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Citation: Aljebreen, M.; Alohali,
M.A.; Saeed, M.K.; Mohsen, H.; Al
Duhayyim, M.; Abdelmageed, A.A.;
Drar, S.; Abdelbagi, S. Binary Chimp
Optimization Algorithm with ML
Based Intrusion Detection for Secure
IoT-Assisted Wireless Sensor
Networks. Sensors 2023,23, 4073.
https://doi.org/10.3390/s23084073
Academic Editor: Achyut Shankar
Received: 4 February 2023
Revised: 24 February 2023
Accepted: 1 March 2023
Published: 18 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Binary Chimp Optimization Algorithm with ML Based Intrusion
Detection for Secure IoT-Assisted Wireless Sensor Networks
Mohammed Aljebreen 1, Manal Abdullah Alohali 2, Muhammad Kashif Saeed 3, Heba Mohsen 4,
Mesfer Al Duhayyim 5, *, Amgad Atta Abdelmageed 6, Suhanda Drar 6and Sitelbanat Abdelbagi 6
1Department of Computer Science, Community College, King Saud University, P.O. Box 28095,
Riyadh 11437, Saudi Arabia
2Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint
Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
3Department of Computer Science, Applied College, King Khalid University, Muhayil 63311, Saudi Arabia
4Department of Computer Science, Faculty of Computers and Information Technology,
Future University in Egypt, New Cairo 11835, Egypt
5Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin
Abdulaziz University, Al-Kharj 16273, Saudi Arabia
6Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin
Abdulaziz University, Al-Kharj 16278, Saudi Arabia
*Correspondence: m.alduhayyim@psau.edu.sa
Abstract:
An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where
WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims
to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and
improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated
for protecting WSN linked to the IoT. This article presents a Binary Chimp Optimization Algorithm
with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The
presented BCOA-MLID technique intends to effectively discriminate different types of attacks to
secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried
out. The BCOA is designed for the optimal selection of features to improve intrusion detection
efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost
regulation extreme learning machine classification model with a sine cosine algorithm as a parameter
optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle
intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique
with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a
reduced accuracy of 96.83% and 97.20%, respectively.
Keywords:
intrusion detection system; wireless sensor networks; machine learning; chimp optimiza-
tion algorithm; feature selection
1. Introduction
The Internet of Things (IoT) is commonly known as a network that is made up of
many devices that are connected through the internet [
1
]. Wireless Sensor Networks (WSN)
have a crucial role in the IoT, which is helpful to produce seamless data that influence the
lifetime of a network. Despite the significant applications of the IoT [
2
], various challenges,
such as storage, security, load balancing, and energy exist. In addition, it is an open
network with random and dynamic topology [
3
]. Thus, it is essential to execute a sequence
of targeted studies to guarantee reliability, real-time response, energy-saving, and other
operational needs of WSNs. As a data-centric network, a lot of delicate information is
transmitted, collected, processed, and stored in WSN [
4
,
5
]. Its security problem has become
very serious. Owing to the characteristics and limitations of WSN itself, the data can be
easily tampered with, ruined, or stolen. How to protect network security effectually in the
Sensors 2023,23, 4073. https://doi.org/10.3390/s23084073 https://www.mdpi.com/journal/sensors
Sensors 2023,23, 4073 2 of 17
face of numerous network attacks becomes a significant research topic [
6
]. Passive defense
via firewalls, access control, and other means is inadequate to thwart every network attack.
Intrusion detection (ID) is a proactive security protection technology that is used to observe
the functioning condition of the network and find intrusions such as maloperations and
internal or external attacks, in such a way that the network can interrupt them and respond
as needed [7].
To protect IoT systems from cyber threats, an Intrusion Detection System (IDSs) is
another line of defense that must be advanced in IoT networks [
8
,
9
]. Many surveys
have been performed to describe machine learning (ML)-related IDSs for protection from
compromised IoT devices or IoT networks. The surveys have covered studies on IDSs for
cloud-related IoT systems, WSNs, mobile ad hoc networks (MANETs), and cyber–physical
systems (CPS) [
10
]. However, conventional IDS techniques are insufficient or less effective
for the security of IoT systems because of their peculiar features, for example„ limited
bandwidth capacity [11], limited energy, heterogeneity, global connectivity, and ubiquity.
Deep Learning (DL) and Machine Learning (ML) related methods have obtained
credibility through a successful implementation in the detection of network attacks, which
includes IoT networks. Since WSN includes low computing and communication abilities,
conventional network intrusion detection models are not directly used in WSN. Presently,
several researchers on WSN intrusion detection can exploit ML models for investigating
traffic data. Because of the expansion in both the network’s size and its user base, the WSN
network produces high-dimensional traffic data, and the classical ML models encounter
problems such as poor feature extraction and detection accuracy, which cannot meet the
requirements of such an application environment [
12
]. Compared to ML models for IDS,
the DL models can decrease the computation burden and increase the ability to learn the
characteristics of data traffic, which can improve the precision of the detection model [
13
].
