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Research article Special Issues

Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning

  • Received: 16 January 2024 Revised: 08 April 2024 Accepted: 10 April 2024 Published: 06 May 2024
  • MSC : 11Y40

  • Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.

    Citation: Mohammed Aljebreen, Hanan Abdullah Mengash, Khalid Mahmood, Asma A. Alhashmi, Ahmed S. Salama. Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning[J]. AIMS Mathematics, 2024, 9(6): 15796-15818. doi: 10.3934/math.2024763

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  • Internet of Things (IoT) security is an umbrella term for the strategies and tools that protect devices connected to the cloud, and the network they use to connect. The IoT connects different objects and devices through the internet to communicate with similarly connected machines or devices. An IoT botnet is a network of infected or cooperated IoT devices that can be remotely organized by cyber attackers for malicious purposes such as spreading malware, stealing data, distributed denial of service (DDoS) attacks, and engaging in other types of cybercrimes. The compromised devices can be included in any device connected to the internet and communicate data with, e.g., cameras, smart home appliances, routers, etc. Millions of devices can include an IoT botnet, making it an attractive tool for cyber attackers to launch attacks. Lately, cyberattack detection using deep learning (DL) includes training neural networks on different datasets to automatically detect patterns indicative of cyber threats, which provides an adaptive and proactive approach to cybersecurity. This study presents an evolutionary algorithm with an ensemble DL-based botnet detection and classification (EAEDL-BDC) approach. The goal of the study is to enhance cybersecurity in the cloud-assisted IoT environment via a botnet detection process. In the EAEDL-BDC technique, the primary stage of data normalization using Z-score normalization is performed. For the feature selection process, the EAEDL-BDC technique uses a binary pendulum search algorithm (BPSA). Moreover, a weighted average ensemble of three models, such as the modified Elman recurrent neural network (MERNN), gated recurrent unit (GRU), and long short-term memory (LSTM), are used. Additionally, the hyperparameter choice of the DL approaches occurs utilizing the reptile search algorithm (RSA). The experimental outcome of the EAEDL-BDC approach can be examined on the N-BaIoT database. The extensive comparison study implied that the EAEDL-BDC technique reaches a superior accuracy value of 99.53% compared to other approaches concerning distinct evaluation metrics.



    The models involving entry-exit decisions apply to many situations, as stated in [1,2]. Such models may be described simply as follows. A firm decides when to invest in or when to abandon a project that can bring profit. There is plenty of literature on models concerning how to schedule the timing under the assumption that exiting the project does not take time, so we do not list them in detail, but refer to [1,2,3,4,5,6,7,8,9]. The ideas of entry-exit decisions apply to many concrete issues, for example, relationships of globalization and entrepreneurial entry-exit [10], when to invest or expand a start-up firm [11], when to enter or exit stock markets [12], and how to make a schedule for buying carbon emission rights [13].

    In practice, the exiting process may take a long time, so ignoring it is not reasonable. For example, Brexit took more than 3 years, from June 23, 2016 to January 31, 2020 (https://www.britannica.com/topic/Brexit). The models [14,15] take account of the time, but with equal output rates in the regular production and exit periods. It is an apparent feature that the output rate of the project is usually reduced during the exit period. In this paper, we introduce a parameter to describe the output rate during the exit period and completely discuss the effects of output reduction on entry-exit decisions, under the assumption that the commodity price of the project follows a geometric Brownian motion.

    We use the optimal stopping theory to carry out the study and obtain the explicit expressions of the optimal activating time and start time of the exit, which is one of the contributions of the paper. With these explicit expressions in hand, we can carefully analyze the effects of output reduction on entry-exit decisions, which is another contribution of the paper.

    If the optimal choice is never to exit the project, the output reduction has no effect on the optimal entry-exit decision.

    If the optimal exit is in a finite time, the situation becomes complicated. However, we obtain complete criterions, which determine the effects of output reduction on entry-exit decisions (see Section 4).

    We outline the structure of this paper. In Section 2, we describe the model in detail. In Section 3, we determine an optimal entry-exit decision. In Section 4, we discuss the effects of output reduction during exit period on entry-exit decisions. Some conclusions are drawn in Section 5.

    Assume that the price process P of one unit product follows

    dP(t)=μP(t)dt+σP(t)dB(t)andP(0)=p, (2.1)

    where μR, σ,p>0, and B is a one dimensional standard Brownian motion, which denotes uncertainty. In this paper, all times are stopping times w.r.t. the filtration generated by the Brownian motion B.

