=Paper=
{{Paper
|id=Vol-3706/Paper18
|storemode=property
|title=Malware Detection in IoT Enabled Multimedia Environment Using the Feline Cad-based Deep CNN
|pdfUrl=https://ceur-ws.org/Vol-3706/Paper18.pdf
|volume=Vol-3706
|authors=Seelam Sai Satyanarayana Reddy,Harikrishna Bommala
|dblpUrl=https://dblp.org/rec/conf/icaids/ReddyB23
}}
==Malware Detection in IoT Enabled Multimedia Environment Using the Feline Cad-based Deep CNN==
https://ceur-ws.org/Vol-3706/Paper18.pdf
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
233
most challenging task in the network. Public surveillance systems [4] are common applications
to prevent from malware attacks. Hence, malware detection is significant for the IoT multimedia
device for secure communication among the devices [1]. Malware detection can be done through
signature-based methods, visualization ideas, and machine learning-based techniques for the
detection and mitigation of malware from the network [5]. The machine learning techniques
transform the binary code to image data and visualize the result for various samples and the
speed of detection can also be enhanced through machine learning. The malware identification
and mitigation are easier and simple while considering the traditional method of the malware
detection technique [6]. Machine learning methods [7, 8] are used to identify and predict the
network attacks. The non-destructive malware detection method detects the malware without
any feature selection strategy. In addition, the optimization strategy of machine learning further
enhances the accuracy of detection. Besides, machine learning offers reduced computation
complexity with minimal processing time along with robust performance [9]. The research
aims to devise an efficient malware detection technique based on the multimedia-enabled IoT
scenario. For safe interaction among the devices, malware needs to be detected and prevented.
Thus, an automatic malware detection technique is required with a more accurate detection rate.
Here, a novel optimization-based machine learning is introduced for malware detection in the
multimedia-enabled IoT environment. For this Feline cad optimization algorithm is proposed by
integrating the foraging behavior of the birdie and the hounding behavior of the feline, which
is used to train the Deep convolutional neural network (Deep CNN) that enhances the detection
accuracy through the fast convergence rate and global best solution. The major contribution of
the research is:
• Proposed Feline cad optimization: The proposed feline cad optimization is designed by
integrating the foraging behavior of the birdie with the hounding behavior of the feline
to obtain the global best optimal solution for tuning the weights of the Deep CNN for the
malware detection.
• Proposed Cat creep based Profound CNN for malware recognition: The malware location
in the mixed media empowered IoT climate is finished utilizing the Profound CNN, which
is prepared utilizing the proposed Cat scoundrel streamlining. Here, the loads of the
classifier are tuned to get a more exact identification through quick union rate by staying
away from the catching at neighborhood optima.
The remaining section of the research is: Section 2 details the literature review along with
the challenges faced by the existing system. Section 3 elaborates the system model of the
proposed method and the result and discussion is presented in Section 4. Finally, the conclusion
is provided in section 5.
2. Motivation
This section details the review of the conventional literature regarding the multimedia-enabled
IoT-based malware detection techniques. The challenges faced by the existing system motivate
the author to devise a novel malware detection technique based on machine learning.
224
2.1. Literature Review
The conventional methods of malware detection based on multimedia-enabled IoT is detailed in
this section. The multimedia-enabled IoT-based attack detection was utilized by [2] using the
adaptive hybrid strategy that uses the timed automata controller technique. Here, the detection
of the malware was done through the timed automaton with self-tuning, in which the pattern
set for malware was created based on the signature by the crowd-sourcing online repository.
Besides, the knowledge regarding the multimedia file format was utilized for the analysis of
packets that carries the multimedia files and obtained enhanced classification accuracy. A
machine learning-based malware detection was employed in [6] for the IoT environment to
avoid the threat. In this strategy, both the unknown and known malware was detected in a very
fast manner. Besides, the feature extraction and selection were performed before the detection
of malware. Minimal error along with better accuracy was obtained by the method utilized.
Dynamic differential game-based cloud-assisted malware detection was utilized by [5] to provide
a secure network. Here, the secure data sharing was done using the support vector machine
(SVM) and the cost function is minimized through the optimal defense strategy. The utility was
evaluated to show the performance enhancement of the malware detection technique. IoT-based
malware detection based on the mobile multimedia application was used by [1] through the
machine learning approach. Here, the features based on the permission were extracted and
were utilized to train the classifier to enhance the classification accuracy. The feature selection
was done using the random forest regressor to minimize the number of features for further
processing. The accuracy was evaluated to show the performance enhancement. The IoT-based
malware detection was employed by [10] based on the intelligent behavior, in which the rule
based and learning-based features were selected for the detection of the unknown malware.