This article presents a Binary Chimp Optimization Algorithm with Machine Learning
based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. In the presented
BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed
for the optimal selection of features to improve intrusion detection efficacy. To detect
intrusions in the IoT-WSN, the BCOA-MLID technique employs a Class-specific Cost
Regulation Extreme Learning Machine (CCR-ELM) classification model with a Sine Cosine
Algorithm (SCA) as a parameter optimization approach. The design of BCOA feature
selection with an SCA-optimized CCR-CLM classifier for intrusion detection shows the
novelty of the work. The experimental result of the BCOA-MLID technique was tested on
the Kaggle intrusion dataset.
2. Related Works
Kagade and Jayagopalan [
14
] developed a new intrusion detection system (IDS) that
was set up with a DL method. First of all, the optimum cluster head (CH) was chosen from
among the SNs, from which SNs with higher energy will be listed to act as CH. In this study,
the CH selection was optimally assessed concerning energy variables under limitations
such as distance and delay. For the best selection, a new technique called the Self-Improved
Sea Lion Optimization (SI-SLnO) method was presented in this study. Krishnan et al. [15]
aimed to frame an intrusion prevention protocol and anomalous ID protocol for interruption
evasion in the IoT, based on WSN for expanding the information reliability and network
time frame. This structure made dissimilar energy-efficient groups reliant on the natural
features of nodes. In [
16
], a smart IDS suitable to finding IoT-related attacks was applied.
Specifically, to identify malicious IoT network traffic, a DL technique was utilized. The
identity solution has supported the IoT connectivity protocols to interoperate, and it assures
the security of operation. An IDS is one common type of network security technology that
can be employed to secure the network. Zhiqiang et al. [
17
] devised an enriched empirical-
related component analysis for choosing applicable features. The feature-selecting method
compiles the benefits of both PCA and empirical mode decomposition to retain many
Sensors 2023,23, 4073 3 of 17
appropriate attributes. The classifications of the attack nodes with selective attributes have
been executed with LSTM.
Muruganandam et al. [
18
] developed a DL-related feed-forward ANN method that
enables accurate predictions of k-barrier count for potential ID and mitigation. The area
of RoI, sensing transmission area, sensor sensing area, and various sensors were the four
potential features that can be utilized to assess and learn the feed-forward ANN method.
Subramani and Selvi [
19
] modeled an intelligent IDS to detect intruders in IoT-related
WSNs so that it can manage such intrusions. To develop this intelligent IDS, a rule- and
multi-objective PSO-based feature selection technique was devised by the author, who even
suggested an intellectual rule-based enhanced multiclass SVM classifier method to detect
the intruders with a higher level of accuracy. Saba et al. [
20
] presented a CNN-related
algorithm for anomaly-based IDS that uses IoT power, offering the ability to potentially
inspect all of the traffic across the IoT. This presented algorithm displays the capability to
find any abnormal traffic behavior and possible intrusion.
Sadeghi et al. [
21
] presented a hybrid method of a new DCNN and multi-objective
binary chimp optimization algorithm (MOBChOA) for selecting the feature optimally.
Then, for optimal selection of features, a method called MOBChOA is applied. Finally,
for classifying the pixels into particular specific land-cover labels, the author trained the
fully connected DCNN. In [
22
], the author presented a method to optimize the network
parameters, which combined both GRU and CNN, and distinct CNN–GRU combination
sequences were introduced. In [
23
], the author scrutinized the effect of data imbalance on
formulating a potential SCADA-based IDS. CNNs were combined with Long Short-Term
Memory (CNN-LSTM) for binary classification.
Abosata et al. [
24
] modeled a Federated-Transfer-Learning-Based Customized Dis-
tributed IDS (FT-CID) approach to identify RPL intrusion in a heterogeneous IoT. Primarily,
to construct a local model, the central server initialized the FT-CID with a predefined
learning approach and observed the unique attributes of various RPL-IoTs. Then, using the
local parameters, the edge IDSs were trained and, through federation, the globally shared
parameters generated by the central server were altered and aggregated into diverse local
parameters of different edges. In [
25
], two different approaches were devised. In the first
method, a custom CNN was framed and united with LSTM deep network layers. The
second model was constructed around each fully connected layer (dense layers) to build an
Artificial Neural Network (ANNs).
3. The Proposed Intrusion Detection Model
In this article, an automated BCOA-MLID technique has been developed for accurate
intrusion detection to accomplish security tasks in the IoT-WSN. The presented BCOA-
MLID technique intends to effectively discriminate different types of attacks to secure
the IoT-WSN. In the presented BCOA-MLID technique, a four-stage process is involved,
namely, data normalization, FS using BCOA, CCR-ELM classification, and SCA-based
parameter optimization. Figure 1represents the overall flow of the BCOA-MLID approach.
3.1. Data Normalization
In the presented BCOA-MLID technique, data normalization is performed at the initial
stage. The data-normalized operation scales the data so that the weighted sum exists in
the range of the activation functions [
26
]. The un-normalized data generates an ill-trained
network and delays the convergence. At the same time, normalizing the data accelerate the
convergence and attain non-dimensionality. For scaling the data in the range of zero and
one, it utilizes the min–max normalized system that is determined as:
Xnorm =x−xmin
xmax −xmin
(1)
where X
norm
represents normalization data, xsignifies the primary value from the database,
xmax denotes the maximal value, and xmin stands for the minimal value.
