Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 983~992
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp983-992  983
Journal homepage: http://ijece.iaescore.com
Experimental analysis of intrusion detection systems using
machine learning algorithms and artificial neural networks
Ademola Abdulkareem1
, Tobiloba Emmanuel Somefun1
, Adesina Lambe Mutalub2
,
Adewale Adeyinka1
1
Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria
2
Department of Electrical and Computer Engineering, Kwara State University, Kwara State, Nigeria
Article Info ABSTRACT
Article history:
Received Oct 3, 2022
Revised Mar 15, 2023
Accepted Apr 3, 2023
Since the invention of the internet for military and academic research
purposes, it has evolved to meet the demands of the increasing number of
users on the network, who have their scope beyond military and academics.
As the scope of the network expanded maintaining its security became a
matter of increasing importance. With various users and interconnections of
more diversified networks, the internet needs to be maintained as securely as
possible for the transmission of sensitive information to be one hundred per
cent safe; several anomalies may intrude on private networks. Several
research works have been released around network security and this research
seeks to add to the already existing body of knowledge by expounding on
these attacks, proffering efficient measures to detect network intrusions, and
introducing an ensemble classifier: a combination of 3 different machine
learning algorithms. An ensemble classifier is used for detecting remote to
local (R2L) attacks, which showed the lowest level of accuracy when the
network dataset is tested using single machine learning models but the
ensemble classifier gives an overall efficiency of 99.8%.
Keywords:
Artificial neural
Ensemble classifier
Intrusion detection system
Machine learning
Networks attack
This is an open access article under the CC BY-SA license.
Corresponding Author:
Tobiloba Emmanuel Somefun
Department of Electrical and Information Engineering, Covenant University
Canaan Land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria
Email: tobi.shomefun@covenantuniversity.edu.ng
1. INTRODUCTION
Access to the internet is very crucial to every business and individual in the 21st
century [1], [2]. It is
nearly impossible to compete in today’s business world without staying connected to the world and
customers. Staying connected to the internet is advantageous in the business world, but these advantages are
not equipped to eliminate the accompanying threats, and it would be a disaster in this 21st
century cyber-age
and cyberspace if the power of a single click on the internet is ever underestimated [3], [4]. The possibility of
these threats gave rise to the need for protective measures on the internet [5], [6]. Many confidential
transactions occur every second. These exchanges on the web give an approach to unfrosted gatherings
outside to obtain entrance into an organization’s private organization and mess with the inside climate, data,
assets, and structure. Network security helps us maintain the authorized access of data from hackers and
authenticated data transfers, and we achieve the security of the network when a firewall is installed and
turned ON.
With the rise in internet and network use [7], the need for security has become tantamount to user’s
convictions and interest to perform sensitive functions and activities on the internet or any cloud-based
network system [8]–[10]. As the internet evolves, likewise the various malicious software hosted on the
network and the attacks have become increasingly sophisticated [11]. In a 2017 report released by Symantec,

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Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 983-992
984
on internet security threat, it recorded over three billion zero-day assaults in 2016, this implied that the
assaults were gaining popularity and becoming increasingly common unlike before [12]. The 2017 data
breach statistics recorded around nine billion lost or hijacked information records since 2013. A Symantec
report tracked down that the quantity of safety penetrate occurrences is rising rapidly [13]. Various malicious
software that penetrates internal company networks have become more sophisticated, directly affecting the
severity of attacks companies experience, even as security measures evolve with time [14]. Several reports
have revealed that security breaches are consistently on the rise. Tactics of cybercriminals have begun to
change with the times, and as some researchers would describe it, more ambitious [15], [16]. Previously these
attackers targeted “smaller fish” like credit cards, bank customers, bank accounts, whereas these days, they
target the banks themselves [17]–[20]. All these are possible because of the evolution of malicious software
[21]–[23]. Malicious Software (Malware) is intentionally designed to take advantage of any compromise, or
weakness however minute, in the firewall to gain access to the inside network.
