Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

Abstract

The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical records of patients. Once an attack is detected, healthcare professionals are promptly alerted to prevent data leakage. The paper utilizes two datasets, namely the synthetic dataset and the University of Queensland Vital Signs (UQVS) dataset, from which numerous samples are collected. Experimental results are conducted to evaluate the performance of the proposed CMLP model, utilizing various performance measures such as Recall, Precision, Accuracy, and F1-score to predict patient activities. Comparing the proposed method with existing approaches, it achieves the highest accuracy, precision, recall, and F1-score. Specifically, the proposed method achieves a precision of 93%, an accuracy of 97%, an F1-score of 92%, and a recall of 92%.

Full text

This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
Com puter Modeling in
Engineering & Sciences ech
T
PressScience
DOI: 10.32604/cmes.2023.044169
ARTICLE
Enhancing Healthcare Data Security and Disease Detection Using
Crossover-Based Multilayer Perceptron in Smart Healthcare Systems
Mustufa Haider Abidi*, Hisham Alkhalefah and Mohamed K. Aboudaif
Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box-800, Riyadh, 11421, Saudi Arabia
*Corresponding Author: Mustufa Haider Abidi. Email: mabidi@ksu.edu.sa
Received: 23 July 2023 Accepted: 27 October 2023 Published: 30 December 2023
ABSTRACT
The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure
proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare
Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting
various diseases and monitoring patient activities. These methods are employed across different domains that are
susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based
Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based
multilayer perceptron neural network to detect adversarial attacks on the medical records of patients. Once an attack
is detected, healthcare professionals are promptly alerted to prevent data leakage. The paper utilizes two datasets,
namely the synthetic dataset and the University of Queensland Vital Signs (UQVS) dataset, from which numerous
samples are collected. Experimental results are conducted to evaluate the performance of the proposed CMLP
model, utilizing various performance measures such as Recall, Precision, Accuracy, and F1-score to predict patient
activities. Comparing the proposed method with existing approaches, it achieves the highest accuracy, precision,
recall, and F1-score. Specifically, the proposed method achieves a precision of 93%, an accuracy of 97%, an F1-score
of 92%, and a recall of 92%.
KEYWORDS
Smart healthcare systems; multilayer perceptron; cybersecurity; adversarial attack detection; Healthcare 4.0
1Introduction
The fundamental component of everyone’s quest for a better life is health, which is categorized
as a complete condition of mental, physical, and social well-being. A health system possesses every
organization, action, people, attempt to affect health determinants, and effort to improve health
[1]. Enhancing health, maintaining health, and restoring health is the main objective of this. The
healthcare domain is currently being met with developments based on new treatment methods and
technologies [2]. The smart healthcare system has greatly modified medical professionals’and patients’
lives. Recently, several healthcare applications are there, those are inserted in consumer devices for
gathering physiological information about a patient and giving treatments in automatic [3]. The
healthcare system should have access control mechanisms and policies to assist the diversity of needed
access. The healthcare data has financial, operational, and clinical value when it is explored correctly

978 CMES, 2024, vol.139, no.1
for extracting primary features [4,5]. Machine learning (ML) models are currently applied in several
fields [6–8]. In the healthcare domain, ML models can be used in several ways, such as exploited for
extracting valuable features from the high-dimensional data for predicting various diseases as well
as the activities of the patient [9]. Adversarial machine learning encompasses strategies designed to
generate incorrect predictions and deceive machine learning models, rendering them ineffective when
subjected to perturbed input [10]. Adversarial attacks can influence any type of data, including images
and videos; therefore, ML algorithms sometimes produce inaccurate detection. Generally, maximum
attacks occur in medical imaging data to change the already detected disease [11].
Adversarial attacks, as used in the context of healthcare data security, describe apparent and
frequently malicious attempts to modify, falsify, or corrupt confidential patient information. These
attacks can take a number of different forms, like inserting bogus data, changing patient records, or
attempting to trick machine learning models by supplying carefully constructed inputs that lead to
false predictions. Adversarial attacks seriously threaten the accuracy and reliability of disease detection
models and healthcare data analysis.
Several methods have analyzed the impact of the attacks and security of the healthcare system
for addressing adversarial attacks [12,13]. Researchers have made advancements in the adversarial
attack detection model; however, the attacks on the healthcare system used in the detection require
proper assessments [14]. In the realm of healthcare data analysis and disease prediction, a persistent
challenge that has garnered significant attention is the issue of ‘instability in disease prediction.’ This
phenomenon refers to the susceptibility of machine learning models, particularly those operating
on complex and high-dimensional healthcare data, to exhibit fluctuations in their predictions over
different instances or input variations. The instability arises due to the intricate interplay of various
factors, such as data noise, feature heterogeneity, and model complexity. In practical terms, the
instability in disease prediction poses a substantial barrier to machine learning models’consistent and
reliable performance in healthcare applications. It engenders uncertainty in diagnoses and prognoses,
impeding the seamless integration of predictive analytics into clinical decision-making workflows.
The potential consequences of this instability include misclassification of diseases, inaccurate risk
assessments, and hindered patient management strategies.
These methods can have issues such as identifying patient movement and have high compu-
tational costs. Some methods also have impacts in adversarial attacks that have an effect on the
smart healthcare system but cannot be detected by the system’s overall security. Hence, better and
optimized solutions are required. Therefore, this paper proposes the Crossover-Based-Multilayer
Perceptron (CMLP) method for detecting adversarial attacks in the smart healthcare domain. The
main contribution of the paper is as follows:
CMLP method: To identify the patient’s activities accurately, a CMLP is developed. MLP is mainly
proposed for detecting patient activities with high accuracy. The crossover optimization algorithm is
primarily deployed to enhance the developed MLP model.
Validation utilizing datasets: Synthetic and UQVS datasets are exploited to validate the proposed
method. The synthetic dataset possesses 17000 samples, and UQVS consists of 209,115 samples, which
evaluate 26 vital signs.
Improved performances: Experimental results show that the proposed method has achieved the
most remarkable performance compared to the other methods. The proposed method gets good
performance using performance measures such as precision, recall, F1-score, and accuracy.
In brief, in today’s data-driven healthcare landscape, where the accurate and secure analysis of
patient information is paramount, ensuring data integrity is a growing concern. Adversarial attacks

