=Paper=
{{Paper
|id=Vol-3774/Paper12
|storemode=property
|title=Securing IoT Devices Based on Zero Trust Intrusion Detection
System Using Deep Learning with Sine Cosine Algorithm
|pdfUrl=https://ceur-ws.org/Vol-3774/Paper12.pdf
|volume=Vol-3774
|authors=Sergey Bakhvalov,Viktor Starostin,Rafina Zakieva,M Ilayaraja,E. Laxmi Lydia
|dblpUrl=https://dblp.org/rec/conf/snsfait/BakhvalovSIL24
}}
==Securing IoT Devices Based on Zero Trust Intrusion Detection
System Using Deep Learning with Sine Cosine Algorithm==
https://ceur-ws.org/Vol-3774/Paper12.pdf
Securing IoT Devices Based on Zero Trust Intrusion
Detection System Using Deep Learning with Sine
Cosine Algorithm
Sergey Bakhvalov1,, Viktor Starostin2, Rafina Zakieva3, M Ilayaraja4 and E. Laxmi
Lydia5,∗
1
Candidate of Economic Sciences, Associate Professor of Department of Economics and Management of Elabuga
Institute, Kazan Federal University, Kazan, Russia; bakhvalov.s.yu@yandex.ru
2
Candidate of Medical Sciences, Associate Professor of Department of Theories and Principles of Physical Education and
Life Safety, North-Eastern Federal University named after M.K. Ammosov, Yakutsk, Russia; resprofsci@gmail.com
3
Doctor of Pedagogical Sciences, Associate Professor of Department of Industrial Electronics, Kazan State Power
Engineering University, Kazan, Russia;
zakievarr@inbox.ru
4
School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India; ilayaraja.m@klu.ac.in
5
Department of Information Technology, VR Siddhartha Engineering College(A), Siddhartha Academy of Higher
Education (Deemed to be University), Vijayawada, India; elaxmi2002@yahoo.com
Abstract
The rapid growth of Internet of Things (IoT) devices provides distinct challenges in preserving the
privacy and security of interconnected systems. As cyber-attacks are more common, evolving a
scalable and effective Intrusion Detection System (IDS) based on deep learning (DL) for IoT has
become more complex. When handling evolving and dynamic cyberattacks, the present techniques
are unable to balance temporal and spatial feature extraction. The lack of diversity in dataset
employed for DL-based IDS evaluation also interferes with evolution. Besides, there is a significant
trade-off between scalability and performance, mainly when the amount of edge devices in
communication upsurges. To tackle these challenges, this research paper presents a horizontal DL
method that unites Bidirectional Long-Term Short Memory (BiLSTM) and Convolutional Neural
Network (CNN) for efficient intrusion detection. This article introduces a novel Sine Cosine
Algorithm with Deep Learning based Zero Trust Intrusion Detection System (SCADL-ZTIDS) method
for secure IoT Devices. The foremost intention of the SCADL-ZTIDS technique rests in the effectual
and automated classification of zero trust IDS. In the first stage, the SCADL-ZTIDS approach endures
a min-max scaler utilizing data pre-processing to convert the actual data into beneficial form.
Moreover, the deep neural network (DNN) technique is employed for the identification and
classification of intrusions. Furthermore, the sine cosine algorithm (SCA) is utilized for fine-tuning
the parameters contained in the DNN method. To describe the heightened performance of the
SCADL-ZTIDS approach, a wide range of empirical analyses are implemented on benchmark datasets,
and the outcomes are examined under various features. The simulation outcomes highlighted the
improved intrusion detection performance of the SCADL-ZTIDS approach over the recent DL
techniques.
Keywords
Intrusion Detection System, Zero Trust, IoT Devices, Sine Cosine Algorithm, Deep Learning, Data
Pre-Processing 1
Proceedings of SNSFAIT 2024: International Symposium on Securing Next-Generation Systems using Future
Artificial Intelligence Technologies, Delhi, India, August 08-09th, 2024
1∗
Corresponding author : , elaxmi2002@yahoo.com –(E. Laxmi Lydia)
†
These authors contributed equally.
bakhvalov.s.yu@yandex.ru ( Sergey Bakhvalov); resprofsci@gmail.com (Viktor Starostin);
zakievarr@inbox.ru (Rafina Zakieva), ilayaraja.m@klu.ac.in -(M Ilayaraja)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1. Introduction
The Internet of Things (IoT) transformed digital connectivity by presenting smart
communication techniques, resulting in more reliable, dynamic, and efficient network
communication [1]. It enables intelligence network processes amongst manual objects using
sensors and a communication protocol. Sensors generate a connectivity set-up by connecting
the applicants, and the communication protocol controls the flow of data over the network [2].