A survey carried out by Kaspersky in 2013 revealed that 91% of companies had experienced at least
one security threat from outside the company network, 35% of these companies encountered data leakage due
to these attacks [24]. 61% of these companies were attacked by spam, while another 66% of the companies
were affected by viruses, spyware, malware, worms, and other malicious programs. Even though the attack
rate is this high, the discovery rate for malware and intrusions is still low [25], [26]. In Panda Lab’s 2015
annual report, the following discoveries were made; 34% of all malwares were produced in 2014. 65% of
attacked systems were intruded on by Trojans, making Trojans the major contributor of security threats. This
report concludes that despite the depth of research and development of network security infrastructure, online
inform action will still be exploited by new forms of attack [27].
Cloud infrastructure utilizes integrated technologies, virtualization techniques, and it moves
according to standard internet protocols, which may attract unauthorized users due to the weaknesses present
in the cloud infrastructure. Distributed computing experiences different conventional assaults that include
protocol spoofing, address resolution, internet protocol (IP) spoofing, flooding, distributed denial of service
(DDoS), domain name system (DNS), poisoning, denial of service (DoS), and routing information protocol
attack. A genuine model is the DoS assault on the fundamental Amazon Cloud framework that caused
BitBucket.org, a site facilitated on Amazon web services (AWS), to stay inaccessible for a couple of hours
[28]. Firewalls can be an effective method to protect a network from external attacks, but it is not applicable
for internal attacks; therefore, an efficient intrusion detection system (IDS) should be fused with Cloud
infrastructure to alleviate these attacks. In this study, authors seek to find out the cause for the attacks on the
networks and investigate ways to identify and curb these attacks. Also, to discover and recommend better
security measures for the protection of networks and network-based systems from security attacks/threats.
2. MATERIAL AND METHOD
In this study, the Network Security Laboratory-knowledge discovery in databases (NSL-KDD)
dataset is used instead of the original KDD Cup 99 dataset, because it gives a good understanding of
intrusion behaviors. Six processes were involved in the approach followed in this study which are data
collection, data pre-processing, feature scaling, feature selection, model development, accuracy evaluation.
The NSL-KDD dataset, which comprises network packets with 42 attributes is used for data
collection. The data is thereafter pre-processed into a suitable form to be utilized by the algorithm.
Pre-processing involves cleaning the algorithm to remove duplicate and redundant entries. Every feature is
transformed to a numerical value/feature by “one-Hot encoding,” which converts objects/string values into
categorical data and is then converted to numerical data using label Encoder in-built in Python. To avoid
features with large values that may weigh too much in the results and eventually lead to overfitting, the
features must be scaled. After the conversion, the dataset is split into 4 different datasets, each representing
the different attack categories. Attack categories are shown in Table 1. The attack categories are renamed as
0=normal, 1=DoS, 2=Probe, 3=R2L, 4=U2R.
Table 1. Attack types in NSL-KDD dataset
Attack category Attack name
Denial of service (DoS) Apache2, Smurf, Neptune, Back, Teardrop, Pod, Land,
Mailbomb, Processtable, UDPstrom
Remote to local (R2L) WarezMaster, Imap, Ftp_Write, Named, MultiHop, Phf, Spy,
Sendmail, SnmpGetAttack, SnmpGuess, Worm, Xsnoop, Xlock
User to root (U2R) probe Buffer_Overflow, Httptuneel, Rootkit, LoadModule, Perl,
Xterm, Ps, SQLattack, Satan, Saint, Ipsweep, Portsweep,
Nmap, Mscan

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Experimental analysis of intrusion detection systems using machine learning … (Ademola Abdulkareem)
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StandardScaler() library is used to scale the data frames and ensure the standard deviation is 1. The
univariate feature selection using analysis of variance (ANOVA) F-test (second percentile method) is first
used, followed by the recursive feature elimination (RFE) method, to get the best features for each dataset.
The formula for each classifier is already built-in to Python, so each attack dataset goes through all the
different classification algorithms before producing results.
2.1. Decision tree classifier
A decision tree (DT) classifier is a popular machine learning algorithm used for both classification
and regression tasks. It recursively partitions a dataset into subsets based on the most significant features,
effectively creating a tree-like structure of decisions. These splits are determined by various criteria, with one
common measure being Gini impurity, which quantifies the randomness or impurity in each subset. Subset is
determined using (1).