CMES, 2024, vol.139, no.1 979
can undermine the trustworthiness of predictive models, impeding accurate disease prediction and
patient monitoring. The use of machine learning models in healthcare is significantly affected by
ethical considerations, particularly those relating to patient consent and data protection. Fundamental
ethical considerations include protecting patient information and obtaining explicit agreement before
using data. In order to foster confidence between patients, healthcare professionals, and these
automated systems, transparent and interpretable models are becoming more and more important in
the field of medicine. To maintain patient rights, data security, and the general integrity of healthcare
procedures, responsible development, and deployment of these models should also be guided by strict
ethical norms. Addressing these concerns forms the backdrop for this research work, motivating the
development and evaluation of the proposed CMLP model. The remaining work of the paper is
structured as follows: Section 2 represents the related works, and Section 3 explains the proposed
methodology. Section 4 demonstrates the experimental results, and the conclusion of the work is
presented in Section 5.
2Literature Review
A plethora of research is going on in the cyber-security of healthcare. Ahmed et al. [15] illustrated
a patient discomfort detection model based on deep learning-based smart healthcare systems in an
IoT (Internet of Things) environment. The model utilized Mask–Region-Based Convolutional Neural
Network (Mask-RCNN) for detecting key points of a patient’s body by different features. The adjacent
key points’ distance is measured to analyze the discomfort in the patient’s body parts. The model’s
performance was evaluated using various metrics such as recall, precision, and accuracy. As a result,
this model detected each organ of the human body with high accuracy. Meanwhile, it did not correctly
detect the patients’ head movements and facial expressions.
Tuli et al. [10] introduced an automatic heart disease detection model Health Fog using a deep-
learning-based healthcare system in an internet of things (IoT) environment. The deployment of
Health Fog was used to analyze the real-time heart patient data by integrating IoT and fog computing.
The model was examined by using evaluation measures such as bandwidth, accuracy, response time,
and energy consumption. The result found that this model offered various configurations and attained
higher accuracy based on the requirements of heart patients. However, it should be noted that Health
Fog only supported file-based input data.
Raina et al. [16] developed an Intelligent and Interactive Healthcare System to focus on the com-
prehensive utilization of Speech Recognition. The Hidden Markov Model is used for implementation
since it is more practical to use a probabilistic method to hold the active characteristics of optical
features. Moreover, it maximizes efficiency but automatically reduces the requirement for any human
intervention in case of any failure. Speech recognition execution is provided with various resource
allocations, energy optimization, and energy efficiency methods. Sufficient data is not obtainable to
train the methods for effective and trustable outcomes.
Liu et al. [17] proposed deep learning (DL)-based channel state information to reveal the effective
output of adversarial attacks on DL-based communication systems. A jamming attack is introduced in
CsiNet to calculate the reaction of the adversarial attack and the outcomes of the NMSE computation.
The COST 2100 channel version is deployed to simulate forms of channel state information (CSI)
datasets in indoor and outdoor scenarios. The test results show analyzing the efficiency of mean square
error and the disastrous effect of the adversarial attack on DL-based CSI feedback. The channel state
information model is stricken by major attacks in several scenarios, which trigger our attentiveness to
DL-based real-world atmospheres.

980 CMES, 2024, vol.139, no.1
For the medical image evaluation mechanisms based on DL, Ma et al. [18] established an
understanding of malicious attacks. The deep neural network (DNN) was the best medical image
evaluation method for identifying lesions and detecting cancer. A finely conducted attack could able
to make changes in the deep learning mechanisms for medical image analysis. This indicated the
challenges in the implementation of these mechanisms in medical fields. A deeper knowledge of various
examples of malicious attacks in clinical images was presented in this article. It was observed that the
DNN used in the medical field gets easily affected by malicious attacks. The current attacks were 98%
identified by the use of normal detectors. These findings could be used to develop safe deep-learning
mechanisms for the medical field. An efficient intrusion detection for cloud computing was presented
by Javadpour et al. [19]. Researchers also developed a distributed multi-agent intrusion detection
system for cloud IoT environment called DMAIDPS [20]. The results reported that the developed
system has improved metrics when compared against other systems available in the literature.
Moreover, Blockchain is a contemporary technology that is very efficient in data security [21].
In the Healthcare 4.0, Bhattacharya et al. [22] employed a Blockchain-based DL as-a-service. The
sharing of health records by patients was made easier by the electronic health record (EHR), and it
also developed risks such as security, privacy, and authenticity. To rectify these challenges created,
a Blockchain-based Deep-Learning as-a-service (BinDaas) was proposed in their research work.
The proposed method combines deep learning methods and blockchain to share the EHR records
between various users. According to the cryptography based on lattices, the verification and signature
method was developed in the first stage for resisting attacks between medical authorities. According
to the present indicators and patient characteristics to forecast upcoming diseases, Deep Learning
as-a-Service was applied to EHR datasets in the second stage. The proposed method outperformed
the conventional methods in terms of communication/calculation cost, delay, accuracy, and time of
mining. The high communication cost required was its limitation. Hady et al. [23] proposed a real-time
Enhanced Healthcare Monitoring System (EHMS). It records patients biometrics and sent the metrics
for further diagnostics and treatment. Several datasets were tested and the system performance showed
and improvement of 7%–25% in some cases. Kumaar et al. [24] presented a hybrid system based on deep
learning for detecting intrusions in healthcare environment. They named their systems as ImmuneNet.
The suggested framework employs a variety of feature engineering techniques, class balance-improving
oversampling techniques, and hyper-parameter optimization methods to achieve excellent accuracy
and performance. Some other researchers utilized adaptive neuro-fuzzy inference system for intrusion
detection in IoT based healthcare system [25]. The efficacy of the proposed method was tested with
various databases. Jeyanthi et al. [26] presented a a recurrent neural network (RNN) and a bidirectional
long short-term memory algorithm to detect and classify intrusion attempts. The results reported
that the proposed system achieved an accuracy of 99.16%, sensitivity ratio of 99.89%, error rate of
0.008371%, and specificity ratio of 98.203% for the given dataset. Iwendi et al. [27]employedthe
random forest with genetic algorithm for intrusion detection in healthcare. A high detection and low
alarm rate was reported.
Ahmed et al. [28] presented a comprehensive literature review for the ML based methods used
in intrusion detection for healthcare data. Kilincer et al. [29] proposed an automated cyber-security
system based on recursive feature elimination (RFE) and multilayer perceptron for detecting attacks in
healthcare system. Savanovi´
cetal.[30] presented a ML method which was optimized by metaheuristics
for intrusion detection in healthcare 4.0 system. XGBoost with modified firefly algorithm was utilized.
To encrypt sensitive data and lessen privacy breaches and cyberattacks from unauthorized users and
hackers, a Lionized remora optimization-based serpent (LRO-S) encryption method was proposed by
Almalawi et al. [31]. It was reported that the suggested approach reduced the time required to encrypt
and decrypt data while raising privacy standards.