Sensors 2023,23, 4073 4 of 17
Sensors 2023, 23, x FOR PEER REVIEW 4 of 17
Figure 1. The overall flow of the BCOA-MLID approach.
3.1. Data Normalization
In the presented BCOA-MLID technique, data normalization is performed at the ini-
tial stage. The data-normalized operation scales the data so that the weighted sum exists
in the range of the activation functions [26]. The un-normalized data generates an ill-
trained network and delays the convergence. At the same time, normalizing the data ac-
celerate the convergence and attain non-dimensionality. For scaling the data in the range
of zero and one, it utilizes the min–max normalized system that is determined as:
Binary Chimp Optimization Algorithm
Figure 1. The overall flow of the BCOA-MLID approach.
3.2. Feature Selection Using BCOA
At this stage, the BCOA is designed for the optimal selection of features to improve
intrusion detection efficacy. Khishe and Mosavi (2020) introduced a BCOA that was
stimulated by the ability of chimpanzees to think individually during group hunting and
sexual motivation [
21
]. The BCOA can recognize optimal solutions by the exploration of
the entire search space and avoids the local optima. It is simple to design and does not
Sensors 2023,23, 4073 5 of 17
require a large number of computational resources. BCOA has a fast convergence rate,
which means it can quickly converge to the optimal solution. This makes it suitable for
applications where time is a critical factor. In summary, BCOA is a simple and robust
optimization algorithm that is capable of finding the global optimal solution in complex
and noisy search spaces.
Meanwhile, attacking, driving, blocking, and chasing are the four major stages of
BCOA. The BCOA can be initialized by randomly producing several chimps. The attacker
chimp prognosticates the breakout path of prey by forcing it back toward the chaser. The
driver chimp follows the prey without trying to capture it. The barrier chimp places
themselves in trees to generate a barrier during prey development.
The chaser chimp moves faster to catch the prey. Chasing and driving the prey are
expressed as follows:
d=c·Xprey (r)−m·Xchimp (2)
Xchim p(r+1)=Xprey (r)−a·d(3)
a=2·f·r1−f(4)
c=2·r2(5)
m=Chaolic_value (6)
Xprey
denotes the prey location vector;
a
,
ct
, and
m
show the coefficient vectors;
Xchim p
symbolizes the chimp location vector;
r
represents the existing iteration;
r1
and
r2
indicate
the random vector
∈[0, 1]
;
f
denotes the dynamic vector
∈[0, 2.5]
, and
m
represents a
chaotic vector. First, the chimpanzees search for the prey location during the hunting stage
based on the four hunting strategies. Then, the prey position can be evaluated using those
hunting strategies, and other chimpanzees update the position of the prey. These steps are
expressed as follows:
dAttacher =|c1·XAttacher −m1·X|
dBarrier =|c2·XBarrier −m2·X|
dChaser =|c3·XChaser −m3·X|
dDriver =|c4·XDriver −m4·X|
(7)
X1=XAttacher −a1(dAttacher)
X2=XBarrier −a2(dBarrier )
X3=XChaser −a3(dChaser )
X4=XDriver −a4(dDriver)
(8)
X(t+1)=X1+X2+X3+X4
4(9)
Let
XAttacher
be the better searching agent,
XBarrier
represents the second better search-
ing agent,
XChaser
represents the third better searching agent,
XDiver
indicates the fourth
better searching agent, and X(t+1)denotes the updated location of every chimp.
Lastly, each chimpanzee attacks the prey. After hunting the prey, they attain sexual
motivation, regardless of their duties. Sexual motivation can be represented as follows:
xchim p(t+1)=Xprey(l)−a·d i f µ<0.5
Choatic_val ue i f µ≥0.5 (10)
In Equation (10), µdenotes the randomly generated number ∈[0, 1]. In the extended
version of BCOA, chimpanzees continuously change their location at any point in the
search space. In discrete issues, the solution is constrained to binary values. The operator
of the binary metaheuristic method moves toward the nearer and farther corners of the
hypercube by constantly changing zero to one and one to zero. Thus, in the BBCOA model,
the position updating formula needs to be adjusted. For these purposes, a transfer function
maps the continuous space to the discrete space. The transfer function symbolizes changing
the probability of the location vector from zero to one. Therefore, the transfer function
Sensors 2023,23, 4073 6 of 17
forces the chimpanzees to move in the discrete space. Here, a newly generated technique
used to update the position of a chimpanzee is presented. In the presented technique, the
location-updating formula can be given as the following:
Xt+1
d=(1i f sig moid X1+X2+X3+X4
4≥R
0otherwise (11)
Sigmoid(x)=1
1+e−14(x−0.45)(12)
In the expression,
Xt+1
d
denotes the upgraded binary location at iteration,
r
,
R
rep-
resents the arbitrary value
∈[
0, 1. Sigmoid
(x)
shows an
S
-shaped function,
X12X22X32
,
and
X4
denotes the chimpanzee’s movement towards the four attacking strategies of
chimps, correspondingly.