∑ 𝑓𝑖(1 − 𝑓𝑖)
𝑐
𝑖=1 (1)
where 𝑓𝑖 is the frequency of labels at a node, and c is the number of unique labels.
2.2. Support vector machine classifier
A support vector machine (SVM) classifier aims to find the optimal hyperplane that best separates
different classes in the feature space. By maximizing the margin between data points and the hyperplane,
SVM enhances its generalization performance, proving especially effective in high-dimensional spaces
commonly encountered in image and text analysis. The hyperplane position is determined by support vectors,
which are the data points closest to the decision boundary, playing a crucial role in defining the classification
boundary accurately.
2.3. K-nearest neighbors algorithm
Unlike some others, the k-nearest neighbors (KNN) is non-parametric, which implies that it makes
no assumptions about the underlying data. It can be used for both regression and classification problems but
primarily for classification. This algorithm stores data such that when a new data entry is made, it quickly
classifies it based on its similarity to already existing data points. Classification algorithm; given a query
instance 𝑥q to be classified, let 𝑥𝑖,…, 𝑥𝑘 denote the k instances from the training examples.
Return 𝑓(𝑥q) ← arg max ∑ 𝛿(𝑣, 𝑓(𝑥𝑖))
k
𝑖=1 for the discrete-valued target function
where (𝑎, 𝑏) = 1 if a=b and where 𝛿(𝑎, 𝑏) = 0, otherwise. The weights of neighbors are taken into
consideration relative to their distance to the query point such that:
𝑓(x𝑞) ← 𝑎𝑟𝑔𝑚𝑎𝑥v∈V ∑ 𝑤𝑖 𝛿(𝑣, f(x𝑖))
𝑘
𝑖=1 (2)
where 𝑤𝑖 =
1
d(x𝑞,x𝑖)
2.
2.4. Artificial neutral network classifier
Artificial neutral network (ANN) is a supervised machine learning (ML) algorithm that is based on
the human brain. The advantage of using this algorithm is its performance ability in nonlinear modelling.
Also, because of it is various layers, it provides a more accurate representation of the predictions. In
developing this model, the dataset is fed into the model 5 times to make provisions for the system memory
and improve the accuracy metric for each attack type.
2.5. Ensemble classifier
The dataset is run through the different classification algorithms that have been previously used. it
goes through the DT, KNN, and SVM classifiers one after the other. This is also done to measure for an
improved accuracy compared to the individual testing and training carried out on the dataset by each
classification algorithm.
3. RESULT AND DISCUSSION
This section discusses the implementation of the machine learning algorithms discussed in section 2.
Furthermore, it explains commonly used evaluation metrics for machine learning methods for IDS. The

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general confusion matrix, which is used to visualize the performance of our supervised learning algorithms is
shown in Table 2.
Table 2. Confusion matrix
Actual Class Predicted class
Attack Normal
Attack True positive False negative
Normal False positive True negative
3.1. DoS attack
After running our DoS attack dataset through this decision tree, SVM, and KNN classifiers, the
results are shown in Tables 3 and 4. Table 3 shows the confusion matrix for DoS attacks, classified using the
three stated classifiers algorithm, while Table 4 shows other metrics tested for by the classifiers. Metrics such
as precision, recall, accuracy, and F-measure.
Table 3. Confusion matrix for three classifiers on DoS attack
DoS attack Predicted attacks Classifier
Actual attacks 0 1 DT
0 9,602 109
1 2,625 485
0 1 SVM
0 9,677 34
1 3,578 3,882
0 1 KNN
0 9,653 58
1 2,645 4,815
Table 4. Evaluation metrics for three classifiers on DoS attack
Metrics Precision Recall F-Measure Support Classifier
0 0.79 0.99 0.88 9,711 DT
1 0.98 0.65 0.78 7,460
Accuracy - - 0.84 17,171
Macro avg 0.88 0.82 0.83 17,171
Weighted avg 0.87 0.84 0.83 17,171
0 0.73 1 0.84 9,711 SVM
1 0.99 0.52 0.68 7,460
Accuracy - - 0.79 17,171
Macro avg 0.86 0.76 0.76 17,171
Weighted avg 0.84 0.79 0.77 17,171
0 0.78 0.99 0.88 9,711 KNN
1 0.99 0.65 0.78 7,460
Accuracy - - 0.84 17,171
Macro avg 0.89 0.82 0.83 17,171
Weighted avg 0.87 0.84 0.83 17,171
The classifier resulted in 9,602 correctly predicted attacks from the 12,821 data entries/input. Only
485 out of the 3,110 standard entries were accurately predicted as regular attacks by this classifier. This is
shown in Table 3. From this result, we can see that this decision tree classifier produces better attack
predictions compared to typical network behavior. The accuracy of this method is 0.84 but can be improved
on. This will be revealed in the results of the ANN and ensemble classifier.