CMES, 2024, vol.139, no.1 981
The literature review reveals a notable research gap in the domain of healthcare data security
and disease detection. Previous studies have often focused on individual aspects of data security or
disease prediction, but there has been limited exploration of comprehensive approaches that integrate
both aspects while addressing the challenges posed by adversarial attacks. This research work bridges
this gap by proposing a novel CMLP model that not only enhances healthcare data security but also
improves the stability and accuracy of disease detection within the context of smart healthcare systems.
By amalgamating these critical components, we aim to provide a holistic solution that safeguards
patient information and augments the reliability of predictive analytics in healthcare.
3Proposed Methodology
In recent years, ML has been employed in various industries to fulfill needs; in the healthcare
domain, one example is that it assists in developing models for effective results based on the patient’s
critical data. A schematic diagram of a smart healthcare system in a key configuration is delineated
in Fig. 1. In this research work, initially, the data is collected from two different datasets, namely the
synthetic dataset and the University of Queensland Vital Signs (UQVS) dataset. The collected samples
are then pre-processed and provided to the CMLP neural network to detect whether the adversarial
attack affects the patients’ medical records.
Figure 1: Architecture of the proposed model

982 CMES, 2024, vol.139, no.1
The selection of datasets in this study was carefully considered to provide a balanced evaluation of
the proposed CMLP model. The synthetic dataset was chosen due to its controllability and flexibility,
enabling us to introduce and test adversarial attacks under controlled conditions systematically.
The UQVS dataset, on the other hand, was selected to bring real-world relevance to our research.
This dataset comprises vital signs data collected from actual healthcare scenarios, making it an
ideal representative of real-world healthcare data. To ensure data quality and integrity, rigorous
preprocessing steps were applied to both datasets. Data cleaning involved the identification and
rectification of any missing or erroneous entries. Normalization was employed to standardize features,
mitigating the influence of varying measurement units. Feature extraction was carried out with
domain-specific knowledge to capture clinically relevant attributes. This combination of synthetic and
real-world datasets, along with meticulous preprocessing, allowed us to evaluate the proposed CMLP
model’s performance across various scenarios while maintaining data quality.
The suggested CMLP model uses a novel strategy to improve transparency and comprehensibility
to address concerns about machine learning model interpretability. The crossover-based process in the
CMLP model lets us track information through layers, helping healthcare practitioners understand
decision-making. The model emphasizes trustworthy information while minimizing noisy or unstable
signals by selectively merging stable layer properties. This strengthens the model and clarifies its
decision-making process. The CMLP model bridges the gap between neural network computations
and healthcare professionals’ demand for actionable insights they can trust by giving interpretable
insights.
3.1 Data Collection and Pre-Processing
A data collector phase integrates the important symptoms of sick individuals from various smart
healthcare devices and sends the data to the pre-processing phase. The data is then pre-processed on
the report of the sample frequencies associated with the specimen and stored in an array. For example,
monitoring a heartbeat device supervises the heart rate of sick individuals, and at the same time, an
Electrocardiogram (ECG) device computes a patient’s cardiac status every 10 s. Diagnostics in the
smart healthcare system (SHS) utilizing the MLP model and real-time supervision pattern data are
applied for training. The training data are tagged with various ailments and diseases, such as high
blood pressure, low sugar, ECG, etc., in order to realize the data sample. The sick person’s collected
physiological data is utilized in the test module to identify the various ailments detected based on the
previously trained MLP sample. Here, the attack method can be described as the pre-processing of
the data pipeline. An adversary carefully maintains the training data during the MLP training phase
after the attack occurs in a healthcare system and comforts the entire schooling process. Safe protocols
for the transfer of data: The representation of the gathered data is: C_encrypted =Encrypt (C, Key),
where the medical data is denoted as C, the encryption process is shown as Encrypt( ), and the
encryption key is indicated as key.
3.2 Development of the Model
MLP models are highly capable of forming accurate forecasts and understanding difficult
patterns; they are used abundantly in medical data analysis. To improve the safety and performance
of the model, it is proposed to implement dropout regularization, and the development of the deep
learning model is explained in this section. The feedforward neural network contains a few hidden
layers, an output layer, and an input layer, which is the MLP. A large number of neurons are joined to
form every layer, and weighted links are present between these neurons. For making accurate forecasts,
studying the suitable values for these weights is the objective of the MLP model. The CMLP model

CMES, 2024, vol.139, no.1 983
addresses disease prediction instability in a novel way. Its main feature is the crossover mechanism’s
adaptive merging of neural network layer characteristics. Unlike conventional models that predict only
from the final layer output, the CMLP model uses intermediate feature representations from multiple
layers. These intermediate representations undergo a crossover process, transferring information from
stable and resilient layers to fluctuating ones. This technique encourages knowledge sharing and
mitigates noisy or unreliable features. The crossover mechanism helps the model capture subtle data
patterns while minimizing instability-causing disturbances by selectively spreading information flow.
This distinguishes the CMLP model from conventional schemes, making disease prediction more
resilient.
The MLP is determined from the artificial neural network (ANN) that is employed with more
than one hidden layer. A huge number of hidden layers are obtained in the central portion, including
input and output. The simple MLP model is employed with one hidden layer that is determined as a
three-layer structure. The MLP is composed of two parts a feedforward neural network and an error-
back propagation algorithm. The neuron received from the input is related to the threshold φand later
implemented through an activation function a(.) that generates output. The output of a single hidden
layer neuron is formulated as:
x=at

j=1
vjyj−φ(1)
From the above equation vjand yjare the input function. Detecting disorders in healthcare is still
considered a challenging role in medical analysis. So, to overcome this situation, the activation function
makes the neural network an efficient method. The expression for the sigmoid activation function is
expressed as:
S(y)=1
1+e−x(2)
3.2.1 Multi-Layer Feedforward Neural Network
It is determined based on MLP to predict the disease accurately in health care. In this approach,
each neuron is interconnected with one another but not merged with the same layer i.e., it does not
provide a cross-layer connection. The input neuron gathered from gth the hidden layer is formulated
as:
γg=
b

j=1
ujgyj(3)
The output of the hidden layer is determined as:
αi=
s

g=1
vgicg(4)
The output of gth a neuron is represented as:
cg=aγg−g=ab

j=1
ujgyj−g(5)