In the presented method, two objective functions have been utilized for feature se-
lection: the minimum number of features and the maximum overall accuracy (OA). The
weighted sum has been used for integrating both main functions. Hence, the fitness
function is represented as follows:
Fitness Funclion(i)=α·0A(i)+(1−α)·log10N
n(i)(13)
In Equation (13), objective Function
(i)
represents the fitness function of
i
-
th
chimps,
0
A(i)
denotes the total accuracy of
i
-
th
chimps,
N=
101 features, and
n(i)
indicates the
number of features chosen at the
i
-
th
chimps. Moreover,
α
represents the weight parameter,
which can be assumed to be 0.92. The calibration of
α
has been set by using the trial-and-
error technique.
3.3. Intrusion Detection Using Optimal CCR-ELM Model
To detect intrusions in the IoT-WSN, the BCOA-MLID technique utilized the SCA with
the CCR-ELM classification model in the ELM model. The input bias and weight of SLFNs
can be randomly created [
27
]. An equal resultant matrix of hidden states was computed,
concerning the resultant, weighted with some steps. Therefore, the computation cost of
ELM was lower.
Assume that there are
N
various instances defined as
(Xi,yi)
,
i=
1, 2,
. . .
,
N
.
Xi= [xi1,xi2, . . . , xin]T
2
Rn
and
yi= [yi1,yi2, . . . , yi m]T
2
Rm
. Consider
aj
and
βj
to be
the input and resultant, weighted correspondingly.
bj
refers bias of hidden units. The SLFN
with Lhidden node can be modeled as:
L
∑
j=1
βjgaj,bj,Xi=0i,i=1, . . . , N(14)
where
g(•)
denotes the activation function and generally utilizes typical non-linear func-
tions, such as radial basis functions, sigmoid, sine, etc. The error amongst evaluated output
0iand the actual output yiis zero if the SLFNs exactly estimate the data feature.
L
∑
j=1
βjgaj,bj,Xi=yi,i=1, . . . , N(15)
Assume
β= [βT
1, . . . , βT
L]T
and
Y= [y1T, . . . , yNT]T
. The above method is repre-
sented as Hβ=Y.
H=
g(a1,b1,X1). . . g(aL,bL,X1)
.
.
. . . . .
.
.
g(a1,b1,XN). . . g(aL,bL,XN)
(16)
Sensors 2023,23, 4073 7 of 17
H
is the supposed resultant matrix of the hidden state.
hij
signifies the resultant of
jth
hidden node equivalent to input
Xi
. In the trained procedure, the parameters of hidden
nodes comprising
aj
and
bj
, could not be modified then primarily created. The equivalent
resultant weighted can be evaluated as:
ˆ
β=H†Y=((l
c+HTH)−1HTY,L<N
HT(l
c+HTH)−1Y,L≥N(17)
H†
represents the Moore–Penrose generalization inverse of H.
C
denotes the pre-
set parameter, intending to give a trade-off between minimizing the trained error and
maximizing the marginal distance.
I
denotes the unit matrix. A better resultant weighted
can be obtained with the minimized cost function kO−Yk.
After establishing class-specific regulation cost, CCR-ELM has been projected for
solving the class imbalance issues. Two trade-off factors, comprising
C+
for minority
positive instances and
C−
for most negative instances, can be utilized for rebalancing
both classes. Let the count of minority positive instances and most negative instances be
formulated as l1and l2, correspondingly. CCR-ELM was modeled as:
min (1
2kβk2+1
2C+l1
∑
i=1|yi=+1
ξ2
i+1
2C−l2
∑
i=1|yi=−1
ξ2
i)(18)
s·t·h(xi)β=yi−ξi,i=1, . . . N.
Equivalent resultant weighted ˆ
βis calculated as:
ˆ
β=H†Y=((l
C++l
C−+HTH)−1HTY,L<N
HT(l
C++l
C−+HTH)−1Y,L≥N(19)
To binary classifier issues, the decision function of the CCR-ELM-based classifier was
f(x)=sign h(x)β.
f(x)=(sign h(x)(I
C++I
C−+HTH)−1HTY,L<N
sign h(x)HT(I
C++I
C−+HTH)−1Y,L≥N(20)
In CCR-ELM, five key parameters contain direct features of the classifier accuracy,
comprising the count of hidden nodes
L
, input weighted
aj
, biases
bj
,
C+
for minority
positive instances, and
C−
for most negative instances. The former three parameters
determine the infrastructure of SLFNs and were generally pre-set by humans.
Finally, the SCA is applied to optimally choose the parameters related to the CCR-
ELM classifier. SCA is a simple and versatile optimization algorithm that is capable of
finding the global optimal solution in complex and noisy search spaces. Its robustness,
fast convergence rate, and scalability make it a suitable algorithm for a wide range of
optimization problems. The SCA creates several primary random solutions and appeals to
them to shift nearby optimum solutions utilizing a mathematical method dependent upon
sine and cosine functions [
28
]. For expressing the functions of SCA, a gathering of random
variables can be utilized. Figure 2illustrates the flowchart of SCA.