    Since involving the construction period leads to complicated calculations and distracts us from analyzing the effects of reduction on entry-exit decisions, and the effects of the construction period have been discussed in [3,15], we assume that there is no construction period (it may happen when the firm buys a project). The firm activates the project at time τI with the entry cost KI and completes the abandonment of the project during the time interval [τO,τO+δ], with the exit cost valued at KO at time τO+δ. Without loss of generality, we assume that the firm produces one unit product per unit time during the period [τI,τO] at the marginal cost C and α (0α1) unit products per unit time during the time interval of exiting the project [τO,τO+δ].

    To answer the two questions, what time is optimal to activate the project and what time is optimal to start the abandonment procedure, we solve the optimization problem

    J(p)=supτIτOEp[τOτIexp(rt)(P(t)C)dt+ατO+δτOexp(rt)(P(t)C)dtexp(rτI)KIexp(r(τO+δ))KO]. (2.2)

    We call stopping times τI and τO the activating times and start times of the exit, respectively, and we call the function J the maximal expected present value of the project.

    If rμ, some straight calculations show us that

    Ep[+0exp(rt)(P(t)C)dt]=+.

    Thus, we obtain the following result.

    Theorem 3.1. Assume that rμ, then τI: =0 is an optimal activating time and τO: =+ is an optimal start time of the exit, i.e., the firm should never exit the project. In addition, the function J in (2.2) is given by J+.

    In the rest of this section, we assume r>μ.

    Taking τI=τO: =0 in (2.2), we have

    J(p)KI+α(1exp((μr)δ))rμpexp(rδ)KOαCr(1exp(rδ)),

    thus, in the remains of this section, we always assume that

    rKI+exp(rδ)rKO+α(1exp(rδ))C0

    to avoid arbitrage opportunities.

    Let λ1 and λ2 be the solutions to the equation

    rμλ12σ2λ(λ1)=0

    with λ1<λ2, then we have λ1<0 and λ2>1.

    Theorem 3.2. Assume that r>μ. The following are true:

    (ⅰ) If

    (1α)C+(αCrKO)exp(rδ)0,

    then (τI,τO) is a solution to (2.2), where

    τI=inf{t:t>0,P(t)pI}

    and τO=+. Here,

    pI=λ2λ21(rμ)(Cr+KI).

    In addition,

    J(p)={Bpλ2,ifp<pI,prμCrKI,ifppI,

    where

    B=pI1λ2λ2(rμ).

    (ⅱ) If

    (1α)C+(αCrKO)exp(rδ)>0

    and

    α(1exp((μr)δ))pOαC(1exp(rδ))exp(rδ)rKOrKI0, (3.1)

    where

    pO=λ1λ11rμ1α+αexp((μr)δ)((1α)Cr+exp(rδ)(αCrKO)),

    then (τI,τO) is a solution to (2.2), where

    τI=inf{t:t>0,P(t)pI}

    and

    τO=inf{t:t>τI,P(t)pO}.

    Here, pI is the largest solution of the algebraic equation

    A(λ2λ1)pλ1I+(λ21)rμpIλ2(Cr+KI)=0.

    In addition,

    J(p)={Bpλ2,ifp<pI,Apλ1+prμCrKI,ifppI,

    where

    A=1α+αexp((μr)δ)λ1(μr)pO1λ1

    and

    B=λ1λ12Apλ1λ2I+pI1λ2λ2(rμ).

    Remark 3.3. We propose condition (3.1) to eliminate the possibility that the firm enters the project at a trigger price lower than the optimal trigger price of the exit, i.e., the firm enters the project and then immediately decides to exit the project.

    In light of [14, Theorems 3.1 and 5.2], we have the following Lemma 3.4, which serves as preparation for the proof of Theorem 3.2.

    Lemma 3.4. If (τ1,τ2) is a solution to the optimization problem

    ˜J(p):=supτIτOEp[τOτIexp(rt)(P(t)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)], (3.2)

    it is also a solution to (2.2) and J(x)=˜J(x), where

    l1:=α(1exp((μr)δ))μr

    and

    l0:=αCr(1exp(rδ))+exp(rδ)KO.