984 CMES, 2024, vol.139, no.1
The final output layer of the perceptron is expressed as:
xi=k(αi−φi)=ks

g=1
vgicg−φi=ks

g=1
vgi ·ab

j=1
ujgyj−g−φi(6)
where the threshold of the hidden layer is denoted as lg,the output layer threshold is indicated by φi.
The connection weight among the input and hidden layer is represented as ujg based on jth function, and
for ith neurons, it is indicated by vgi . The output activation function is indicated by k(.). The forward
propagation model of a three-layer perceptron is generated in the form of a matrix.
X=⎡
⎢
⎢
⎢
⎢
⎢
⎣
x1
.
.
.
xi
.
.
.
xm
⎤
⎥
⎥
⎥
⎥
⎥
⎦
=⎛
⎜
⎜
⎜
⎜
⎜
⎝
⎡
⎢
⎢
⎢
⎢
⎢
⎣
v11 ··· v1g··· v1s
.
.
.....
.
.....
.
.
vi1··· vig ··· vis
.
.
.....
.
.....
.
.
vm1··· vmg ··· vms
⎤
⎥
⎥
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎢
⎢
⎢
⎣
c1
.
.
.
cg
.
.
.
cs
⎤
⎥
⎥
⎥
⎥
⎥
⎦
−⎡
⎢
⎢
⎢
⎢
⎢
⎣
φ1
.
.
.
φi
.
.
.
φm
⎤
⎥
⎥
⎥
⎥
⎥
⎦
⎞
⎟
⎟
⎟
⎟
⎟
⎠
=k⎛
⎜
⎜
⎜
⎜
⎜
⎝
v⎡
⎢
⎢
⎢
⎢
⎢
⎣
c1
.
.
.
ci
.
.
.
cs
⎤
⎥
⎥
⎥
⎥
⎥
⎦
−φ⎞
⎟
⎟
⎟
⎟
⎟
⎠
(7)
=⎛
⎜
⎜
⎜
⎜
⎜
⎝
k.a⎛
⎜
⎜
⎜
⎜
⎜
⎝
⎡
⎢
⎢
⎢
⎢
⎢
⎣
u11 ··· u1j··· u1b
.
.
.....
.
.....
.
.
ug1ugj ··· ugb
.
.
.....
.
.....
.
.
us1··· usj ··· usb
⎤
⎥
⎥
⎥
⎥
⎥
⎦
⎡
⎢
⎢
⎢
⎢
⎣
y1
yj
yb
⎤
⎥
⎥
⎥
⎥
⎦
−⎡
⎢
⎢
⎢
⎢
⎣
1
g
s
⎤
⎥
⎥
⎥
⎥
⎦
⎞
⎟
⎟
⎟
⎟
⎟
⎠
−φ⎞
⎟
⎟
⎟
⎟
⎟
⎠
=k(v.a(uy −)−φ)(8)
3.2.2 Error Backpropagation Algorithm
In order to update the function, the backpropagation is performed in a MLP, which updates
the loss function of the optimization parameter. The differences obtained between the predicted and
theoretical values are estimated using the mean squared error characterization, which demonstrates
the learning accuracy of the MLP. The training of the output neural network is expressed as follows:
∧
xn=∧
x
n
1,∧
x
n
2,··· ,∧
x
n
m(9)
where
∧
x
n
i=a(αi−φi),i=1, 2, ··· ,m(10)
The mean squared for the training set is formulated as:
Fn=1
2
m

i=1∧
x
n
i−xn
i2
(11)
Based on the gradient descent strategy, the backpropagation optimizes the negative gradient
function. The learning rate η(0, 1)is utilized to direct the step size at each iteration. The updated
value is formulated as:
Δvgi =−η∂Fn
∂vgi
(12)
From the above equation, the connection weight is denoted by vgi. The activation function for the
hidden and output layer based on the sigmoid function is expressed as:

CMES, 2024, vol.139, no.1 985
a(y)=a(y)(
1−a(x)) (13)
Integrating Eqs. (10) and (11) the gradient strategy is obtained as:
ki=−
∂Fn
∂∧
x
n
i
·∂∧
x
n
i
∂αi
=−
∧
x
n
i−xn
ia(αi−φi)=∧
x
n
i1−∧
x
n
ixn
i−∧
x
n
i(14)
The output of MLP based on error propagation diminished the cumulative error obtained while
training.
F=1
r
r

n=1
Fn(15)
However, the error determined in the back-propagation is validated before updating the layer. The
hidden and output layer function is processed, and the cumulative error will be determined as 10−3,or
the iterative process will exceed 105.
In the case of training data, the MLP models are specialized; in the case of unseen data, the MLP
models fail to generalize. The frequently occurring problem in the MLP models is overfitting, which
will reduce safety and performance in medical data analysis. The dropout regularization is applied in
the MLP model to solve this problem. At the time of training, by randomly deactivating a few of the
neurons, the problem of overfitting is prevented by the dropout regularization technique. The model
is forced to understand many representations, and the dropout process introduces redundancy. The
model easily makes forecasts and depends less on particular links by unevenly dropping neurons. In
the MLP model, the dropout normalization is usually linked after one or more hidden layers. The
dropout rate is the probability of being dropped allotted to every neuron in the hidden layer while the
training occurs. 0.2 to 0.5 is the rate of normal dropout; the complexity of the model and the particular
dataset decides the optimal rate of dropout. The dropout is executed at the time of the MLP model’s
forward pass, and zero is fixed as the activations of the neurons drop. For maintaining the anticipated
resulting magnitude, the scaling factor 1/(1−dropout_rate)is used for the leftover active neurons. The
entire input for the upcoming layers is maintained roughly constant by this scaling, and at the time of
training, it recompenses for the neurons that are deactivated. The representation of the MLP model’s
forward pass equation utilizing dropout normalization is given below:
MLP_output =MLP (C_input,W_mlp,b_mlp), (16)
Here the input data is mentioned as C_input, the weight of the MLP is indicated as W_mlp,and
the term of bias is given as b_mlp. Dropout layers are inserted between the hidden layers, deactivating
a certain number of neurons based on the dropout rate. Dropout regularization is implemented in the
MLP model to enhance both the safety of medical data analysis and the generalization of the model.
Using dropout offers several advantages, including improved performance on unfamiliar data, reduced
reliance on specific connections, and the ability to identify strong features.
3.3 Cybersecurity and Privacy Elements
3.3.1 Mechanisms for Access Control
The channel for communication present in between the storage of medical data and the end
user is secured by the Elliptic Curve Integrated Encryption Scheme (ECIES) during the execution
of mechanisms for access control. The system is used to safeguard the valid user’s public key. The