•The motion direction;
•The movement place;
•Emphasizing or de-emphasizing the target effect;
•Swapping amongst the sine and cosine elements.
Sensors 2023,23, 4073 8 of 17
Sensors 2023, 23, x FOR PEER REVIEW 8 of 17
Finally, the SCA is applied to optimally choose the parameters related to the CCR-
ELM classifier. SCA is a simple and versatile optimization algorithm that is capable of
finding the global optimal solution in complex and noisy search spaces. Its robustness,
fast convergence rate, and scalability make it a suitable algorithm for a wide range of op-
timization problems. The SCA creates several primary random solutions and appeals to
them to shift nearby optimum solutions utilizing a mathematical method dependent upon
sine and cosine functions [28]. For expressing the functions of SCA, a gathering of random
variables can be utilized. Figure 2 illustrates the flowchart of SCA.
• The motion direction;
• The movement place;
• Emphasizing or de-emphasizing the target effect;
• Swapping amongst the sine and cosine elements.
Figure 2. Flowchart of SCA.
The upgrade procedure of candidate solutions can be carried out utilizing the subse-
quent formula.
Figure 2. Flowchart of SCA.
The upgrade procedure of candidate solutions can be carried out utilizing the subse-
quent formula.
P(t+1)=(P(t)+r5·sin(r6)·|r7S∗(t)−S(t)|r4<0.5
P(t)+r5·cos(r6)·|r7S∗(t)−S(t)|r4≥0.5 (21)
where
t
refers to the count of searching iterations. Present and better solutions can be
indicated as
S
and
S∗
. The values of [0, 1] are assigned to random variables
r4
,
r6
, and
r7
. For instance, it is seen in the formula that the places of optimum solutions control the
present solution position, generating it more simply to obtaining an ideal solution. The
value of r4was altered as follows in the running iterations of SCA.
r4=a−a×t
tmax (22)
where
a
represents the constant, and
t
and
tmax
signify the present and maximal iterations,
correspondingly. The SCA technique is more resilient than a broad range of metaheuristic
Sensors 2023,23, 4073 9 of 17
techniques from the literature because it utilizes just one better solution to manage the other
solution. Fitness selection becomes a vital factor in the SCA method. Solution encrypting
was used to evaluate the accuracy of the candidate solution. Here, the accuracy value was
the main condition utilized to modchip a fitness function.
Fitness =max (P)(23)
P=TP
TP +FP (24)
From the expression, FP denotes thefalse positive value and TP indicates the
true positive.
4. Results and Discussion
In this section, the intrusion detection fallouts of the BCOA-MLID technique are
examined using the WSN-DS dataset [29], which holds 374661 samples with 5 class labels
as defined in Table 1. For experimental validation, we have used 80:20 and 70:30 of
training/testing data.
Table 1. Details of the dataset.
Class No. of Samples
Normal 340,066
Blackhole 10,049
Grayhole 14,596
Flooding 3312
Scheduling Attacks 6638
Total Number of Samples 374,661
The proposed model was simulated using Python 3.6.5 tool on a PC with i5-8600k
CPU, GeForce 1050Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter
settings are given as follows: learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count:
50, and activation: ReLU.
In Figure 3, the confusion matrices of the BCOA-MLID technique are examined under
distinct sizes of the Training Phase (TRP) and Testing Phase (TSP). The figures indicate that
the BCOA-MLID technique categorizes the attacks and normal samples proficiently.
In Table 2, the entire results of the BCOA-MLID technique received under 80:20 of
TRP/TSP are given. In Figure 4, the average intrusion detection results of the proposed
model are illustrated under 80:20 of TRP/TSP. The results show that the BCOA-MLID
technique reported improved results under every individual class. With 80% of TRP, the
BCOA-MLID technique reaches an average
accuy
of 99.63%,
sensy
of 97.91%,
specy
of
99.67%,
Fscore
of 94.52%, and
AUCscore
of 98.79%. Concurrently, with 20% of TSP, the BCOA-
MLID approach reaches an average
accuy
of 99.63%,
sensy
of 97.86%,
specy
of 99.66%,
Fscore
of 94.28%, and AUCscore of 98.76%.
Table 2. Classifier outcome of the BCOA-MLID approach on TRP/TSP of 80:20.
Class Labels AccuySensySpecyFscore AUCscore
Training Phase (80%)
Normal 99.19 99.19 99.21 99.55 99.20
Blackhole 99.75 98.44 99.79 95.46 99.11
Grayhole 99.71 98.45 99.76 96.38 99.11
Flooding 99.77 95.64 99.80 87.84 97.72
Scheduling Attacks 99.75 97.84 99.79 93.39 98.81
Average 99.63 97.91 99.67 94.52 98.79
Sensors 2023,23, 4073 10 of 17
Table 2. Cont.