From this perspective, the idea of zero trust turns out to be critical. Cyber experience in IoT
creates chances for different antagonistic pursuits like malware exploits, Denial of Service (DoS)
attacks, phishing schemes, IoT botnet infiltrations, routing manipulations, Man-in-the-Middle
(MITM) attacks, and tasks associated with cloud securities [3].
The zero-trust method has become progressively more widespread as administrations look
to improve their cybersecurity posture in front of developing threats [4]. By pretentious that
each customer, application, and network device is untrustworthy till confirmed reliable, the
zero-trust approach can assist in reducing the attack footsteps and reduce data breaches by
incessantly authenticating and verifying devices, applications, and users in an active
environment [5]. The zero-trust concentrated on limiting source access and admitting access on
the standard of the minimum privileges necessary to implement the desired task. An operative
description of zero trust is whether it be a security approach that needs continuous
authentication and verification of all devices, applications, and users’ earlier allowing access to
resources [6]. Zero trust is a complete security outline and operating policies that execute the
zero-trust method over an organization's complete networks, to reduce the threat of data
breaches and cyberattacks [7].
The safety of IoT networks besides cyber threats is becoming a paramount study area in
modern years [8]. Artificial Intelligence (AI) based methodologies, particularly Federated
Learning (FL), have stored significant attention for recommending security resolutions because
of their privacy-preserving mechanism and reliability [9]. The FL-based Intrusion Detection
System (IDS) functions in a systematic design except for real data interchange among the
participant nodes and the central server; as an alternative, this method upgrade is shared among
the communication party [10]. FL significantly supports continuous learning methods in which
iterative cycles are executed, succeeding that the central server has distributed the universal
approach. When training at the confined data nodes, this method upgrades were transferred
back to the server.
This article introduces a new Sine Cosine Algorithms with DL based Zero Trust Intrusion
Detection System (SCADL-ZTIDS) method for secure IoT Devices. In the first stage, the SCADL-
ZTIDS approach endures min-max scaler utilizing data preprocessing to transform the actual
data into beneficial forms. Moreover, the deep neural network (DNN) technique is employed
for the identification and classification of intrusions. Furthermore, the sine cosine algorithm
(SCA) is utilized for fine-tuning the parameters contained in the DNN method. To describe the
heightened performance of the SCADL-ZTIDS approach, wide range of empirical analyses are
implemented on benchmark datasets, and the results are studied below various features.
2. Related Works
Dhanya and Chitra [11] present a DL method named Autoencoder to encode the IoMT data.
The encoding features are provided to an XGBoost Classification whose hyper-parameters are
enhanced by utilizing the Genetic Algorithms. XGBoost classification identifies the occurrence
of malware in the clamp and IoMT datasets by a precision correspondingly. This lightweight
method attains the reduction of dimensionality using Autoencoder and efficiently identifies
malware with an enhanced XGBoost classification through limited computing cost and faster
convergence. In [12], an improved DL method based on uniting XGBoost and AutoEncoder (AE)
method is presented. At initial, the SHapley Additive exPlanations (SHAPs) FS technique is
utilized to choose the proper subset features. Then, the AE is trained on the preceding subsets
to study a squeeze representation of the input feature. The latent representation produced by
the AE is utilized as input for the XGBoost method, simultaneously, Grid Search Cross
Validation (GSCV) is utilized to detect the optimum hyper-parameters for the AE-XGBoost.
Zhu and Liu [13] present a new technique that leverages a single combination of ensemble
learning and subspace clustering. This structure incorporates 3 advanced tactics: Iterative
Feedback Loop (IFL), Two Level Decision Making (TDM), and Clustering Results as Features
(CRFs). This method uses common data for the selection of features and uses 4 sub space
clustering methods–, LOF, SUBCLU, PROCLUS, and CLIQUE– to generate further feature sets.