    Proof. By the strong Markov property of the process {(s+t,P(t)),t0}, where sR, we have

    Ep[τOτIexp(rt)(P(t)C)dt+ατO+δτOexp(rt)(P(t)C)dtexp(rτI)KIexp(r(τO+δ))KO]=Ep[τOτIexp(rt)(P(t)C)dt+αexp(rτO)EP(τO)[δ0exp(rt)(P(t)C)dt] exp(rτI)KIexp(r(τO+δ))KO].

    Thus, we need to calculate

    αEP(τO)[δ0exp(rt)(P(t)C)dt]exp(rδ)KO=αδ0exp(rt)(exp(μt)P(τO)C)dtexp(rδ)KO=l1P(τO)l0.

    The proof is complete.

    Remark 3.5. The proof of Lemma 3.4 has an economic meaning as follows. We first discount the benefit during the abandonment period to time τO, then discount this value to time zero.

    With the help of Lemma 3.4, we can prove Theorem 3.2.

    Proof of Theorem 3.2. (1) By Lemma 3.4, we solve problem (3.2),

    supτIτOEp[τOτIexp(rt)(P(t)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)]=supτIτOEp[exp(rτI)τOτI0exp(rt)(P(t+τI)C)dtexp(rτI)KIexp(rτO)(l1P(τO)+l0)]=supτIEp[exp(rτI)(G(P(τI))KI)]=:H(p), (3.3)

    where

    G(p):=supτOEp[τO0exp(rt)(P(t)C)dtexp(rτO)(l1P(τO)+l0)]. (3.4)

    (2) Assume

    (1α)C+(αCrKO)exp(rδ)0.

    We first solve problem (3.4) and then problem (3.3).

    For problem (3.4), noting

    Ep[0exp(rt)(P(t)C)dt]=prμCr,

    we see that

    Ep[0exp(rt)(P(t)C)dt]l1pl0,

    which implies

    τO=+

    and

    G(p)=prμCr.

    Set

    h(p):=prμCrKI.

    Since

    {p:p>0andrh(p)μph(p)0}={p:pC+rKI},

    the exercise region of problem (3.3) takes the form [pI,+) for some

    pIC+rKI.

    The function H satisfies

    rHμpH12σ2p2H=0

    on the continuation region (0,pI) and is Lipschitz continuous on (0,+) and C1 continuous at pI. Thus, we get

    H(p)=Bpλ2

    and B and pI solve

    {Bpλ2I=pIrμCrKI,λ2Bpλ21I=1rμ,

    by which we finish the proof of (ⅰ).

    (3) Assume

    (1α)C+(αCrKO)exp(rδ)>0

    and (3.1) hold. We again first solve problem (3.4) and then problem (3.3).

    Set

    g(p):=l1pl0.

    A straight calculation shows that

    {p:p>0andrgμpg(p)p+C0}=(0,(1α)C+(αCrKO)exp(rδ)1α(1exp((μr)δ))),

    which means the exercise region of problem (3.4) takes the form (0,pO] for some

    pO(1α)C+(αCrKO)exp(rδ)1α(1exp((μr)δ)).

    The function G satisfies

    rGμpG12σ2p2Gp+C=0

    on the continuation region (pO,+) and is Lipschitz continuous on (0,+) and C1 continuous at pO. Thus, we get

    G(p)=Apλ1+prμCr,

    and A and pO solve

    {Apλ1O+pOrμCr=l1pOl0,λ1Apλ11O+1rμ=l1,

    which implies coefficient A and the optimal exit trigger pricer of (ⅱ).

    To solve problem (3.4), we define

    h(p):=G(p)KI.

    In light of (3.1),

    {p:p>0andrh(p)μph(p)0}={p:pC+rKI},

    thus, the exercise region of problem (3.3) takes the form [pI,+) for some

    pIC+rKI.

    The function H satisfies

    rHμpH12σ2p2H=0

    on the continuation region (0,pI) and is Lipschitz continuous on (0,+) and C1 continuous at pI. Thus, we get

    H(p)=Bpλ2,

    and B and pI solve

    {Bpλ2I=Apλ1I+pIrμCrKI,λ2Bpλ21I=λ1Apλ11I+1rμ,

    by which we finish the proof of (ⅱ).

    We analyze the effects of output reduction during the exit period on entry-exit decisions.

    If rμ, the firm has an optimal time τI=0 to activate the project and should never exit the project. Thus, the reduction does not affect entry-exit decisions.