Class Labels AccuySensySpecyFscore AUCscore
Testing Phase (20%)
Normal 99.17 99.17 99.16 99.54 99.16
Blackhole 99.77 98.72 99.80 95.87 99.26
Grayhole 99.73 98.45 99.78 96.53 99.11
Flooding 99.73 94.79 99.78 86.43 97.28
Scheduling Attacks 99.75 98.18 99.78 93.03 98.98
Average 99.63 97.86 99.66 94.28 98.76
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Figure 3. Confusion matrices of the BCOA-MLID approach (a,b) TRP/TSP of 80:20 and (c,d)
TRP/TSP of 70:30.
In Table 2, the entire results of the BCOA-MLID technique received under 80:20 of
TRP/TSP are given. In Figure 4, the average intrusion detection results of the proposed
model are illustrated under 80:20 of TRP/TSP. The results show that the BCOA-MLID
technique reported improved results under every individual class. With 80% of TRP, the
BCOA-MLID technique reaches an average of 99.63%, of 97.91%, of
99.67%, of 94.52%, and of 98.79%. Concurrently, with 20% of TSP, the
BCOA-MLID approach reaches an average of 99.63%, of 97.86%, of
99.66%, of 94.28%, and of 98.76%.
Figure 3.
Confusion matrices of the BCOA-MLID approach (
a
,
b
) TRP/TSP of 80:20 and (
c
,
d
) TRP/TSP
of 70:30.
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Sensors 2023, 23, x FOR PEER REVIEW 11 of 17
Table 2. Classifier outcome of the BCOA-MLID approach on TRP/TSP of 80:20.
Class Labels
Training Phase (80%)
Normal
99.19
99.19
99.21
99.55
99.20
Blackhole
99.75
98.44
99.79
95.46
99.11
Grayhole
99.71
98.45
99.76
96.38
99.11
Flooding
99.77
95.64
99.80
87.84
97.72
Scheduling Attacks
99.75
97.84
99.79
93.39
98.81
Average
99.63
97.91
99.67
94.52
98.79
Testing Phase (20%)
Normal
99.17
99.17
99.16
99.54
99.16
Blackhole
99.77
98.72
99.80
95.87
99.26
Grayhole
99.73
98.45
99.78
96.53
99.11
Flooding
99.73
94.79
99.78
86.43
97.28
Scheduling Attacks
99.75
98.18
99.78
93.03
98.98
Average
99.63
97.86
99.66
94.28
98.76
Figure 4. The average outcome of the BCOA-MLID approach on TRP/TSP of 80:20.
Table 3 shows the overall results of the BCOA-MLID technique obtained under 70:30
of TRP/TSP.
Figure 5 demonstrates the average classification outcomes of the BCOA-MLID tech-
nique are given under 70:30 of TRP/TSP. The results show that the BCOA-MLID algorithm
reported improved results under every individual class. With 70% of TRP, the BCOA-
MLID technique reaches an average of 99.19%, of 89.96%, of 98.86%,
of 86.23%, and of 94.41%. Concurrently, with 30% of TSP, the BCOA-
MLID approach reaches an average of 99.18%, of 89.41%, of 98.81%,
of 85.70%, and of 94.11%.
Figure 4. The average outcome of the BCOA-MLID approach on TRP/TSP of 80:20.
Table 3shows the overall results of the BCOA-MLID technique obtained under 70:30
of TRP/TSP.
Figure 5demonstrates the average classification outcomes of the BCOA-MLID tech-
nique are given under 70:30 of TRP/TSP. The results show that the BCOA-MLID algorithm
reported improved results under every individual class. With 70% of TRP, the BCOA-MLID
technique reaches an average
accuy
of 99.19%,
sensy
of 89.96%,
specy
of 98.86%,
Fscore
of
86.23%, and
AUCscore
of 94.41%. Concurrently, with 30% of TSP, the BCOA-MLID approach
reaches an average
accuy
of 99.18%,
sensy
of 89.41%,
specy
of 98.81%,
Fscore
of 85.70%, and
AUCscore of 94.11%.
Table 3. Classifier outcome of the BCOA-MLID approach on TRP/TSP of 70:30.
Class Labels AccuySensySpecyFscore AUCscore
Training Phase (70%)
Normal 98.63 98.90 95.99 99.25 97.45
Blackhole 99.39 94.15 99.53 89.19 96.84
Grayhole 99.28 87.97 99.74 90.46 93.85
Flooding 99.45 82.38 99.60 72.83 90.99
Scheduling Attacks 99.21 86.39 99.44 79.43 92.92
Average 99.19 89.96 98.86 86.23 94.41
Testing Phase (30%)
Normal 98.63 98.92 95.76 99.24 97.34
Blackhole 99.39 94.27 99.53 89.29 96.90
Grayhole 99.25 87.55 99.74 90.27 93.64
Flooding 99.43 81.72 99.58 70.90 90.65
Scheduling Attacks 99.19 84.57 99.45 78.82 92.01
Average 99.18 89.41 98.81 85.70 94.11
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Sensors 2023, 23, x FOR PEER REVIEW 12 of 17
Table 3. Classifier outcome of the BCOA-MLID approach on TRP/TSP of 70:30.