After running the DoS attack dataset through the SVM classifier, which uses a subset of training
points in the decision function. The accuracy of the classification algorithm is measured using the metrics
recorded is 0.79. The KNN classifiers is a simple algorithm that stores and classifies cases based on
similarity measures such as distance functions. The confusion matrix for this classifier shows a more robust
prediction for the regular network behavior compared to that of the decision tree and strongly predicts the
DoS attacks. The accuracy for this classifier is 0.84, like that of the decision tree classifier.
The results from the ANN classifier for the DoS attacks using the tensor flow framework in Python
is a loss metric of 0.0602, and an accuracy of 0.975. The graphs Figure 1 compares the three classifiers with
ANN. In Figure 1, all precision metrics for the DoS attacks are mapped out, and the DT algorithm has a more
precise measure for DoS attacks.

987
The accuracy of predicting DoS attacks using all the different classification algorithms is compared
in Figure 1, and it is evident that the ANN has higher accuracy. This can result from the deep neural networks
utilized in developing the ANN model, unlike the other machine learning algorithms where the dataset is fed
into the classifier only once.
Figure 1. Accuracy in DoS attack
3.2. Probe attacks
These attacks are deliberately crafted so that the legitimate users of the network recognize the
intrusion and report it. After reporting the attack, the attacker uses recognizable fingerprints to learn more
about the network capabilities. After running the dataset through the decision tree, SVM, and KNN
classifiers, the results from the confusion matrix are shown in Table 5. It shows the ability of the classifier to
predict attacks accurately. The results signify that the decision tree classifier may not be the best for
predicting probe attacks. Inasmuch as the false negative and false positives are less than the true negative and
true positive, the values are still relatively large. The accuracy of this classification algorithm is found to be
84% as shown in Table 6.
Table 5. Confusion matrix for three classifiers on probe
Probe attack Predicted attacks Classifier
0 8,709 1,002
1 944 1,477
0 2 SVM
0 9,074 637
1 958 1,463
0 2 KNN
0 9,107 604
1 943 1,478
Table 6. Evaluation metrics for the three classifiers on probe
0 0.9 0.9 0.9 9,711 DT
1 0.6 0.61 0.6 7,460
Accuracy - - 0.84 17,171
Macro avg 0.75 0.75 0.75 17,171
Weighted avg 0.84 0.84 0.84 17,171
0 0.9 0.93 0.92 9,711 SVM
1 0.7 0.6 0.65 7,460
Accuracy - - 0.87 17,171
Macro avg 0.8 0.77 0.78 17,171
Weighted avg 0.86 0.87 0.86 17,171
0 0.91 0.94 0.92 9,711 KNN
1 0.71 0.61 0.66 7,460
Accuracy - - 0.87 17,171
Macro avg 0.81 0.77 0.79 17,171
Weighted avg 0.87 0.87 0.87 17,171
0
0.2
0.4
0.6
0.8
1
1.2
Decision
Tree
SVM KNN ANN
Accuracy
Machine Learning Algorithm
Accuracy in DoS Attacks

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SVM can be used for regression and classification. Since this is a classification problem, it is used
here for classification. It works by finding an optimal boundary between two outputs. Accuracy of this
classifier is 87%.
The results gotten from the KNN classifier are shown in Tables 5 and 6. There is a significantly high
prediction possibility, evident in the true negative and true positive values. The accuracy of this classification
algorithm is measured to be 87% and given as the output of the code in Python.