    If r>μ and

    (1α)C+(αCrKO)exp(rδ)0,

    the optimal time to activate the project is given by

    τI=inf{t:t>0,P(t)pI},

    where

    pI=λ2λ21(rμ)(Cr+KI).

    Thus, the reduction does not affect the optimal activating time. As same as the case of rμ, the firm should never exit the project and the reduction does not affect exit decisions.

    Assume that r>μ and

    (1α)C+(αCrKO)exp(rδ)>0.

    We first analyze the effects of reduction on the optimal start time of the exit. By (ⅱ) of Theorem 3.2, the optimal trigger price is an increasing function of α if

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    a decreasing function if

    exp(μδ)(CrKO)<Cexp(rδ)rKO,

    and a constant function if

    exp(μδ)(CrKO)=Cexp(rδ)rKO.

    We list some examples to illustrate the analysis. Taking

    r=0.2,μ=0.1,σ=0.3,δ=2,C=5,KI=20,andKO=10,

    we have

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    then the optimal trigger price is an increasing function of α. See Figure 1. If we replace μ=0.1 with μ=0.1, we have a decreasing function. See Figure 2.

    Figure 1.  α-pO-increasing.
    Figure 2.  α-pO-decreasing.

    We conclude that:

    (1) If

    exp(μδ)(CrKO)>Cexp(rδ)rKO,

    as the reduction increases (i.e., α decreases), the firm will postpone the exit.

    (2) If

    exp(μδ)(CrKO)<Cexp(rδ)rKO,

    as the reduction increases, the firm will advance the exit.

    (3) If

    exp(μδ)(CrKO)=Cexp(rδ)rKO,

    the reduction does not affect the exit.

    To analyze the effects of reduction on the optimal time to activate the project, we define two functions as follows:

    f(p):=A(λ2λ1)pλ1

    and

    g(p):=(λ21)rμp+λ2(Cr+KI)

    according to (ⅱ) of Theorem 3.2. Thus, the functions f and g intersect at two points, say, (p1,f(p1)) and (p2,f(p2)) with p1<p2, and the optimal trigger price pI of activating the project is given by pI=p2.

    To capture the behavior of pI as α varies, we only need to examine the behavior of the coefficient A as α varies. Setting

    M:=(Cexp(rδ)rKO)(1exp((μr)δ))+(1λ1)exp(rδ)(CrKOCexp(μδ)+exp((μr)δ)rKO)

    and

    N:=C(1exp(rδ))(1exp((μr)δ)),

    after some calculations, we find that:

    (1) If M/N0, A is increasing on [0,1] and pI is decreasing on [0,1].

    (2) If M/N1, A is decreasing on [0,1] and pI is increasing on [0,1].

    (3) If 0<M/N<1, A is decreasing on [0,M/N] and increasing on [M/N,1] and pI is increasing on [0,M/N] and decreasing on [M/N,1].

    Numerical examples help us understand the analysis above.

    Figure 3 demonstrates the decreasing of pI (r=0.2, μ=0.1, σ=0.3, δ=0.6, C=5, KI=20, and KO=10). Figure 4 demonstrates the increasing of pI (r=0.2, μ=0.1, σ=0.3, δ=2, C=5, KI=20, and KO=10), and Figure 5 demonstrates the non-monotonicity of pI (r=0.2, μ=0.1, σ=0.3, δ=0.6, C=5, KI=20, and KO=10).

    Figure 3.  α-pI-decreasing.
    Figure 4.  α-pI-increasing.
    Figure 5.  α-pI-non-monotonicity.

    In other words, here are the effects of the reduction on the optimal time of activating the project:

    (1) If M/N0, as the reduction increases (i.e., α decreases), the firm will postpone activating the project.

    (2) If M/N0, as the reduction increases, the firm will advance activating the project.

    (3) If 0<M/N<1, as the reduction increases, the firm will postpone activating the project then advance activating the project.

    There is an apparent phenomenon that firms usually reduce their output rate during stopping the regular production of a project. We intend to analyze the effects of reduction on the optimal entry-exit decision. Since the papers [3,15] have investigated the effects of the construction period on entry-exit decisions, we assume that the project has been constructed and the production immediately starts for concentrating on the study of the effects of reduction.

