. (2010), “Video transmission over MANETs with enhanced
quality of experience”,IEEE Transactions on Consumer Electronics, Vol. 56 No. 4, pp. 2217-2225.
Hanzo, L. and Tafazolli, R. (2007), “A survey of QoS routing solutions for mobile ad hoc networks”,
IEEE Communications Surveys & Tutorials, Vol. 9 No. 2, pp. 50-70.
Hanzo, L. II and Tafazolli, R. (2011), “QoS-aware routing and admission control in shadow-fading
environments for multirate MANETs”,IEEE Transactions on Mobile Computing, Vol. 10 No. 5,
pp. 622-637.
Hejmo, M., Mark, B.L., Zouridaki, C. and Thomas, R.K. (2006), “Design and analysis of a denial-of-
service-resistant quality-of-service signaling protocol for MANETs”,IEEE Transactions on
Vehicular Technology, Vol. 55 No. 3, pp. 743-751.
Hu, C.C., Wu, H. and Chen, G.H. (2008), “Bandwidth-satisfied multicast trees in MANETs”,IEEE
Transactions on Mobile Computing, Vol. 7 No. 6, pp. 712-723.
Hurley-Smith, D., Wetherall, J. and Adekunle, A. (2017), “SUPERMAN: security using pre-existing
routing for mobile ad hoc networks”,IEEE Transactions on Mobile Computing, Vol. 16 No. 10,
pp. 2927-2940.
Jiang, S., Liu, Y., Jiang, Y. and Yin, Q. (2004), “Provisioning of adaptability to variable topologies for
routing schemes in MANETs”,IEEE Journal on Selected Areas in Communications, Vol. 22 No. 7,
pp. 1347-1356.
Jin, C., Kang, R. and Li, R. (2018), “VTB-RTRRP: variable threshold based response time reliability real-
time prediction”,IEEE Access, Vol. 6 No. 1, pp. 60-71.
Jung, J.Y., Choi, H.H. and Lee, J.R. (2018), “Survey of bio-inspired resource allocation algorithms and
MAC protocol design based on a bio-inspired algorithm for mobile ad hoc networks”,IEEE
Communications Magazine, Vol. 56 No. 1, pp. 119-127.
Khan, M.S., Midi, D., Khan, M.I. and Bertino, E. (2017), “Fine-grained analysis of packet loss in
MANETs”,IEEE Access, Vol. 5 No. 1, pp. 7798-7807.
Kumar, M., Bhandari, R., Rupani, A. and Ansari, J.H. (2018), “Trust-based performance
evaluation of routing protocol design with security and QoS over MANET”,2018 IEEE
International Conference on Advances in Computing and Communication Engineering,
pp. 139-142.
Li, X., Liu, T., Liu, Y. and Tang, Y. (2014), “Optimized multicast routing algorithm based on a tree
structure in MANETs”,China Communications, Vol. 11 No. 2, pp. 90-99.
Li, Z. and Shen, H. (2014), “A QoS-oriented distributed routing protocol for hybrid wireless networks”,
IEEE Transactions on Mobile Computing, Vol. 13 No. 3, pp. 693-708.
Liu, J., Yu, F.R., Lung, C.H. and Tang, H. (2009), “Optimal combined intrusion detection and biometric-
based continuous authentication in high-security mobile ad hoc networks”,IEEE Transactions
on Wireless Communications, Vol. 8 No. 2, pp. 806-815.
174
IJIUS
7,4
Meng, L. and Song, W. (2013), “Routing protocol based on Grover’s searching algorithm for mobile ad-
hoc networks”,China Communications, Vol. 10 No. 3, pp. 145-156.
Mohammed, N., Otrok, H., Wang, L., Debbabi, M. and Bhattacharya, P. (2011), “Mechanism design-
based secure leader election model for intrusion detection in MANET”,IEEE Transactions on
Dependable and Secure Computing, Vol. 8 No. 1, pp. 89-103.
Movahedi, Z., Hosseini, Z., Bayan, F. and Pujolle, G. (2016), “Trust-distortion resistant trust
management frameworks on mobile ad hoc networks: a survey”,IEEE Communications Surveys
& Tutorials, Vol. 18 No. 2, pp. 1287-1309.