Here, the elevated accuracy and minimal FNR and FPR were the achievements obtained by the
introduced method.
2.2. Challenges
The difficulties looked by the customary media based IoT malware discovery is point by point
in this segment.
• The computation complexity of the network is not evaluated and the delays such as state
transition and response are neglected [2].
• The machine learning technique for malware detection obtained better accuracy but
failed to incorporate the optimization approach that enhances the detection accuracy
further [1, 6].
• With the usage of Nash equilibrium criteria, the computation deviation happens, which
is considered a non-negligible deviation [5].
• The malware detection technique developed by [10] utilizes an elevated number of features
and is not suitable for the detection of new malware.
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3. Proposed Methodology
The interaction of device to device in the virtual and physical environment through the collection
of protocols, interfaces, and multimedia-related information constitutes the multimedia-enabled
IoT. During the communication process, the malware attack in such a network is unavoidable.
Hence there is a need for an automatic malware detection technique. Here, an efficient technique
named, Feline cad-based Deep CNN is proposed for the detection of malware in the multimedia-
enabled IoT network. Initially, the information from the multimedia-enabled IoT devices is
gathered and then pre-processed to extract the essential 27 attributes. Here, the first five
attributes such as SHA1 hashes and executable MD5 unique attributes along with three textual
attributes such as dynamic compilers, functions, and libraries are extracted. In addition, the
remaining 22 numerical attributes such as entropy, time data stamp, uninitialized data size,
header’s optional size, initialized data’s size, image and code size, size of the pointer to the
symbol table, type of PE, total symbols, total section number, size and virtual address number,
magic, machine, the base of the image, alignment of the file, characteristics of dynamic link
library, database, and codebase are also extracted. From the extracted 27 attributes, the malware
detection is employed using the proposed Feline cad-based Deep CNN, in which the classifier
named Profound CNN is prepared utilizing the proposed Cat creep advancement calculation to
improve the exactness of recognition. The illustration of the proposed Feline cad-based Deep
CNN for malware detection is shown in Fig. 1.
3.1. Data Pre-processing
The data acquisition for the proposed malware detection technique is taken from the Brazilian-
malware-dataset [8], in which 22 attributes numeral attributes, 2 unique attributes, and 3 textual
attributes are utilized for the malware detection. Initially, the null data needs to be checked
and it should be removed if available. Then, the above mentioned 27 significant attributes are
extracted and the redundant information is removed for the reduction of the computational
complexity.
3.2. Proposed Feline cad based Deep CNN for malware detection
The malware detection in the multimedia-enabled IoT environment is done using the proposed
Feline cad optimization based on Deep CNN. Here the malware detection is done using the
extracted 27 attributes from the IoT network which is fed as input to the Deep CNN. The
Profound CNN is prepared utilizing the proposed Cat lowlife enhancement calculation to
expand the precision of identification.
3.2.1. System model of Deep CNN
A deep convolutional neural network (Deep CNN) [11] is widely used in visual imaginary-based
applications due to its efficiency, accuracy, and reliability. Besides, Deep CNN is a feed-forward
category network, in which the feedback cannot be fed in itself. The Deep CNN comprises
several layers such as a fully connected layer, pooling layer, and convolutional layer. The
functioning of each layer is detailed below and is illustrated in Fig. 2.
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Figure 1: System model of proposed Feline cad based Deep CNN for malware detection
Figure 2: Architecture of Deep CNN
Convolutional Layer: The input attributes are fed into the convolutional layer, in which
several patterns are generated, and it performs the convolutional operation with the filters
and forms the feature maps. Here, the input is categorized into several windows and these
categorized inputs are convolved with the filters based on certain weights. The output obtained
by the convolutional neural network is formulated as,
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𝑐1(𝑇 −1)
(𝑇 −1)
𝑋𝑥𝑇 = 𝐸𝑥𝑇 + ∑ 𝐿𝑥,𝑦 𝑇 × 𝑃𝑥 (1)
𝑦=1
where, 𝐿 refers to the size of the kernel, the 𝑥 th feature map with the layer 𝑇 is notated as
𝑋𝑥𝑇 , the bias is represented as 𝐸, and the weight is denoted as 𝑃.