Class Labels
𝑨𝒄𝒄𝒖𝒚
𝑺𝒆𝒏𝒔𝒚
𝑺𝒑𝒆𝒄𝒚
𝑭𝒔𝒄𝒐𝒓𝒆
𝑨𝑼𝑪𝒔𝒄𝒐𝒓𝒆
Training Phase (70%)
Normal
98.63
98.90
95.99
99.25
97.45
Blackhole
99.39
94.15
99.53
89.19
96.84
Grayhole
99.28
87.97
99.74
90.46
93.85
Flooding
99.45
82.38
99.60
72.83
90.99
Scheduling Attacks
99.21
86.39
99.44
79.43
92.92
Average
99.19
89.96
98.86
86.23
94.41
Testing Phase (30%)
Normal
98.63
98.92
95.76
99.24
97.34
Blackhole
99.39
94.27
99.53
89.29
96.90
Grayhole
99.25
87.55
99.74
90.27
93.64
Flooding
99.43
81.72
99.58
70.90
90.65
Scheduling Attacks
99.19
84.57
99.45
78.82
92.01
Average
99.18
89.41
98.81
85.70
94.11
Figure 5. The average outcome of the BCOA-MLID approach on TRP/TSP of 70:30.
The TACY and VACY of the BCOA-MLID model were used to investigate the IoT-
WSN detection performance in Figure 6. The figure shows that the BCOA-MLID model
has shown improved performance with increased values of TACY and VACY. To be spe-
cific, the BCOA-MLID method has attained maximum TACY valued outcomes.
Figure 5. The average outcome of the BCOA-MLID approach on TRP/TSP of 70:30.
The TACY and VACY of the BCOA-MLID model were used to investigate the IoT-WSN
detection performance in Figure 6. The figure shows that the BCOA-MLID model has
shown improved performance with increased values of TACY and VACY. To be specific,
the BCOA-MLID method has attained maximum TACY valued outcomes.
Sensors 2023, 23, x FOR PEER REVIEW 13 of 17
Figure 6. TACY and VACY outcome of the BCOA-MLID approach.
The TLOS and VLOS of the BCOA-MLID approach were tested on IoT-WSN detec-
tion performance in Figure 7. The figure shows that the BCOA-MLID approach has supe-
rior performance with menial values of TLOS and VLOS. The BCOA-MLID model has
resulted in reduced VLOS-valued outcomes.
A brief, clear precision–recall analysis of the BCOA-MLID system under the test da-
tabase is shown in Figure 8. The figure shows the BCOA-MLID approach has enhanced
values of precision–recall values for each class label.
In Table 4, the classification results of the BCOA-MLID technique compared with
recent methods are examined briefly [30,31]. The results indicate that the AdaBoost, GB,
and KNN-PSO algorithms result in the worst performance compared other models. Next,
the XGBoost model manages to demonstrate moderately improved results. Meanwhile,
the KNN model results in somewhat considerable performance, with an of 97.2%,
of 96.49%, of 96.34%, and of 90.23%. In contrast, the BCOA-MLID
technique attains a maximum performance of 99.63%, of 97.91%, of
99.67%, and of 94.52%.
Figure 6. TACY and VACY outcome of the BCOA-MLID approach.
Sensors 2023,23, 4073 13 of 17
The TLOS and VLOS of the BCOA-MLID approach were tested on IoT-WSN detection
performance in Figure 7. The figure shows that the BCOA-MLID approach has superior
performance with menial values of TLOS and VLOS. The BCOA-MLID model has resulted
in reduced VLOS-valued outcomes.
Sensors 2023, 23, x FOR PEER REVIEW 14 of 17
Figure 7. TLOS and VLOS outcome of the BCOA-MLID approach.
Figure 8. The precision-recall outcome of the BCOA-MLID approach.
Figure 7. TLOS and VLOS outcome of the BCOA-MLID approach.
A brief, clear precision–recall analysis of the BCOA-MLID system under the test
database is shown in Figure 8. The figure shows the BCOA-MLID approach has enhanced
values of precision–recall values for each class label.
In Table 4, the classification results of the BCOA-MLID technique compared with
recent methods are examined briefly [
30
,
31
]. The results indicate that the AdaBoost, GB,
and KNN-PSO algorithms result in the worst performance compared other models. Next,
the XGBoost model manages to demonstrate moderately improved results. Meanwhile, the
KNN model results in somewhat considerable performance, with an
accuy
of 97.2%,
sensy
of 96.49%,
specy
of 96.34%, and
Fscore
of 90.23%. In contrast, the BCOA-MLID technique
attains a maximum performance
accuy
of 99.63%,
sensy
of 97.91%,
specy
of 99.67%, and
Fscore of 94.52%.
Table 4. Comparative outcome of the BCOA-MLID approach with recent systems [30,31].