986 CMES, 2024, vol.139, no.1
request for accessing medical data by an end user is encrypted with the receiver’s public key utilizing
ECIES. Data decryption can only be done with the particular key of the valid user.
3.3.2 Encryption of Data
The ECIES is used to safeguard medical data while transferring and storing. The information is
encrypted with the receiver’s public key utilizing ECIES before transferring or storing. From this, it is
known that confidential medical information can only be decrypted and used by the receiver who has
the particular private key.
1. Key Generation: Initially, the creation of an elliptic curve key pair is included in the key
generation process. A private key (c) is produced approximately, and the congruent public
key (R) is obtained, and the base point of the elliptic curve by the private key (R =c∗H)
is increased. The elliptic curve parameters and the base point (H) are preplanned and assigned
to the contract parties.
2. Encryption: The following steps are taken using ECIES to encrypt the message (m).
a. Random Secret Generation: For every encryption, a random secret key (l) is produced to get
uniform encryption, and the secret key is employed for MAC keys.
b. Key Derivation:The receiver’s public key (Q) is increasing with the sender’s private key to
obtain the shared secret (T). The shared secret (T =c∗Q) is then the uniform encryption key
(KE)andtheMACkey(KM) to get the random secret (k) is employed in the key derivation
function (KDF).
c. Symmetric Encryption: With the obtained encryption key the message (m) is encrypted
using a uniform encryption algorithm, such as AES and it assures the surreptitiousness
of the message.
d. Message Authentication Code (MAC) Generation: HMAC and MAC are applied to pro-
duce MAC in the encrypted message along with the obtained MAC key. The MAC
guarantees the encrypted message’s integrity and discovers any interception efforts.
e. Public Key and Encrypted Data Packaging: In the ciphertext, the public key is used for
encryption and the encrypted data consists of the sender.
3. Decryption: Using ECIES the receiver executes the following steps to decrypt the ciphertext
(Q, C):
a) Key Derivation: Increasing the receiver’s private key with the sender’s public key is used to
obtain the secret key (T). The shared secret key (T =c∗R) is then used during encryption
with the uniform and MAC keys as the random secret keys obtained in the key derivation
function.
b) MAC Verification: Using the received MAC key the MAC is computed for the derived
encrypted data, and the probity of the encrypted data is cross-checked when the computed
MAC suits the obtained MAC.
c) Symmetric Decryption: The encrypted data, the received encryption key, and a similar
uniform encryption algorithm are decrypted and used during encryption, and the original
message (m) is shown by the decrypted data.
3.3.3 Data Access Logging and Auditing
When recording instances of data access, ECIES can be applied to keep in existence a preserved
record of data acquisition activities data can be encrypted with the public key of the recording system.

CMES, 2024, vol.139, no.1 987
The acquired records are preserved in the form of encryption, assured only decrypted by the official
recording system. The surreptitiousness and integrity of the records are kept in existence, storing
unofficial access by employing ECIES to access data records.
3.3.4 Security Training and Awareness
In the context of safety training and awareness programs, the use of ECIES can be implemented
to promote secure communication. When distributing sensitive training materials or information
among multiple contributors, the message can be encrypted using the ECIES public key of each
contributor. This ensures that only authorized users with the corresponding private key can decrypt
and access the training materials, thereby maintaining confidentiality and preventing unauthorized
dissemination. This approach ensures the confidentiality and controlled distribution of sensitive
information, reducing the risk of unofficial spreading and unauthorized access.
3.3.5 Crossover Based MLP Algorithm for Adversarial Attack Detection
The application of a crossover AOA-based MLP pattern in safeguarding healthcare data enhances
the aspect of two-step authentication. Following this, the recipients are prompted to undergo authenti-
cation for the second factor, and upon successful user authentication, access is granted. A Time-based
One-Time Password (TOTP) method, such as a mobile application, generates a unique one-time code
to implement the two-step authentication. A shared secret key and the current-time code are generated,
and the receiver’s computer verifies the second component of the code. The system ensures that only
authorized individuals can interact with the healthcare data by integrating two-step authentication,
valid credentials, and second-factor verification.
3.4 Arithmetic Optimization Algorithm (AOA)
The Arithmetic optimization algorithm (AOA) [32] is a completely new meta-heuristic method.
The important suggestion of this algorithm is to mix the four conventional arithmetic operators in
mathematics, i.e., subtraction (s), multiplication (m), division (d), and addition (a).Similartothe
sine-cosine algorithm (SCA), AOA additionally has a very easy shape and occasional computation
complexity. The M and D operators inside the iterations can generate big steps mand d, specifically
within the exploration phase where the result is carried out. The declaration is as follows:
Wj (s+1)=Wq(s)/(MoP +EPS).((ub −lb)λ+lb),RAND <0.5
Wa (s).MoP .((ub −lb)λ+lb),RAND ≥0.5 (17)
wherein EPS is the smallest positive number, and λis a continuous coefficient that is cautiously
deliberate for this set of rules. During iterations, the MoP is decreased non-linearly from 1 to 0, and
the advent is as follows:
MoP =1−s
S1
β(18)
According to the AOA βis a continuous variable, which is set to 5 according to the AOA.

988 CMES, 2024, vol.139, no.1
From the above equation, Mand Doperators each have very consistent postures at the concept
of what the hunt agent can produce. Instead, sand aoperators had been used to prominence
neighborhood exploitation, which creates small steps within the search location. The numerical
notation is represented as:
Wj (s+1)=Wa (s)−MoP .((ub −lb)λ+lb),RAND <0.5
Wa (s).MoP .((ub −lb)λ+lb),RAND ≥0.5 (19)
For an optimization algorithm to explore and exploit, there may be no ambiguity approximately
the consequences of equilibrium. In AOA, the MOA parameter is used for the duration of iterations
to change exploration and exploitation, which is represented as:
MoA (s)=MINI +sMAXI −MINI
S(20)
where MINI and MAXI are constant values. According to Eq. (20), MOA increases from Min to
Max. Therefore, in the preliminary stage, the search agent operates in the seek area and has a greater
opportunity to explore even as the search agent is much more likely to act the search close by the
optimal position. In order to enhance the exploration and exploitation capability, a crossover strategy
is established. In the crossover operation, a hybrid offspring is created by combining two parent
solutions using the crossover operator, preserving the characteristics of both parent solutions in the
offspring [33]. The equation presented below describes the crossover utilized in the current work.
fr=eq
r
aq
r
if iq≤XV
otherwise (21)
HeretherateofcrossoverisXV . The evenly issued random number in the range [0,1] is denoted
as iq. The parent solutions used for crossing are erand ar. The value of 0.3 is fixed as the crossover
probability XV of the current work. By comparing the robustness of the present solution vector and
the updated solution vector, the selection of greedy determines whether the vector frwhich is updated,
exists or not in the upcoming generation. The following equation defines the selection operator:
ar,g+1=fr,g
ar,g
if
if
kfr,g≤kar,g
kfr,g>kar,g(22)
Here the objective function is denoted as k. For the challenge of minimization, this selection
process can be used. The solution’s populations in the flat search locations are steered by the inequality
≤implemented, and it prevents solutions from immobility. Thus, by employing crossover based MLP
algorithm, the medical data of various patients are protected from adversarial attack. Fig. 2 below
describes the developed hybrid algorithm.