Pooling Layer: The down-sampling of the spatial dimension is performed in this layer to avoid
overfitting issues. The output obtained in this layer is invariant to distortions and translational
shifts.
Fully connected layer: The output produced at this layer is nonlinear. Here, the activation
function named Rectified Linear Unit (ReLU) is used to avoid the issues related to the vanishing
gradient problem. The output of the fully connected layer is formulated as,
𝑐1(𝑇 −1) 2(𝑇 −1) 𝑐3(𝑇 −1)
𝑋𝑥𝑇 = 𝑓 (𝑑𝑥𝑇 ) with 𝑑𝑥𝑇 = ∑ ∑ 𝐼
∑ 𝑃𝑥,𝑦,𝑝,𝑞 (𝑋𝑦𝑇 −1 )𝑝,𝑞 (2)
𝑦=1 𝑝=1 𝑞=1
𝐼
where, the weights of the location (𝑝, 𝑞) is referred as 𝑃𝑥,𝑦,𝑝,𝑞 and 𝑐1(𝑇 − 1, 𝑐2(𝑇 − 1), and
𝐼
𝑐3(𝑇 − 1) refers to the feature maps 𝑊 𝑒𝑖𝑟,𝑠,𝑥,𝑦 refers to the weights of the location. The loads of
the classifier are tuned utilizing the proposed Cat Miscreant Advancement calculation to limit
the preparation misfortune and to raise the location precision.
3.2.2. Proposed Feline Cad Optimization algorithm
The proposed feline cad optimization is designed by integrating the foraging behavior of the
birdie [12] with the hounding behavior of the feline [13] to enhance the searching capability
and for the attainment of a more accurate global best optimal solution. Thus, more accurate
malware detection is possible through the proposed method. a) Motivation Birdie is a bird with
strong memory and intelligence and is mostly a residential bird. Cadger and the producer are
the two different categories of the birdie in the food search. Here, the cadger obtains food from
the producer and the producer obtains its food in the searching process. The low energies birdie
utilizes the cadging behavior than the producer. In addition, the feline is a hunting animal with
deliberate and hounding characteristics. The hounding characteristic of the feline to hunt the
target is high and clear. Thus, in the proposed optimization the foraging behavior of the birdie
is incorporated with the hounding behavior of the feline to enhance the convergence rate. b)
Mathematical modeling For the mathematical modeling of the proposed feline cad optimization,
the fol-lowing rules are considered. Rule 1: The producers of birdie are high-energy members
and they need to direct all the cadger in the group in search of food. The energy reserves depend
on the fitness value of the birdie. Rule 2: An alarming signal is generated by the birdie when
they detect a predator. The producer birdie needs to lead the entire cadger in the group to a
safe location when the alarming sound exceeds the threshold level. Rule 3: The proportion of
both the cadger and the producer remains the same in the population, but the birdie with better
food searcher can become the producer of the food source. Rule 4: The producers have more
energy levels and the cadgers are in search of food when they are in a starving condition. Rule
5: The cadgers follow the producers in search of the food and they constantly monitor them for
228
enhancing the level of predation. Rule 6: When the birdies in the edge face the danger then
they move randomly to-ward the neighbor in the safe area.
Initialization: The birdies in the population can be initialized as,
𝐶 𝐶1,2 ⋯ ⋯ 𝐶1,𝑠
⎡ 1,1 ⎤
⎢ 𝐶 𝐶2,2 ⋯ ⋯ 𝐶2,𝑠 ⎥
𝐶 = ⎢ 2,1 (3)
⎢ ⋮ ⋮ ⋮ ⋮ ⋮ ⎥ ⎥
⎣ 𝐶𝑟,1 𝐶𝑟,2 ⋯ ⋯ 𝐶𝑟,𝑠 ⎦
where, 𝑠 refers to the dimension of the solution and 𝑟 refers to the birdie. The fitness of the
birdie in the population is formulated as,
𝑓 ([𝐶1,1 𝐶1,2 ⋯ ⋯ 𝐶1,𝑠 ])
⎡ ⎤
⎢ 𝑓 ([𝐶2,1 𝐶2,2 ⋯ ⋯ 𝐶2,𝑠 ]) ⎥
𝐴𝐶 = ⎢ ⎥ (4)
⎢ ⋮ ⋮ ⋮ ⋮ ⋮ ⎥
⎣ 𝑓 ([𝐶𝑟,1 𝐶𝑟,2 ⋯ ⋯ 𝐶𝑟,𝑠 ]) ⎦
Here, the fitness of the unique birdie is represented in a row. The producers have the highest
fitness and have the priority in food search and are responsible for guiding all the members.