3 base learners – XGB, NB, and LGBM– are utilized in conjunction with a Logistic Regression
(LR) Meta learners. To tune our method, the method employs a Particle Swarm Optimizer (PSO)
for the optimization of hyper-parameters. Cheng et al. [14] present an in vehicle IDS, which
incorporates an integration of stream clustering and sparse regularization convolutional AE
(SRCAE) to create a deep evolving stream clustering method, like DESC-IDS. Particularly, this
technique encodes the constant message as a 2D data frame that is fed into the SRCAE created
by the temporal CN (TCN) method. In [15], an optimum secure defense mechanism is presented
for DDoS in IoT networks utilizing the feature optimization and intrusion detection system
(OSD-IDS). The method proposes an improved ResNet structure for feature extraction that
mines many profound features from the traces of given traffic traces. An improved quantum
query optimizer (IQQO) method is utilized in FS. The model designs an accurate and fast
intrusion detection mechanism, called as hybrid DL method that incorporates CNN and
diagonal XG boosting (CNN-DigXG) methods.
3. Methodology
In this article, we have introduced a novel SCADL-ZTIDS method for secure IoT Devices. The
foremost intention of the SCADL-ZTIDS technique rests in the effectual and automated
classification of zero trust IDS. It contains three distinct processes such as preprocessing,
classification, and parameter tuning are demonstrated in Fig. 1.
Figure 1: Overall process of SCADL-ZTIDS method
3.1. Min-max Scaler
In the first stage, the SCADL-ZTIDS approach endures min-max scaler utilizing data
preprocessing to transform the actual data into beneficial forms. The Min-Max Scaler is a vital
data pre-processing method employed to regularize the scope of feature values [16]. By scaling
feature to a standard range, usually [0, 1], the Min-Max Scaler certifies that every feature
donates evenly to the method, averting any distinct feature from unevenly influencing the
outcomes. This normalization is vital to enhance the accuracy and performance of ML
techniques in identifying intrusions, as it permits the model to progress input data effectively
and consistently, increasing its capability to detect latent security threats in an IoT
environment.
3.2. Intrusion Detection using DNN Model
Next, the DNN technique is utilized for the identification and classifier of intrusions. A DNN is
a novel kind of artificial neural network (ANN), which contains many layers and authorizes it
to obtain data and appeal conclusions from wide databases [17]. DNN excels in speech and
image detection tasks, exhibiting their skill to identify compound patterns and deliver specific
forecasts. DNN has 3 main layers such as input, output, and hidden layer (HL). The DNN is
organized with double HLs to easily acquire the map relation between the output and input data
by engaging the weight fitness. Throughout the stage of training, the DNN utilizes JOA to attain
its objectives. In the HL, the weight of the nodes was modified. Always, the neural networks
are regulating the decision limit of the categorized training data owing to the enlarged amount
of training iteration. To get superior identification accuracy and quicker DNN training, dual
HLs are generated. The complete quantity of nodes in the HL is defined utilizing Eq. (1).
𝑗 = √𝑀 + 𝑁 + 𝐵 (1)
The input and output layers have 𝑀 and 𝑁 nodes, respectively. The HL has 𝑗 nodes, which
refers to a constant value between 1 and 10, and is signified as 𝐵.
In the HL of DNN, an activation function is comprised to allow non‐linear fitness capability.
Then the sigmoid is applied as an activation function.
1
𝐴− (2)
1 + 𝑒 !"
𝑃 denotes the input data and is activated by the map function 𝐾# .
𝐾# − 𝑠𝑖𝑔𝑚(𝛼$ 𝑃 + 𝛾$ ) (3)
The matrix of weight and bias among the layer of output and HL were signified by 𝑃 and 𝑁,
correspondingly.
To make sure the interior neuron of a DNN, we intend a supervised 𝑙𝑜𝑠𝑠 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛%&!%' .
This model utilizes labeled data samples. Particularly, we have a data sample (𝑃, 𝑙), which is
labeled theoretically for an HL, so we can compute the function of loss.
)
1
𝑉(𝑊( , 𝑁( ; 𝑝, 𝑙) = F ‖ 𝑂* (𝑊( , 𝑁( ; 𝑃) − 𝑙* ‖%% (4)
2𝑠
*+,
𝑊( and 𝑁( are the sub-sets of biases; the amount of neurons in the HL is represented as 𝑆.