Nadeem, A. and Howarth, M.P. (2013), “A survey of MANET intrusion detection & prevention
approaches for network layer attacks”,IEEE Communications Surveys & Tutorials, Vol. 15
No. 4, pp. 2027-2045.
Nguyen, D.Q., Toulgoat, M. and Lamont, L. (2016), “Impact of the trust-based security association and
mobility on the delay metric in MANET”,Journal of Communications and Networks, Vol. 18
No. 1, pp. 105-111.
Rezaiesarlak, R. and Manteghi, M. (2015), “Accurate extraction of early-/late-time responses using
short-time matrix pencil method for transient analysis of scatterers”,IEEE Transactions on
Antennas and Propagation, Vol. 63 No. 11, pp. 4995-5002.
Sangeetha, V. and Kumar, S.S. (2018), “Detection of the malicious node in a mobile ad-hoc network”,2018
IEEE International Conference on Power, Signals, Control and Computation,January 6,pp.1-3.
Sheikh, N.A., Malik, A.A., Mahboob, A. and Nisa, K. (2014), “Implementing voice over internet protocol
in mobile ad hoc network –analyzing its features regarding efficiency, reliability and security”,
The Journal of Engineering, Vol. 2014 No. 5, pp. 184-192.
Surendran, S. and Prakash, S. (2015), “An ACO look-ahead approach to QOS enabled fault-tolerant
routing in MANETs”,China Communications, Vol. 12 No. 8, pp. 93-110.
Tsuda, T., Komai, Y., Hara, T. and Nishio, S. (2016), “Top-k query processing and malicious node
identification based on node grouping in MANETs”,IEEE Access, Vol. 4 No. 1, pp. 993-1007.
Veeraiah, N. and Krishna, B.T. (2018), “Selfish node detection IDSM based approach using individual master
cluster node”,2018 IEEE 2nd International Conference on Inventive Systems and Control (ICISC),
January 19, pp. 427-431.
Yang, H., Luo, H., Ye, F., Lu, S.W. and Zhang, L. (2004), “Security in mobile ad hoc networks: challenges
and solutions”,Wireless Communications, IEEE, Vol. 11, pp. 38-47, doi: 10.1109/MWC.2004.1269716.
Zhang, Y. and Lee, W. (2005), “Security in mobile ad-hoc networks”, in Mohapatra, P. and
Krishnamurthy, S.V. (Eds), Ad Hoc Networks, Springer, Boston, MA, pp. 249-268.
About the authors
Rajendran N. received the BE Degree in Computer Science and Engineering from Bharathiar
University, Coimbatore, India, in 2002, the ME Degree in Software Engineering from Anna University,
Chennai, India, in 2007. He is currently pursuing his PhD Degree in Information Technology from B.S.
Abdur Rahman Crescent Institute of Science and Technology. He has 13 years of teaching experience
and he is currently working as Assistant Professor (Senior Grade) in the Department of Information
Technology at B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil
Nadu, India. His current research interests include mobile ad hoc networks, cloud computing and
mobile application development. Rajendran N. is the corresponding author and can be contacted at:
rajendran.n81@gmail.com
Jawahar P.K. received the BE Degree in Electronics and Communication Engineering from the
Coimbatore Institute of Technology, Coimbatore, India, in 1989, the MTech Degree in Electronics and
Communication Engineering from the Pondicherry Engineering College, Puducherry, India, in 1998,
and the PhD Degree in Information and Communication Engineering from Anna University, Chennai,
India, in 2010. He has 28 years of teaching experiences and he is currently Professor in the Department
of Electronics and Instrumentation Engineering, B.S. Abdur Rahman Crescent Institute of Science and
Technology, Chennai. His current research interests include wired and wireless networks, very large
scale integration, microprocessor and microstrip antenna.
175
Makespan of
routing and
security in
CCIDS
Priyadarshini R. received the BE Degree in Electrical and Electronics Engineering from
Madras University, Chennai, India, in 2003, the MTech Degree in Information Technology
from Anna University, Chennai, India, in 2006. She is currently pursuing her PhD Degree in
Information Technology from B.S. Abdur Rahman Crescent Institute of Science and
Technology, Chennai, India. She has 13 years of teaching experience and she is currently
working as Assistant Professor (Senior Grade) in the Department of Information Technology at
B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India. Her current
research interests include cloud computing, semantics in big data, mobile ad hoc networks and
mobile application development.
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
176
IJIUS
7,4