Thus, the producers search more areas compared to the cadger. Here, the search capability of
the producer is enhanced by incorporating the hounding capability of the feline in search of
food. Then, the position updation of the producer depends on rule 1 and 2 and is formulated as,
𝜏 ⋅ exp ( −𝑝 )
𝐶𝑝,𝑞 if Rand 2 < 𝐷
𝜏 +1
𝐶𝑝,𝑞 = { 𝛽⋅𝑖max (5)
𝐶𝑝,𝑞 + 𝐸𝐹 if Rand 2 ≥ 𝐷
where, 𝜏 refers to the iteration, the 𝑝 th birdie in 𝑞 th dimension is referred as 𝐶𝑝,𝑞 . The
random number is represented as 𝛽 ∈ [0, 1], the alarm value is notated as Rand 2 ∈ [0, 1] and
the safety threshold is referred as 𝐷 ∈ [0, 5, 1]. The random number that satisfies the normal
distribution is notated as 𝐸 and the matrix of size [1 × 𝑠] is denoted as 𝐹. The producers search
the food when there are no predators found, which satisfies the condition Rand 2 < 𝐷. The
birdies fly to the safe location when they found the predator, the condition for it is Rand 2 ≥ 𝐷.
As per rule 5 cadgers search and monitors the producers and when they found the food
immediately move to the food-rich location and compete with the producer. If they win, then
the position updation of the cadger is formulated as,
𝜏 𝜏
𝐶worst −𝐶𝑝,𝑞
𝜏 +1 = {𝐸 ⋅ exp ( 𝛽⋅𝑖max
) if 𝑝 > 𝑟/2
𝐶𝑝,𝑞 (6)
𝜏 +1 𝜏 𝜏 +1
𝐶𝐺 + |𝐶𝑝,𝑞 − 𝐶𝐺 | ⋅ 𝐻 + ⋅ 𝐹 otherwise
where, the optimal position of the producer is referred as 𝐶𝐺 , the worst solution is denoted
𝜏 −1
as 𝐶worst , and 𝐻 + = 𝐻 𝑇 (𝐻 𝐻 𝑇 ) . The birdie with the lowest fitness is considered as starving
and the condition for it is 𝑝 > 𝑟/2.
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Let us consider 10% to 20% of the birdie are aware of the danger and according to rule 6 , the
position updation is formulated as,
𝜏
𝐶best 𝜏 − 𝐶𝜏
+ 𝛼 |𝐶𝑝,𝑞 if 𝑎𝑝 > 𝑎𝑣
best |
𝜏 +1 = {
𝐶𝑝,𝑞 𝜏 −𝐶 𝜏 (7)
𝜏 + 𝐼( ∣𝐶 𝑝,𝑞 worst
𝐶𝑝,𝑞 ) if 𝑎𝑝 = 𝑎𝑣
(𝑎𝑝 −𝑎𝑢 )+𝛾
where, the constant included to minimize the zero-divisionerror is represented as 𝛾, the
𝜏
control parameter for the step size is referred as 𝛼, the best solution is referred as 𝐶best , and
the random number is represented as 𝐼 , and ranges between [−1, 1]. Here, the current birdie’s
present fitness is represented as 𝑎𝑝 and the worst and the best fitness of the birdie are notated as
𝑎𝑢 and 𝑎𝑣 respectively. The condition 𝑎𝑝 > 𝑎𝑣 indicates that the birdie is at the edge and 𝑎𝑝 = 𝑎𝑣
depicts the danger and hence moves towards the other members of the group.
The position updation based on the hounding capability of the feline in search of food is
formulated as,
𝜏 +1 = 𝐶
𝐶𝑝,𝑞 𝑝,𝑞 + 𝑉𝑘,𝑑 (8)
where, the velocity of searching for food by the feline at 𝑝 th feline in 𝑞 th dimension.
As per the rule utilized in [14] utilized for the integration of the characteristic behavior of
two algorithms by combining equation (7) and equation (8) and is expressed as,
𝜏 + 𝛼 𝜏 − 𝐶𝜏
𝑌𝑙′ = 0.5 [𝐶𝑏𝑒𝑠𝑡 𝜏
|𝐶𝑝,𝑞 𝑏𝑒𝑠𝑡 |] + 0.5 [𝐶𝑝,𝑞 + 𝑉𝑘,𝑑 ] (9)
The pseudo-code for the proposed Feline cad optimization algorithm is presented in Fig. 3
given below.