CMES, 2024, vol.139, no.1 989
Figure 2: MLP based crossover AOA algorithm
4Results and Discussions
This paper presents the proposed CMLP method for accurately identifying the patient’s activities.
The method simulates various attacks in MATLAB using Python library and implements Keras and
Scikit-Learn library on the Google Collab platform to test the dataset. The algorithm and computation
was peformed on i7 processor with 16 Gb RAM, and 4 GB graphics card. Hyperparameter tuning was
conducted to optimize the CMLP model’s performance. This process involved systematic adjustments
to parameters such as learning rates, batch sizes, and the number of hidden layers to achieve the
best results. For the training-validation split, an 80–20 ratio was adopted, where 80% of the dataset
was used for training, and the remaining 20% was designated for validation. To further assess the
model’s robustness, a cross-validation procedure using k-fold cross-validation (with k =5) on both
the synthetic and UQVS datasets was conducted. This approach involved splitting the data into k
subsets (folds), training the model on k-1 folds, and validating it on the remaining fold. This process
was repeated k times, rotating the validation fold each time. The results from each iteration were then
averaged to provide a more robust estimate of the model’s performance. This method is assessed with
various metrics such as precision, accuracy, recall, and F1-score, and these results are compared with
existing methods such as DL-based CSI [17], Blockchain-based DL [22], and Mask-RCNN [15].

990 CMES, 2024, vol.139, no.1
4.1 Data Collection
This work collects data from the UQVS dataset, which consists of almost 16,000 data samples
[34]. The samples in the dataset are divided into training and testing in the ratio of 80:20. In data
analysis, this research utilized the MLP model to learn complex patterns and provide accurate
predictions. When implementing access control mechanisms, ECIES can be used to secure the
communication channel between the user and the healthcare data repository. ECIES is used to protect
the communication channel between the healthcare data repository and the user. This ensures that only
authenticated users with the corresponding private key can decrypt and access the data. This research
implements a two-factor authentication phase, which increases healthcare data security by MLP
model. For identifying the various activities of the patient, the proposed CMLP utilizes correlation
among several medical devices. Here, that correlation is utilized to execute the crafting decision tree
untargeted attacks for finding more important devices. The untargeted attack assumes and utilizes
the total training sample to generate adversarial examples. To change the state of the disease from a
stroke to an abnormal oxygen level, hemoglobin devices were established for observing the untargeted
attack. Likewise, the glucose device is influenced when modifying the disease state, as mentioned from
high cholesterol to stroke. An oxygen saturation device, heart rate, and glucose are more influenced
devices, and these devices perform the untargeted attack. Tabl e 1 depicts the parameters involved, its
final state, affected device as well as device count.
Table 1: Status of every affected device and device count
Current state Final state Affected device Device count
High blood pressure Stroke Glucose One
Stress Heart attack Blood oxygen One
Abnormal oxygen level Stroke Heart rate, glucose Two
High cholesterol Stroke Glucose One
Sleeping Drunk Alcohol One
Stroke Abnormal oxygen level Glucose, hemoglobin Two
4.2 Evaluation Measures
The evaluation in terms of precision, accuracy, recall, and F1-score is conducted to validate the
performance of the proposed CMLP model.
Accuracy: Accuracy is the most basic measurement used to evaluate the effectiveness of the model.
It is defined as the number of correct predictions to the total number of predictions. Accuracy measures
the overall correctness of the model’s predictions, providing a global assessment of its performance. In
healthcare data security and disease detection, high Accuracy indicates the model’s capability to make
correct predictions across various scenarios, contributing to reliable decision support.
Accuracy =TruePositive +TrueNegative
TruePositive +TrueNegative +FalsePositive +FalseNegative
(23)
Precision: Precision is the measurement used to analyze positive predictions. Precision quantifies
the model’s ability to correctly identify relevant instances among all instances it classifies as positive.
In healthcare data security, a high Precision signifies that the model’s positive predictions are highly
accurate, reducing the chances of false alarms and unnecessary alerts. It is defined as the ratio of true

CMES, 2024, vol.139, no.1 991
positives to the summation of true positives and false positives.
Precision =TruePositive
TruePositive +FalsePositive
(24)
Recall: Recall is the measurement used to analyze the number of true positive samples. Recall, also
known as Sensitivity or True Positive Rate, measures the model’s ability to correctly identify relevant
instances among all actual positive instances. In the context of healthcare data security, a high Recall
ensures that the model effectively detects adverse events, minimizing the risk of data breaches and
unauthorized access. It is defined as the ratio of true positives to the sum of false negatives and true
positives.
Recall =TruePositive
TruePositive +FalseNegative
(25)
F1-score: F1-score is the harmonic mean of precision and recall, and during the precision-recall
tradeoff, if the precision increase, recall decrease and vice versa. The F1-score strikes a balance between
Precision and Recall, offering a single metric that considers both false positives and false negatives. In
the healthcare domain, a high F1-score signifies a model’s proficiency in achieving accurate, reliable,
and balanced predictions, which is crucial for data security and disease detection scenarios.
F1score =2×(Precision ×Recall)
Precision +Recall
(26)
4.3 Performance Analysis
The performance of the developed algorithm is evaluated based on the above-mentioned measures.
Fig. 3a demonstrates the effects of device reduction in terms of the effectiveness of untargeted attacks.
The success rate of the proposed attack is dropped when the number of devices is decreased. To
remove 2 devices (glucose, blood oxygen) and 1 device, the proposed attains the greatest success rate
of 14.60% than the existing methods. The proposed method attains the greatest success rate of 16.45%
in removing three devices: oxygen, glucose, and heart rate. To eliminate one, two, and three devices,
as can be seen in Fig. 3b. By decreasing the devices at SHE, the effectiveness of adversarial attacks
decreased in short.
Figure 3: Analysis based on (a) attacker success rate (b) accuracy drop rate

992 CMES, 2024, vol.139, no.1
4.3.1 Assessment with Various Attack Algorithms
Here, in CMLP, two attacks are executed such as a black box and white box attack, to assess the
proposed model’s effectiveness by several algorithms. Table 2 shows the effectiveness of the various
methods with the proposed method. In Tabl e 2, the proposed method has a 32.30% for success rate
and a 15.70% for accuracy drop in both targeted attack and targeted attack. The existing Mask-RCNN
method attained an 8.32 accuracy drop lower than the proposed method as well as it attained a success
rate of 9.26. The proposed method achieved a success rate of 8.22 and a 12.29 accuracy drop in the
black box.
Table 2: Performance analysis
Methods Accuracy drop Success rate Actual accuracy
Proposed 32.66 8.5 97.56
DL-based CSI 20.44 7.77 88.66
Blockchain-based DL 24.18 8.65 89.21
Mask-RCNN 8.32 7.99 87.32
The time complexity of the proposed method is analyzed with the existing methods. Fig. 4a
demonstrates the complexity analysis of the proposed method. The proposed method obtains low
complexity in the synthetic data. It has low complexity in 10 sensors with 1600000 clauses. The
proposed method achieves low complexity in the clauses. Fig. 4b shows the complexity of using
UQVS data. In the UQVS data, when compared to existing techniques, the proposed method has
low complexity in 10 sensors with 200000 clauses. Hence, these methods are utilized across various
domains which will be affected by adversarial attacks.
Figure 4: Complexity analysis for (a) synthetic data (b) UQVS data
The performance is evaluated by comparing the proposed CMLP method with the existing DL-
based CSI, Blockchain-based DL, and Mask-RCNN. Fig. 5a shows the accuracy analysis of the
proposed method. The proposed method and existing methods are compared in terms of detecting
activities. The proposed method achieves a higher accuracy of 97% than the other methods. The DL-
based CSI attains an accuracy of 94%, 85% for the Blockchain-based DL method, and Mask RCNN