Figure 2: DNN structure
The DNN utilizes cross entropy for both testing and training uses as its loss function. This
is the major development in the performance of SoftMax and sigmoid output methods. The
formulation of cross entropy loss is expressed in Eq. (11).
.
1
Q0 + (1 − 𝐷/ ) log (1 − 𝐷
𝑅- = F[ 𝐷/ log𝐷 Q/ )] (5)
𝑗
/+,
Where 𝑗 denotes the integer of training sample, 𝐷/ and 𝑘12 expresses the 𝑘12 output of training
and testing sets, respectively. Fig. 2 depicts the infrastructure of DNN.
3.3. Parameter Tuning
Eventually, the SCA is utilized for fine-tuning the parameters contained in the DNN method.
The SCA is a population‐based optimization algorithm that randomly produces many promising
solutions for the optimization problem [18]. It exploits the mathematical equation of Sin‐Cos to
oscillate away from or towards the optimum solution, highlighting exploration and exploitation
to find a global optimum solution in the search range.
Compared to another population‐based approach, SCA has shown its maximum
effectiveness in reaching global optimal solution. It explores various milestones in the search
range when the sin and cos functions produce values below or beyond the one. This can be
mathematically formulated as follows:
𝑋$13, = 𝑋$1 + 𝑟, sin(𝑟% ) ∗ [𝑟& 𝑃$1 − 𝑋$1 [ (6)
13, 1 1 1
)
𝑋$ = 𝑋$ + 𝑟, cos(𝑟% ∗ [𝑟& 𝑃$ − 𝑋$ [ (7)
1 1
Where, 𝑋$ and 𝑃$ are the current and best candidate locations in the 𝑖 dimension at 𝑡 12 12
iterations. 𝑟, , 𝑟% , 𝑟& , and 𝑟4 are the random integers.
𝑋$1 + 𝑟, ∗ sin(𝑟% ) ∗ [𝑟& 𝑃$1 − 𝑋$1 [, 𝑟4 < 0.5
𝑋$13, = _ 1 (8)
𝑋$ + 𝑟, ∗ cos(𝑟% ) ∗ [𝑟& 𝑃$1 − 𝑋$1 [, 𝑟4 ≥ 0.5
In Eq. (8), 𝑟, , defines whether the search range stays within or extends beyond the solution
space. 𝑟% , define the extent of this deviance from the destination. 𝑟& presents a random weight
to the destination, de‐emphasizing ((𝑟& ) < 1) or emphasizing d(𝑟& ) > 1f its influence on the
distance. Finally, 𝑟4 smoothly alternates between the sin and cos components. During the search
process, Eq. (9) dynamically fine‐tunes 𝑟, to balance exploration and exploitation.
𝑎
𝑟, = 𝑎 − 𝑡 (9)
𝑇
Where 𝑇 is the optimum iteration counter, 𝑡 is the existing iteration and 𝑎 is a constant value.
The SCA is harnessed for optimizing the learning rate of Patch GAN discriminator and the
generator. The learning rate considerably influences performance of the model. The algorithm
finds the optimum value configuration that increases a performance measure. It modifies the
learning rate iteratively through a metaheuristic algorithm that seeks the configuration that
leads to effective generation of real images. This ensures that the generator has been
instrumental in the overall image quality.
𝑥$ (𝑡 + 1) = 𝑥$ (𝑡) + 𝑟, (𝑡) ⋅ sin(𝜔𝑡) ⋅ |𝑝56)1 (𝑡) − 𝑥$ (𝑡)|
+𝑟% (𝑡) ⋅ cos(𝜔𝑡) ⋅ |𝑔56)1 (𝑡) − 𝑥$ (𝑡)| (10)
12 12
In Eq. (10), 𝑋(𝑡) refers to the location of 𝑖 particles at 𝑡 iteration. 𝑝56)1 (𝑡) and 𝑔56)1 (𝑡)
are the optimum location and the global optimum location of each particle. 𝜔 shows the angular
frequency. 𝑟(𝑡) and 𝑟(𝑡) are two random values within [0,1]. Thus, the update of learning rate
can be formulated as follows:
l𝑒𝑎𝑚𝑖𝑛𝑔7816 = l𝑏 + 0.5 ⋅ (𝑢𝑏 − 𝑙𝑏) ⋅ d1 + sin(𝑎)f (11)
In Eq. (11), 𝑙𝑏 and 𝑢𝑏 are the lower and upper boundaries of learning rates. The parameter 𝑎
differs with all the iterations as follows:
2𝜋𝑡
𝑎= (12)
max$167
In Eq. (12), 𝑡 and max$167 are the existing and the maximal iteration counters. 𝜋 indicates
the mathematical constant 𝑝𝑖. The variable 𝑎 is adjusted according to the 𝑡 12 existing iteration
to the max$167 . This makes a smooth changing variable that proportionally increases with the
iteration count. The 2𝜋 term proposes a full cycle, hence the parameter 𝑎 differs over the full
cycle as the process repeats from 1 to max$167 .