Figure 3: Pseudo-code for the proposed Feline cad optimization algorithm
Here, the global best solution obtained is utilized for tuning the weights of the Deep CNN
optimally. The proposed calculation has the capacity to get the worldwide arrangement with
quick assembly by staying away from the catching at neighborhood optima utilizing the reason-
able investigation and abuse stage. The incorporation of the hounding behavior of the Feline
230
helps the birdie to escape from the attack leading to the maintenance of the population and
helps to explore more regions that assist to obtain the global best solution. Thus, the optimal
tuning using the proposed optimization algorithm helps to detect the malware in the network
more accurately and hence the preventive mechanism can be employed to avoid the damage.
4. Result and Discussion
The result evaluated by the proposed Feline cad-based DeepCNN is detailed in this section
based on the performance metrics such as accuracy, sensitivity, and specificity.
4.1. Experimental Setup
For the evaluation of the proposed malware detection technique, MATLAB is uti-lized with an
Intel i3 core processor, 2GB RAM, and Windows 10 OS.
4.2. Dataset description
The Brazilian-malware-dataset [15] comprises of 80GB malicious binary samples with 23,033
unique samples with a total of 29,704 samples. In addition, each sample consists of 27 attributes,
in which 3 textual, two unique, and 22 numerical attributes.
4.3. Comparative Methods
The analysis of the proposed method is compared with the traditional malware de-tection
techniques such as Neural Network [16], Deep CNN [11], PSO- based DeepCNN [17],CSO-
based DeepCNN [13] and Sparrow search based DeepCNN [12].
4.4. Performance metrics
The efficiency of the developed Feline cad-based DeepCNN is evaluated in terms of accuracy,
sensitivity and specificity.
Accuracy (A): The measure of the closeness of the target obtained by the proposed method is
measured in terms of accuracy and is formulated as,
where, Pos𝑡 represent true positive, Pos𝑓 indicate false positive, 𝑁 𝑒𝑔 𝑡 indicate true negative,
and Neg𝑓 represent false negative.
Sensitivity (S1): The ratio of a positive which correctly detected by the proposed malware
detection technique and is expressed as,
Pos 𝑡
𝑆1 = (10)
Pos 𝑡 + 𝑁 𝑒𝑔
Specificity (𝑆2 ∶) :The ratio of negatives that are correctly detected by the proposed malware
detection technique and is represented by,
𝑁 𝑒𝑔 𝑡
𝑆2 = (11)
𝑁 𝑒𝑔 𝑡 + Pos 𝑓
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Table 1
Comparison based on Statistical Analysis
Metrics/ Methods NN DeepCNN PSO based DeepCNN CSO based DeepCNN Sparrow search based DeepCNN Feline cad based DeepCNN
Accuracy ( Best 74.56 85.23 82.54 83.26 85.26 99.95
Mean 74.56 85.23 82.54 83.26 85.26 99.95
Variance 0.003 0.0031 0.0029 0.0025 0.003 0.0019
Sensitivity ( Best 78.51 82.17 86.51 86.97 89.25 97.45
Mean 78.51 82.17 86.51 86.97 89.25 97.45
Variance 0.0029 0.0027 0.003 0.0032 0.0021 0.0018
Specificity ( Best 79.54 82.56 85.65 87.23 91.15 96.12
Mean 79.54 82.56 85.65 87.23 91.15 96.12
Variance 0.0032 0.0027 0.0035 0.0032 0.0028 0.002
4.5. Comparative Analysis
Table 1 depicts the comparative analysis of the proposed Feline cad optimization based DeepCNN
with the conventional methods such as NN, Deep CNN, PSO- based DeepCNN, and Sparrow
search based DeepCNN. The proposed method obtained the maximal accuracy of 99.95%, which
is 25.40%, 14.73%, 17.42%, 16.70%, and 14.70% better than the existing NN, Deep CNN, PSO-
based DeepCNN, and Sparrow search based DeepCNN. Likewise, the sensitivity acquired by
the proposed method is 97.45%, which is 19.44%, 15.68%, 11.23%, 10.75%, and 8.41% better than
the existing NN, Deep CNN, PSO- based DeepCNN, and Sparrow search based DeepCNN. The
maximal specificity acquired by the proposed method is 96.12%, which is 17.25%, 14.11%, 10.89%,
9.25%, and 5.17% better than the existing NN, Deep CNN, PSO- based DeepCNN, and Sparrow
search based DeepCNN.