The ANN classifier evaluated in the tensor flow framework of the Python IDE gives an accuracy of
88.7%, with a loss measure of 0.321. The loss in this classification algorithm is high. The accuracy measure
is not as high as expected because information security must be optimal enough to predict over 90% of
attacks. From Figure 2, it is clear that the ANN classifier has the highest accuracy, which the presence of
more layers can explain unlike the single layers of the other machine learning algorithms.
Figure 2. Accuracy in probe attacks
3.3. R2L attack
The R2L attack type represents a scenario where a user without remote network access attempts to
send packets to gain unauthorized entry. In the context of our analysis, the decision tree classifier's
performance in detecting these R2L attacks is depicted in the confusion matrix displayed in Table 7. This
matrix reveals a remarkably high prediction rate, underscoring the effectiveness of the decision tree model in
identifying and mitigating such intrusion attempts.
Table 7. Confusion matrix for three classifiers on probe
0 9,649 62
1 2,560 325
0 SVM
0 9,711 0
3 2,885 0
0 3 KNN
0 9,710 1
3 2,885 0
The accuracy measure of this method is gotten to be 79% as shown in Table 8. This is not a very
high accuracy for internet security, so we will use other classification algorithms to decide on the model with
the highest accuracy. The results outputted from the code for this classifier show us an accuracy level of
77%. This accuracy level is not good enough for network security purposes, so other classification algorithms
and ANN are used to analyses the accuracy levels.
The accuracy of this classification algorithm is also 77% which is still not good enough for network
security. So far, we have seen that machine learning algorithms are not the best for predicting R2L attacks.
The ANN classifier gives an output of 0.9998 and a loss of 0.003. This accuracy level is very efficient for a
network security prediction model. Figure 3 is a graphical representation of the different classification
0.84
0.87 0.87
0.887
0.8
0.82
0.84
0.86
0.88
0.9
Decision
Tree
SVM KNN ANN
Accuracy
Accu r acy in Pr ob e Attacks

989
algorithms used to analyses the R2L dataset. The ANN classifier produces a more robust accuracy, unlike the
machine learning algorithms.
Table 8. Evaluation metrics for the three classifiers on probe
0 0.79 0.99 0.88 9,711 DT
1 0.84 0.11 0.2 7,460
Accuracy - - 0.84 17,171
Macro avg 0.82 0.55 0.54 17,171
Weighted avg 0.8 0.79 0.72 17,171
0 0.79 0.99 0.88 9,711 SVM
1 0.84 0.11 0.2 7,460
Accuracy - - 0.84 17,171
Macro avg 0.82 0.55 0.54 17,171
Weighted avg 0.8 0.79 0.72 17,171
0 0.77 1 0.87 9,711 KNN
1 0 0 0 7,460
Accuracy - - 0.77 17,171
Macro avg 0.39 0.5 0.44 17,171
Weighted avg 0.59 0.77 0.67 17,171
Figure 3. Accuracy in R2L attacks
3.4. U2R attacks
User 2 Root (U2R) attack is the illegal access of the root of a network by a local user who has only
been granted access to the leading network, not the network’s backend. With the considered three classifiers
in this study, their true positive, true negative, false positive, and false negative are shown in Table 9 while
the other metrics are given in Table 10. The accuracy output of this classification metric is very high at 99%,
making it very efficient and appropriate for predicting network attacks.
As shown in Figure 4, the accuracy for the U2R attacks using the SVM classifier has an extremely
high accuracy of 99%. This signifies that the SVM classifier efficiently predicts future U2R attacks on a
network. Also, the accuracy of this KNN classifier is 99%, meaning it would be very efficient in predicting
attacks and protecting the network from intrusion. The results from the ANN classification produced an
output prediction value of 99.69%.
3.5. Ensemble classifier
The ensemble classifier is a combination of the various classifier previously used. This is
experimented upon the dataset to determine the accuracy of identifying attacks. The ensemble classification
was carried out on the R2L attack to see if there will be an increase in it is 77% accuracy, which was
obtained from the other independent machine learning algorithms. The output from the ensemble classifier
outputted a whooping accuracy of 99.98%.