The SCA obtains a FF to achieve enhanced classifier performances. It defines a positive
integer to express the best performances of the candidate solution. In this research, the
reduction of the classifier rate of error is examined as the FF, as provided in Eq. (13).
𝑓𝑖𝑡𝑛𝑒𝑠𝑠(𝑥$ ) = 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑟𝐸𝑟𝑟𝑜𝑟𝑅𝑎𝑡𝑒(𝑥$ )
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑖𝑠𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑠𝑎𝑚𝑝𝑙𝑒𝑠
= ∗ 100 (13)
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑒𝑠
4. Result Analysis and Discussion
In this section, the stimulation validation analysis of the SCADL-ZTIDS method is tested
using UNSW-NB18 dataset [19], which contains 2500 sample records under five classes are
represented in Table 1.
Table 1
Details on Dataset
Type of Attacks Data Record
Normal 500
Generic 500
Exploits 500
Fuzzers 500
DoS 500
Total Record 2500
Fig. 3 depicts the classifier outcomes of the SCADL-ZTIDS model under the test database. Figs.
3a-3b shows the confusion matrix with correct classification and identification of all 5 classes
on a 70:30 TRAP/TESP. Fig. 3c represents the analysis of PR, pointing out superior performance
over all class labels. Finally, Fig. 3d denoted the analysis of ROC and portrayed efficient
outcomes with greater values of ROC for different classes.
Figure 3: Classifier outcomes of (a-b) Confusion Matrices and (c-d) PR and ROC curves
Table 2 represents the detection results of the SCADL-ZTIDS approach with 70%TRAP and
30%TESP. The results inferred that the SCADL-ZTIDS model has appropriately identified five
classes. With 70%TRAP, the SCADL-ZTIDS methodology gains an average 𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; ,
𝐹1)<=76 , and MCC of 96.07%, 90.28%, 90.12%, 90.05%, and 87.72%, respectively. Moreover, with
30%TESP, the SCADL-ZTIDS approach obtains average 𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; , 𝐹1)<=76 , and MCC
of 96.48%, 91.31%, 91.21%, 91.19%, and 89.04%, accordingly.
In Fig. 4, the training and validation accuracy outcomes of the SCADL-ZTIDS method are
established. The precision values are calculated for 0-25 epoch counts. This figure underlined
that the training and validation accuracy values display a growing trend that informed the
capability of the SCADL-ZTIDS technique with enhanced performance across numerous
iterations. Moreover, the training accuracy and validation accuracy stay nearer over the epoch
counts which specifies less minimum overfitting and shows greater performance of the SCADL-
ZTIDS systems, assuring continuous prediction on hidden instances.
Table 2
Detection outcomes of SCADL-ZTIDS approach at70%TRAP and 30%TESP
Class 𝑨𝒄𝒄𝒖𝒚 𝑷𝒓𝒆𝒄𝒏 𝑹𝒆𝒄𝒂𝒍 𝑭𝟏𝑺𝒄𝒐𝒓𝒆 MCC
TRAP (70%)
Normal 95.89 91.94 87.25 89.53 87.02
Generic 94.91 92.48 81.09 86.41 83.57
Exploits 97.60 90.82 98.34 94.43 93.02
Fuzzers 95.89 86.63 93.64 90.00 87.52
DoS 96.06 89.50 90.29 89.90 87.45
Average 96.07 90.28 90.12 90.05 87.72
TESP (30%)
Normal 96.40 93.48 87.76 90.53 88.37
Generic 95.73 92.20 86.09 89.04 86.47
Exploits 97.47 91.61 94.93 93.24 91.70
Fuzzers 96.13 88.34 93.51 90.85 88.46
DoS 96.67 90.91 93.75 92.31 90.20
Average 96.48 91.31 91.21 91.19 89.04
Figure 4: 𝐴𝑐𝑐𝑢9 curve of the SCADL-ZTIDS approach
In Fig. 5, the training and validation loss graph of the SCADL-ZTIDS approach is presented.