    We introduce the output rate into the models [14,15] and describe the problem using the optimal stopping theory, obtaining explicit solutions in Theorems 3.1 and 3.2. These explicit solutions help us in discovering the effects of reduction on the optimal entry-exit decision.

    We carefully examine the effects of reduction. According the analysis listed in Section 4, we come to the effects of reduction as follows. If the firm should never exit the project to obtain the maximal profit, the reduction does not affect the optimal entry-exit decision. However, if the firm exits the project in finite time, the effects show in a complicated manner. We study the effects analytically and numerically, and provide the relations of the parameters involved in the model that can determine the effects.

    The author declares he has not used Artificial Intelligence (AI) tools in the creation of this article.

    Many thanks are due to the editors and reviewers for their constructive suggestions and valuable comments. This work is partially supported by the Fundamental Research Funds for the Central Universities (Grant No. N2023034).

    No potential conflict of interest exists regarding the publication of this paper.



    [1] J. K. Samriya, C. Chakraborty, A. Sharma, M. Kumar, Adversarial ML-Based Secured Cloud Architecture for Consumer Internet of Things of Smart Healthcare, IEEE Transactions on Consumer Electronics, 2023. https://doi.org/10.1109/TCE.2023.3341696 doi: 10.1109/TCE.2023.3341696
    [2] Y. Djenouri, D. Djenouri, A. Belhadi, G. Srivastava, J. C. W. Lin, Emergent deep learning for anomaly detection in the Internet of everything, IEEE Internet of Things Journal, 2021.
    [3] M. A. Rahman, M. S. Hossain, A deep learning assisted software defined security architecture for 6G wireless networks: ⅡoT perspective, IEEE Wirel. Commun., 29 (2022), 52–59. https://doi.org/10.1109/MWC.006.2100438 doi: 10.1109/MWC.006.2100438
    [4] S. Bhingarkar, S. T. Revathi, C. S. Kolli, H. K. Mewada, An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing, Int. J. Intell. Robot., 7 (2023), 575–588. https://doi.org/10.1007/s41315-022-00239-x doi: 10.1007/s41315-022-00239-x
    [5] N. Kansal, B. Bhushan, S. Sharma, Architecture, security vulnerabilities, and the proposed countermeasures in Agriculture-Internet-of-Things (AIoT) Systems, Int. Things Anal. Agricul., 3 (2022), 329–353. https://doi.org/10.1007/978-981-16-6210-2_16 doi: 10.1007/978-981-16-6210-2_16
    [6] Q. Aslan, M. Ozkan-Okay, D. Gupta, Intelligent behavior-based malware detection system on cloud computing environment, IEEE Access, 9 (2021), 83252–83271. https://doi.org/10.1109/ACCESS.2021.3087316 doi: 10.1109/ACCESS.2021.3087316
    [7] B. B. Gupta, A. Gaurav, V. Arya, P. Kim, A Deep CNN-based Framework for Distributed Denial of Services (DDoS) Attack Detection in Internet of Things (IoT), In Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems, 2023, 1–6. https://doi.org/10.1145/3599957.3606239 doi: 10.1145/3599957.3606239
    [8] A. Lakhan, M. A. Mohammed, A. N. Rashid, S. Kadry, K. H. Abdulkareem, J. Nedoma, et al., Restricted Boltzmann machine assisted secure serverless edge system for internet of medical things, IEEE J. Biomed. Health, 27 (2022), 673–683. https://doi.org/10.1109/JBHI.2022.3178660 doi: 10.1109/JBHI.2022.3178660
    [9] R. Zhou, X. Zhang, X. Wang, G. Yang, H. N. Dai, M. Liu, Device-Oriented Keyword-Searchable Encryption Scheme for Cloud-Assisted Industrial IoT, IEEE Internet Things, 9 (2021), 17098–17109. https://doi.org/10.1109/JIOT.2021.3124807 doi: 10.1109/JIOT.2021.3124807
    [10] N. Kumar, V. Goel, R. Ranjan, M. Altuwairiqi, H. Alyami, S. A. Asakipaam, A Blockchain-Oriented Framework for Cloud-Assisted System to Countermeasure Phishing for Establishing Secure Smart City, Secur. Commun. Netw., 2023. https://doi.org/10.1155/2023/8168075 doi: 10.1155/2023/8168075