CMES, 2024, vol.139, no.1 993
reaches an accuracy of 91%. The graphical representation of the precision analysis is illustrated in
Fig. 5b. From the figure, it can be see seen the proposed method attains a precision of 93% higher
than the existing methods.
Figure 5: Comparative analysis for (a) accuracy and (b) precision
Fig. 6a demonstrates the recall analysis with the comparison of the proposed method and other
methods. When comparing the existing methods with the proposed method, the proposed method
outperformed the existing methods. It achieves a recall of 92% more than the other methods. The
DL-based CSI method attains a recall of 90%, the Blockchain-based DL method achieves a recall of
86%, and the mask RCNN method has 85%. Fig. 6b illustrates the F1-score analysis for the proposed
method. The proposed method attains an F1-score of 92% in the comparison of existing methods.
The methods get an F1-score of 90% for DL-based CSI, .80% for Blockchain-based DL, and 85% for
mask-RCNN.
Figure 6: Comparative results based on (a) recall and (b) F1-score
The achieved accuracy, precision, recall, and F1-score bear significant practical implications in
the realm of healthcare data security and disease detection. The high precision indicates a low rate

994 CMES, 2024, vol.139, no.1
of false alarms, ensuring that healthcare professionals can confidently act on model alerts without
being inundated with erroneous notifications. The equally high recall signifies the model’s ability to
effectively detect potential issues, minimizing the risk of missed critical events. The impressive F1-score
balances precision and recall and demonstrates the model’s proficiency in maintaining accuracy while
providing comprehensive coverage.
In practical terms, this translates to a more secure environment for healthcare data, where there
is a substantially lower risk of data breaches and illegal access. It simultaneously improves disease
detection accuracy, enabling prompt intervention and accurate patient management. These metrics
demonstrate the value and relevance of this study in enhancing the dependability and security of smart
healthcare systems.
To provide a more comprehensive comparison, a thorough assessment of the proposed CMLP
model was made against three existing methods: DL based CSI, Blockchain-based DL, and Mask-
RCNN. This detailed evaluation sheds light on the CMLP model’s unique advantages in healthcare
data security and disease detection. While DL-based CSI exhibits competence in certain predictive
tasks, it may be vulnerable to adversarial attacks and often lacks transparency. The CMLP model
excels by integrating a crossover-based mechanism that enhances both security and interpretability.
Blockchain-based DL offers data security but can introduce computational inefficiencies and scalabil-
ity challenges. The CMLP model, through its feature fusion approach, manages to provide robustness
against adversarial attacks without compromising computational efficiency. Mask-RCNN, designed
for image segmentation, may not seamlessly translate to healthcare data security and disease detection,
particularly with non-image data. The CMLP model’s adaptability to diverse data types and its ability
to effectively handle adversarial attacks position it as a more versatile solution in the healthcare
domain.
CMLP model addresses adversarial attacks by selectively fusing features from different layers, pri-
oritizing reliable information while attenuating noisy or adversarial signals. Its adaptability to diverse
healthcare data types and its capability to maintain robustness without sacrificing computational
efficiency sets it apart from other approaches.
5Conclusions
Machine learning methods are used to analyze large amounts of patient data in the healthcare
industry. However, these models face several challenges due to their instability in disease prediction. To
address this drawback, this research proposed a novel adversarial CMLP-based attack. This method
used the UQVS dataset and some performance evaluation measures such as accuracy, precision,
recall, and F1-score. The performance of the proposed CMLP model is validated by comparing it
with existing methods such as DL-based CSI, Blockchain-based DL, and Mask-RCNN. The model
achieved an accuracy of 97%, a precision of 93%, a recall of 92%, and an F1 score of 92%, respectively.
The experimental result revealed that the proposed CMLP model performs better than the existing
methods.
While the proposed CMLP model presents several advantages, it is essential to acknowledge its
limitations and identify avenues for future research. One potential limitation lies in its susceptibility to
highly sophisticated adversarial attacks that may exploit intricate data correlations. Further research
into advanced adversarial defense mechanisms could enhance the model’s resilience. Additionally,
while our model demonstrates robustness in offline experiments, deploying it in real-time automated
disease detection systems poses practical challenges. Considerations such as data privacy, real-time
processing constraints, and scalability need to be carefully addressed. Strategies for secure and efficient

CMES, 2024, vol.139, no.1 995
data transmission and processing warrant exploration, especially in cloud-based healthcare systems.
Its effective application in real-world healthcare settings will depend on recognizing its limitations and
resolving deployment issues that arise in the real world, paving the way for a safer and more dependable
healthcare ecosystem.
In the future, this proposed CMLP model will be applied in a real-time automated disease
detection system. Future research could also focus on enhancing the model’s interpretability further,
making it more accessible to healthcare professionals. Incorporating domain-specific knowledge or
designing user-friendly interfaces could facilitate seamless integration into clinical workflows.
Acknowledgement: The authors extend their appreciation to King Saud University for funding
this work through Researchers Supporting Project Number (RSP2024R499), King Saud University,
Riyadh, Saudi Arabia.
Funding Statement: This research was funded by King Saud University through Researchers Support-
ing Program Number (RSP2024R499).
Author Contributions: The authors confirm contribution to the paper as follows: study conception
and design: M.H.A., H.A., M.K.A.; methodology: M.H.A., H.A.; data collection: M.H.A., M.K.A.;
analysis and interpretation of results: M.H.A., H.A.; draft manuscript preparation: M.H.A., H.A.,
M.K.A. All authors reviewed the results and approved the final version of the manuscript.
Availability of Data and Materials: All the databases and their reference source are mentioned in the
paper.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
References
1. Islam, M. M., Rahaman, A., Islam, M. R. (2020). Development of smart healthcare monitoring system in
IoT environment. SN Computer Science, 1(3), 185.
2. Yahya, Z., Hassan, M., Younis, S., Shafique, M. (2020). Probabilistic analysis of targeted attacks using
transform-domain adversarial examples. IEEE Access, 8, 33855–33869.
3. Selvakkumar, A., Pal, S., Jadidi, Z. (2022). Addressing adversarial machine learning attacks in smart
healthcare perspectives, pp. 269–282. Cham: Sensing Technology.
4. Maqsood, M., Yasmin, S., Gillani, S., Aadil, F., Mehmood, I. et al. (2023). An autonomous decision-
making framework for gait recognition systems against adversarial attack using reinforcement learning.
ISA Transactions, 132, 80–93.
5. Anthi, E., Williams, L., Javed, A., Burnap, P. (2021). Hardening machine learning denial of service (DoS)
defences against adversarial attacks in IoT smart home networks. Computers & Security, 108, 102352.
6. Abidi, M. H., Alkhalefah, H., Mohammed, M. K. (2022). Mutated leader sine-cosine algorithm for secure
smart IoT-blockchain of Industry 4.0. Computers, Materials & Continua, 73(3), 5367–5383. https://doi.org/
10.32604/cmc.2022.030018
7. Abidi, M. H., Mohammed, M. K., Alkhalefah, H. (2022). Predictive maintenance planning for Industry
4.0 using machine learning for sustainable manufacturing. Sustainability, 14.https://doi.org/10.3390/
su14063387