The loss values are calculated throughout 0-25 epoch counts. It is portrayed that the training
and validation accuracy values demonstrate the lowest trend that reported the capacity of the
SCADL-ZTIDS technique to balance a trade-off between generalization and data fitting. The
consistent decrease in loss values also promises better performance of the SCADL-ZTIDS
methodology and tuning of the prediction outcomes in time.
Figure 5: Loss curve of the SCADL-ZTIDS approach
Table 3
Comparative outcome of SCADL-ZTIDS approach with existing models
Research 𝑨𝒄𝒄𝒖𝒚 𝑷𝒓𝒆𝒄𝒏 𝑹𝒆𝒄𝒂𝒍 𝑭𝟏𝑺𝒄𝒐𝒓𝒆
SCADL-ZTIDS 96.48 91.31 91.21 91.19
Random Forest 95.43 85.89 89.73 83.01
Decision Tree 94.20 81.94 89.13 81.29
CNN Classifier 96.00 82.84 86.71 85.28
MLP Algorithm 84.24 83.60 84.24 82.85
KNN Model 75.62 79.92 75.61 76.58
Densely-ResNet 73.93 80.94 76.68 88.11
In Table 3 and Fig. 6, the performance outcomes of the SCADL-ZTIDS method with the
existing technique are provided [20-22]. These outcomes display that the Densely-ResNet
approach showed inferior performance with 𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; , and 𝐹1)<=76 of 73.93%,
80.94%, 76.68%, and 88.11%, correspondingly. Simultaneously, the KNN methodology has
attained marginally improved results with 𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; , and 𝐹1)<=76 of 75.62%, 79.92%,
75.61%, and 76.58%, appropriately. Further, the MLP, CNN, and DT techniques have achieved
reasonably adjacent performance. In the meantime, the RF approach has caused significant
results with𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; , and 𝐹1)<=76 of 95.43%, 85.89%, 89.73%, and 83.01%,
proportionately. Then the SCADL-ZTIDS model exceeds the other technique with
greater𝑎𝑐𝑐𝑢9 , 𝑝𝑟𝑒𝑐: , 𝑟𝑒𝑐𝑎; , and 𝐹1)<=76 of 96.48%, 91.31%, 91.21%, and 91.19%, correspondingly
[21].
Figure 6: Comparative outcome of SCADL-ZTIDS approach with existing models
5. Conclusion
In this article, we have introduced a novel SCADL-ZTIDS method for secure IoT Devices.
The foremost intention of the SCADL-ZTIDS technique rests in the effectual and automated
classification of zero trust IDS. In the first stage, the SCADL-ZTIDS approach endures min-max
scaler utilizing data pre-processing to convert the actual data into beneficial form. Moreover,
the DNN technique is employed for the identification and classification of intrusions.
Furthermore, the SCA is utilized for fine-tuning the parameters contained in the DNN method.
To describe the heightened performance of the SCADL-ZTIDS approach, wide range of
empirical analyses are implemented on benchmark datasets, and the outcomes are examined
under various features. The simulation outcomes highlighted the improved intrusion detection
performance of the SCADL-ZTIDS approach over the recent DL techniques.
References
[1] J. Simon, N. Kapileswar, P.K. Polasi, M.A. Elaveini, Hybrid intrusion detection system for
wireless IoT networks using deep learning algorithm, Computers and Electrical
Engineering, 102, (2022) 108190.
[2] G. Parimala , R. Kayalvizhi, An Effective Intrusion Detection System for Securing IoT Using
Feature Selection and Deep Learning, 2021 International Conference on Computer
Communication and Informatics (ICCCI), Coimbatore, India, (2021) 1-4.
[3] R. Salama, M. Ragab, Blockchain with Explainable Artificial Intelligence Driven Intrusion
Detection for Clustered IoT Driven Ubiquitous Computing System, Computer Systems
Science & Engineering, 46.3, (2023) 2917-2932.