Methods AccuySensySpecyFscore
BCOA-MLID 99.63 97.91 99.67 94.52
AdaBoost 95.69 95.77 95.00 90.31
GB Model 94.58 95.25 94.09 93.31
XGBoost 96.83 96.10 94.43 91.52
KNN-AOA 97.20 96.49 96.34 90.23
KNN-PSO 92.89 95.63 95.08 92.99
In Table 5and Figure 9, the computation time (CT) outcomes of the BCOA-MLID
technique compared with existing techniques are investigated. The experimental outcomes
demonstrate that the AdaBoost, KNN, and KNN-PSO algorithms led to ineffectual results,
Sensors 2023,23, 4073 14 of 17
with higher CT values over other models. Moreover, the XGBoost model tried to exhibit
somewhat reduced CT values. In addition, the BG model results in somewhat considerable
performance, with a CT of 12.75 s. In contrast, the BCOA-MLID technique attains better
results, with a lower CT of 7.26 s. These results ensure the improved detection performance
the of BCOA-MLID technique in the IoT-WSN environment. The enhanced performance of
the proposed model is due to the inclusion of BCOA for feature subset selection and SCA
based parameter tuning.
Sensors 2023, 23, x FOR PEER REVIEW 14 of 17
Figure 7. TLOS and VLOS outcome of the BCOA-MLID approach.
Figure 8. The precision-recall outcome of the BCOA-MLID approach.
Figure 8. The precision-recall outcome of the BCOA-MLID approach.
Sensors 2023, 23, x FOR PEER REVIEW 15 of 17
Table 4. Comparative outcome of the BCOA-MLID approach with recent systems [30,31].
Methods
𝑨𝒄𝒄𝒖𝒚
𝑺𝒆𝒏𝒔𝒚
𝑺𝒑𝒆𝒄𝒚
𝑭𝒔𝒄𝒐𝒓𝒆
BCOA-MLID
99.63
97.91
99.67
94.52
AdaBoost
95.69
95.77
95.00
90.31
GB Model
94.58
95.25
94.09
93.31
XGBoost
96.83
96.10
94.43
91.52
KNN-AOA
97.20
96.49
96.34
90.23
KNN-PSO
92.89
95.63
95.08
92.99
In Table 5 and Figure 9, the computation time (CT) outcomes of the BCOA-MLID
technique compared with existing techniques are investigated. The experimental out-
comes demonstrate that the AdaBoost, KNN, and KNN-PSO algorithms led to ineffectual
results, with higher CT values over other models. Moreover, the XGBoost model tried to
exhibit somewhat reduced CT values. In addition, the BG model results in somewhat con-
siderable performance, with a CT of 12.75 s. In contrast, the BCOA-MLID technique at-
tains better results, with a lower CT of 7.26 s. These results ensure the improved detection
performance the of BCOA-MLID technique in the IoT-WSN environment. The enhanced
performance of the proposed model is due to the inclusion of BCOA for feature subset
selection and SCA based parameter tuning.
Table 5. CT outcome of the BCOA-MLID approach with recent systems.
Methods
Computational Time (s)
BCOA-MLID
7.26
AdaBoost
15.65
GB Model
12.75
XGBoost
13.67
KNN
15.01
KNN-PSO
14.87
Figure 9. CT outcome of the BCOA-MLID approach with recent systems.
Figure 9. CT outcome of the BCOA-MLID approach with recent systems.
Sensors 2023,23, 4073 15 of 17
Table 5. CT outcome of the BCOA-MLID approach with recent systems.
Methods Computational Time (s)
BCOA-MLID 7.26
AdaBoost 15.65
GB Model 12.75
XGBoost 13.67
KNN 15.01
KNN-PSO 14.87
5. Conclusions
In this article, an automated BCOA-MLID technique has been developed for accurate
intrusion detection to accomplish security tasks in the IoT-WSN. The presented BCOA-
MLID technique identifies intrusions using a series of processes: data normalization,
BCOA-based feature subset selection, CCR-ELM classification, and SCA-based parameter
tuning. The experimental result of the BCOA-MLID technique was tested on the Kaggle
intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID
technique with a maximum accuracy of 99.63%. In the future, the performance of the
proposed technique can be improved by the use of an unsupervised or semi-supervised
WSN intrusion detection model. These models will not only target a particular type of DoS
attack, but also strive to cover Sybil attacks, routing attacks, and other possible attacks.
Author Contributions:
Conceptualization, M.A.; Methodology, M.A.A.; Software, A.A.A. and S.D.;
Validation, M.A. and M.K.S.; Formal analysis, H.M. and A.A.A.; Investigation, M.A. and S.D.;
Resources, M.A.D.; Data curation, M.A.D. and A.A.A.; Writing—original draft, M.A., M.A.A., M.K.S.,
H.M., M.A.D. and S.A.; Writing—review & editing, M.K.S., H.M., A.A.A., S.D. and S.A.; Visualization,
S.D.; Supervision, M.A.A.; Project administration, M.A.D.; Funding acquisition, M.A.A, M.A. and
M.K.S. The manuscript was written through the contributions of all authors. All authors have read
and agreed to the published version of the manuscript.
Funding:
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid
University for funding this work through Large Groups Project under grant number (117/44). Princess
Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R330),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Research Supporting Project
number (RSP2023R459), King Saud University, Riyadh, Saudi Arabia. This study is supported via
funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
Institutional Review Board Statement:
This article does not contain any studies with human partic-
ipants performed by any of the authors.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data sharing does not apply to this article as no datasets were generated
during the current study.
Conflicts of Interest: The authors declare no conflict of interest.
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