996 CMES, 2024, vol.139, no.1
8. Alkahtani, M., Abidi, M. H., Obaid, H. S. B., Alotaik, O. (2023). Modified gannet optimization algorithm
for reducing system operation cost in engine parts industry with pooling management and transport
optimization. Sustainability, 15(18), 13815.
9. Ali, F., El-Sappagh, S., Islam, S. M. R., Kwak, D., Ali, A. et al. (2020). A smart healthcare monitoring
system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion,
63, 208–222.
10. Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C. et al. (2020). HealthFog: An ensemble deep
learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog
computing environments. Future Generation Computer Systems, 104, 187–200.
11. Finlayson, S. G., Bowers, J. D., Ito, J., Zittrain, J. L., Beam, A. L. et al. (2019). Adversarial attacks on
medical machine learning. Science, 363(6433), 1287–1289.
12. Kebande, V. R., Alawadi, S., Awaysheh, F. M., Persson, J. A. (2021). Active machine learning adversarial
attack detection in the user feedback process. IEEE Access, 9, 36908–36923.
13. Hao, J., Tao, Y. (2022). Adversarial attacks on deep learning models in smart grids. Energy Reports, 8,
123–129.
14. Newaz, A. I., Haque, N. I., Sikder, A. K., Rahman, M. A., Uluagac, A. S. (2020). Adversarial attacks to
machine learning-based smart healthcare systems. GLOBECOM 2020—2020 IEEE Global Communications
Conference, pp. 1–6. Taipei, Taiwan.
15. Ahmed, I., Jeon, G., Piccialli, F. (2021). A deep-learning-based smart healthcare system for patient’s
discomfort detection at the edge of internet of things. IEEE Internet of Things Journal, 8(13), 10318–10326.
16. Raina, R., Jha, R. K. (2022). Intelligent and interactive healthcare system (I2HS) using machine learning.
IEEE Access, 10, 116402–116424.
17. Liu, Q., Guo, J., Wen, C. K., Jin, S. (2020). Adversarial attack on DL-based massive MIMO CSI feedback.
Journal of Communications and Networks, 22(3), 230–235.
18. Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y. et al. (2021). Understanding adversarial attacks on deep learning
based medical image analysis systems. Pattern Recognition, 110, 107332.
19. Javadpour, A., Abharian, S. K., Wang, G. (2017). Feature selection and intrusion detection in cloud
environment based on machine learning algorithms. 2017 IEEE International Symposium on Parallel and
Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing
and Communications (ISPA/IUCC), pp. 1417–1421. Guangzhou, China.
20. Javadpour, A., Pinto, P., Ja’fari, F., Zhang, W. (2023). DMAIDPS: A distributed multi-agent intrusion
detection and prevention system for cloud IoT environments. Cluster Computing, 26(1), 367–384.
21. Abidi, M. H., Alkhalefah, H., Umer, U., Mohammed, M. K. (2021). Blockchain-based secure information
sharing for supply chain management: Optimization assisted data sanitization process. International Journal
of Intelligent Systems, 36(1), 260–290.
22. Bhattacharya, P., Tanwar, S., Bodkhe, U., Tyagi, S., Kumar, N. (2021). BinDaaS: Blockchain-based deep-
learning as-a-service in Healthcare 4.0 applications. IEEE Transactions on Network Science and Engineering,
8(2), 1242–1255.
23. Hady, A. A., Ghubaish, A., Salman, T., Unal, D., Jain, R. (2020). Intrusion detection system for healthcare
systems using medical and network data: A comparison study. IEEE Access, 8, 106576–106584.
24. Akshay Kumaar, M., Samiayya, D., Vincent, P. M. D. R., Srinivasan, K., Chang, C. Y. et al. (2022). A hybrid
framework for intrusion detection in healthcare systems using deep learning. Frontiers in Public Health, 9.
25. Akram, F., Liu, D., Zhao, P., Kryvinska, N., Abbas, S. et al. (2021). Trustworthy intrusion detection in
E-healthcare systems. Front Public Health, 9, 1–10.
26. Jeyanthi, D. V., Indrani, B. (2023). IoT-based intrusion detection system for healthcare using RNNBiLSTM
deep learning strategy with custom features. Soft Computing, 27(16), 11915–11930.

CMES, 2024, vol.139, no.1 997
27. Iwendi, C., Anajemba, J. H., Biamba, C., Ngabo, D. (2021). Security of Things intrusion detection system
for smart healthcare. Electronics, 10.https://doi.org/10.3390/electronics10121375
28. Si-Ahmed, A., Al-Garadi, M. A., Boustia, N. (2023). Survey of machine learning based intrusion detection
methods for Internet of Medical Things. Applied Soft Computing, 140, 110227.
29. Firat Kilincer, I., Ertam, F., Sengur, A., Tan, R. S., Rajendra Acharya, U. (2023). Automated detection
of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron
optimization. Biocybernetics and Biomedical Engineering, 43(1), 30–41.
30. Savanovi´
c, N., Toskovic, A., Petrovic, A., Zivkovic, M., Damaˇ
seviˇ
cius, R. et al. (2023). Intrusion detection
in Healthcare 4.0 Internet of Things systems via metaheuristics optimized machine learning. Sustainability,
15(16), 12563.
31. Almalawi, A., Khan, A. I., Alsolami, F., Abushark, Y. B., Alfakeeh, A. S. (2023). Managing security of
healthcare data for a modern healthcare system. Sensors, 23(7), 3612.
32. Zheng, R., Jia, H., Abualigah, L., Liu, Q., Wang, S. (2021). Deep ensemble of slime mold algorithm and
arithmetic optimization algorithm for global optimization. Processes, 9.https://doi.org/10.3390/pr9101774
33. Gupta, S., Deep, K. (2019). Improved sine cosine algorithm with crossover scheme for global optimization.
Knowledge-Based Systems, 165, 374–406.
34. Imtiazul Haque, N., Ashiqur Rahman, M., Hasan Shahriar, M., Ataur Khalil, A., Uluagac, S. (2021). A
novel framework for threat analysis of machine learning-based smart healthcare systems. https://doi.org/
10.48550/arXiv.2103.03472