[4] S. Fraihat, S. Makhadmeh, M. Awad, M.A. Al-Betar , A. Al-Redhaei, Intrusion detection
system for large-scale IoT NetFlow networks using machine learning with modified
Arithmetic Optimization Algorithm, Internet of Things, (2023) 100819.
[5] A.D. Jasim, A survey of intrusion detection using deep learning in internet of things, Iraqi
Journal For Computer Scienc., Mathematics, 3.1, (2022) 83-93.
[6] D. Musleh, M. Alotaibi, F. Alhaidari, A. Rahman , R.M. Mohammad, Intrusion Detection
System Using Feature Extraction with Machine Learning Algorithms in IoT, Journal of
Sensor and Actuator Networks, 12.2, (2023) 29.
[7] B. R, S. Deepajothi, P. G, D. T, P. Karthikeyan , V. S, Survey on Intrusions Detection System
using Deep learning in IoT Environment, 2022 International Conference on Sustainable
Computing and Data Communication Systems (ICSCDS), Erode, India, (2022) 195-199.
[8] M. Ragab, M.F. S. Sabir, Outlier detection with optimal hybrid deep learning enabled
intrusion detection system for ubiquitous and smart environment, Sustainable Energy
Technologies and Assessments, 52 (2022) 102311.
[9] J. Shareena, A. Ramdas, H. AP, Intrusion detection system for iot botnet attacks using deep
learning, SN Computer Science, 2.3, (2021) 1-8.
[10] Y. Zhang, P. Li , X. Wang, Intrusion detection for IoT based on improved genetic algorithm
and deep belief network, IEEE Access, 7, (2019) 31711-31722.
[11] L. Dhanya, R. Chitra, A novel autoencoder based feature independent GA optimised
XGBoost classifier for IoMT malware detection, Expert Systems with Applications, 237,
(2024) 121618.
[12] M.A. Setitra, M. Fan , Z.E.A. Bensalem, An efficient approach to detect distributed denial
of service attacks for software defined internet of things combining autoencoder and
extreme gradient boosting with feature selection and hyperparameter tuning optimization,
Transactions on Emerging Telecommunications Technologies, 34.9, (2023) 4827.
[13] J. Zhu , X. Liu, An integrated intrusion detection framework based on subspace clustering
and ensemble learning, Computers and Electrical Engineering, 115, (2024) 109113.
[14] P. Cheng, M. Han, G. Liu, DESC-IDS: Towards an efficient real-time automotive intrusion
detection system based on deep evolving stream clustering, Future Generation Computer
Systems, 140, (2023) 266-281.
[15] J.S. Prasath, V.I. Shyja, P. Chandrakanth, B.K. Kumar , A. Raja Basha, An optimal secure
defense mechanism for DDoS attack in IoT network using feature optimization and
intrusion detection system, Journal of Intelligent & Fuzzy Systems, (Preprint), (2024) 1-18.
[16] B. Deepa, K. Ramesh, Epileptic seizure detection using deep learning through min max
scaler normalization, Int. J. Health Sci, 6, (2022) 10981-10996.
[17] Z. Zhou, H. Zhang, M. Effatparvar, Improved sports image classification using deep neural
network and novel tuna swarm optimization, Scientific Reports, 14.1, (2024) 1-20.
[18] A. Alqushaibi, M.H. Hasan, S.J. Abdulkadir, K.U. Danyaro, M.G. Al-Selwi, E.H. Sumiea, H.
Alhussian, Enhanced Colon Cancer Segmentation and Image Synthesis through Advanced
Generative Adversarial Networks based-Sine Cosine Algorithm, in IEEE Access, 2024.
[19] https://www.kaggle.com/datasets/dhoogla/unswnb15
[20] I. Katib, M. Ragab, Blockchain-assisted hybrid harris hawks optimization based deep DDoS
attack detection in the IoT environment, Mathematics, 11.8 (2023) 1887.
[21] I. Tareq, B.M. Elbagoury, S. El-Regaily , E.S.M. El-Horbaty, Analysis of ton-iot, unw-nb15,
and edge-iiot datasets using dl in cybersecurity for iot, Applied Sciences, 12.19, (2022) 9572.
[22] M. Basheri, M. Ragab, Quantum Cat Swarm Optimization Based Clustering with Intrusion
Detection Technique for Future Internet of Things Environment, Computer Systems
Science & Engineering, 46.3, (2023) 3783-3798.