From the results output, which has been visualized in the Table 8 and Figure 5, it is clear that using
the ANN classifier is the most accurate way to predict network attacks and intrusions. The ANN classifier
produces results that are close in metric to the ensemble classification, i.e., the combination of the various
machine learning algorithms.
0.79 0.77 0.77
0.9998
0
0.2
0.4
0.6
0.8
1
1.2
Decision
Tree
SVM KNN ANN
Accuracy
Accuracy in R2L Attacks

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Table 9. Confusion matrix for the classifier on U2R
0 9,706 5
4 52 15
0 4 SVM
0 9,711 0
4 67 0
0 4 KNN
0 9,709 2
4 60 7
Table 10. Evaluation metrics for the three classifiers on U2R
0 0.99 1 1 9,711 Decision Tree
1 0.75 0.22 0.34 67
Accuracy - - 0.99 8,778
Macro avg 0.87 0.61 0.67 9,778
Weighted avg 0.99 0.99 0.99 9,778
0 0.99 1 1 9,711 SVM
1 0 0 0 67
Accuracy - - 0.99 8,778
Macro avg 0.5 0.5 0.5 9,778
Weighted avg 0.99 0.99 0.99 9,778
0 0.99 1 1 9,711 KNN
1 0.78 0.1 0.18 67
Accuracy - - 0.99 8,778
Macro avg 0.89 0.55 0.59 9,778
Weighted avg 0.99 0.99 0.99 9,778
Figure 4. Accuracy in U2R attacks
Figure 5. Accuracy in U2R attacks
0.99 0.99 0.99
0.9969
0.986
0.988
0.99
0.992
0.994
0.996
0.998
Decision
Tree
SVM KNN ANN
Accuracy
Accuracy in U2R Attacks
0
0.2
0.4
0.6
0.8
1
1.2
DoS
Probe
R2L
U2R
DoS
Probe
R2L
U2R
DoS
Probe
R2L
U2R
DoS
Probe
R2L
U2R
DoS
Probe
R2L
U2R
Decision Tree SVM KNN ANN Ensemble
Classifier
Accuracy
Accuracy of various Machine Learning Algorithms

991
4. CONCLUSION
In this study, various network intrusions were analyzed using several machine learning algorithms
as classifiers. This was to see how accurately, and intelligently various machine learning algorithms detect
network intrusions when encountered in a system. These experiments were carried out to analyses the
NSL-KDD dataset, which revealed that the dataset is ideal for comparing intrusion detection models. 99%
accuracy was obtained on some of the intrusion detection models developed. The experiments have
demonstrated that there is no single machine learning algorithm that can efficiently handle all types of
attacks, but the models can be trained to give efficiencies up to 99.98% which will tremendously predict and
prevent attacks from flooding the network.
ACKNOWLEDGEMENTS
The authors will like to appreciate Covenant University for her financial support.
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BIOGRAPHIES OF AUTHORS
Ademola Abdulkareem is a renowned researcher and associate professor of
electrical engineering in Covenant University. His areas of specialization are electrical and
electronics engineering, power systems quality analysis and control and energy. His
research interests are optimization of power system network and security, power system
investment planning, high voltage engineering, smart power distribution system and intelligent
building and energy management and renewable energy. He can be contacted at email:
ademola.abdulkareem@covenantuniversity.edu.ng.
Tobiloba Emmanuel Somefun is a Ph.D. holder from Covenant University in
electrical and electronics engineering. His research interest covers energy management,
power system analysis, and data analysis. He can be contacted at email:
tobi.somefun@covenantuniversity.edu.ng.
Adesina Lambe Mutalub currently works as a senior lecturer at the Electrical
and Computer Engineering Department, Faculty of Engineering and Technology, Kwara State
University, Malete, Nigeria. L. M. Adesina does research in engineering education and
electrical engineering. He can be contacted at email: lambe.adesina@kwasu.edu.ng.
Adewale Adeyinka obtained his Ph.D. in information and communication
engineering at Covenant University. His research interests are quality of service (QoS) in
mobile or wireless communication; computer network security; artificial intelligence
application; digital signal processing; software design and programming; electric power
distribution; electrical installations; and Seismic or geophysical data acquisition operations. He
can be contacted at email: adeyinka.adewale@covenantuniversity.edu.ng.

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