5. Conclusion
This research proposed a malware detection technique for the multimedia enabled IoT environ-
ment. Here, the data gathered from the IoT environment is extracted based on 27 attributes
for the reduction of the computational complexity and is fed to the input for the detection of
the malware using the DeepCNN, which is trained using the proposed Feline cad optimization
approach that is designed by integrating the forag-ing behavior of the birdie with the hounding
behavior of the feline to obtain the glob-al best optimal solution for tuning the weights. The
proposed method obtained the maximal accuracy, sensitivity, and specificity and obtained the
values of 99.95%, 97.45%, and 96.12% respectively.
References
[1] R. Taheri, R. Javidan, Z. Pooranian, Adversarial android malware detection for mobile mul-
timedia applications in IoT environments, Multimed. Tools Appl. 80 (2021) 16713–16729.
[2] S. Venkatraman, B. Surendiran, Adaptive hybrid intrusion detection system for crowd
sourced multimedia internet of things systems, Multimed. Tools Appl. 79 (2020) 3993–4010.
[3] V. Madaan, D. Sethi, P. Agrawal, L. Jain, R. Kaur, Public network security by bluffing the
intruders through encryption over encryption using public key cryptography method, in:
Advanced Informatics for Computing Research: First International Conference, ICAICR
232
2017, Jalandhar, India, March 17–18, 2017, Revised Selected Papers, Springer, 2017, pp.
249–257.
[4] N. Mohod, P. Agrawal, V. Madan, Human detection in surveillance video using deep
learning approach, in: 2023 6th International Conference on Information Systems and
Computer Networks (ISCON), 2023, pp. 1–6. doi:10.1109/ISCON57294.2023.10111951 .
[5] A. V. Srinivas, S. S. S. Reddy, A novel approach for excavating communication using
taxonomy and outline mechanisms, in: 2021 Sixth International Conference on Image
Information Processing (ICIIP), IEEE, 2021.
[6] J. Jeon, J. H. Park, Y.-S. Jeong, Dynamic analysis for IoT malware detection with convolution
neural network model, IEEE Access 8 (2020) 96899–96911.
[7] R. Shanker, P. Agrawal, A. Singh, M. W. Bhatt, Framework for identifying network attacks
through packet inspection using machine learning, Nonlinear Engineering 12 (2023)
20220297.
[8] R. Shanker, V. Madaan, P. Agrawal, Fss-part: Feature grouping subset model for predicting
network attacks, SN Computer Science 5 (2023) 94.
[9] X. Liu, J. Zhang, Y. Lin, H. Li, ATMPA: Attacking machine learning-based malware
visualization detection methods via adversarial examples (2018). arXiv:1808.01546 .
[10] O. Aslan, M. Ozkan-Okay, D. Gupta, Intelligent behavior-based malware detection system
on cloud computing environment, IEEE Access 9 (2021) 83252–83271.
[11] A. Anand, S. Rani, D. Anand, H. M. Aljahdali, D. Kerr, An efficient CNN-based deep
learning model to detect malware attacks (CNN-DMA) in 5G-IoT healthcare applications,
Sensors (Basel) 21 (2021) 6346.
[12] J. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search
algorithm, Syst. Sci. Control Eng. 8 (2020) 22–34.
[13] B. Binu D Kariyappa, Rider deep LSTM network for hybrid distance score-based fault
prediction in analog circuits, IEEE Transactions on Industrial Electronics (2020).
[14] S. S. S. Reddy, G. Sowmya, V. B. Reddy, B. D. Kumar, A. Kumar, Deep cnn based whale
optimization for predicting the rice plant disease in real time, in: International Conference
on Artificial Intelligence and Data Science, Springer, 2021, pp. 191–202.
[15] F. Ceschin, F. Pinage, M. Castilho, D. Menotti, L. S. Oliveira, A. Gregio, The need for speed:
An analysis of brazilian malware classifiers, IEEE Secur. Priv. 16 (2018) 31–41.
[16] D. Binu, B. Kariyappa, Rider-deep-lstm network for hybrid distance score-based fault predic-
tion in analog circuits, IEEE Transactions on Industrial Electronics 68 (2020) 10097–10106.
[17] H. Bommala, R. Aluvalu, S. Mudrakola, et al., Machine learning job failure analysis and
prediction model for the cloud environment, High-Confidence Computing 3 (2023) 100165.
233