    [11] S. M. Alshahrani, F. S. Alrayes, H. Alqahtani, J. S. Alzahrani, M. Maray, S. Alazwari, et al., IoT-Cloud assisted botnet detection using Rat Swarm Optimizer with deep learning, Comput., Mater. Con., 74 (2023). https://doi.org/10.32604/cmc.2023.032972 doi: 10.32604/cmc.2023.032972
    [12] K. Haseeb, I. U. Din, A. Almogren, I. Ahmed, M. Guizani, Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things, Sustain. Cities Soc., 68 (2021), 102779. https://doi.org/10.1016/j.scs.2021.102779 doi: 10.1016/j.scs.2021.102779
    [13] R. S. Prabhu, A. Prema, E. Perumal, A novel cloud security enhancement scheme to defend against DDoS attacks by using deep learning strategy, In 2022 6th international conference on electronics, communication and aerospace technology, IEEE, 2022,698–704. https://doi.org/10.1109/ICECA55336.2022.10009177
    [14] F. Alrowais, M. M. Eltahir, S. S. Aljameel, R. Marzouk, G. P. Mohammed, A. S. Salama, Modeling of botnet detection using chaotic binary Pelican Optimization Algorithm with deep learning on Internet of Things Environment, IEEE Access, 11 (2023), 130618–130626. https://doi.org/10.1109/ACCESS.2023.3332690 doi: 10.1109/ACCESS.2023.3332690
    [15] M. I. Ahmed, G. Kannan, S. R. Polamuri, LSITA: An Integrated Framework for Leveraging Security of Internet of Things Application with Remote Patient Monitoring System, 2022. https://doi.org/10.21203/rs.3.rs-1948226/v1
    [16] M. Aljebreen, F. S. Alrayes, S. S. Aljameel, M. K. Saeed, Political Optimization Algorithm with a hybrid deep learning assisted Malicious URL detection model, Sustainability, 15 (2023), 16811. https://doi.org/10.3390/su152416811 doi: 10.3390/su152416811
    [17] T. Wang, Q. Yang, X. Shen, T. R. Gadekallu, W. Wang, K. Dev, A privacy-enhanced retrieval technology for the cloud-assisted internet of things, IEEE T. Ind. Inform., 18 (2021), 4981–4989. https://doi.org/10.1109/TII.2021.3103547 doi: 10.1109/TII.2021.3103547
    [18] V. D. Padma, K. Venkata, An adaptive lightweight hybrid encryption scheme for securing the healthcare data in cloud-assisted Internet of Things, Wireless Pers. Commun., 130 (2023), 2959–2980. https://doi.org/10.1007/s11277-023-10411-6 doi: 10.1007/s11277-023-10411-6
    [19] L. Friedman, O. V. Komogortsev, Assessment of the effectiveness of seven biometric feature normalization techniques, IEEE T. Inf. Foren. Sec., 14 (2019), 2528–2536. https://doi.org/10.1109/TIFS.2019.2904844 doi: 10.1109/TIFS.2019.2904844
    [20] B. Crawford, F. Cisternas-Caneo, K. Sepúlveda, R. Soto, A. Paz, A. Peña, et al., B-PSA: A binary Pendulum Search Algorithm for the feature selection problem, Computers, 12 (2023), 249. https://doi.org/10.3390/computers12120249 doi: 10.3390/computers12120249
    [21] H. Gunasekaran, K. Ramalakshmi, D. K. Swaminathan, M. Mazzara, GIT-Net: An ensemble deep learning-based GI tract classification of endoscopic images, Bioengineering, 10 (2023), 809. https://doi.org/10.3390/bioengineering10070809 doi: 10.3390/bioengineering10070809
    [22] M. Vellaisamy, L. I. Freitas, Detection of human stress using optimized feature selection and classification in ECG signals, Math. Probl. Eng., 2023. https://doi.org/10.1155/2023/3356347 doi: 10.1155/2023/3356347
    [23] R. Keyimu, W. Tuerxun, Y. Feng, B. Tu, Hospital outpatient volume prediction model based on gated recurrent unit optimized by the modified cheetah optimizer, IEEE Access, 2023. https://doi.org/10.1109/ACCESS.2023.3339613 doi: 10.1109/ACCESS.2023.3339613
    [24] J. Sun, R. Cao, M. Zhou, W. Hussain, B. Wang, J. Xue, et al., A hybrid deep neural network for classification of schizophrenia using EEG Data, Sci. Rep., 11 (2021), 4706. https://doi.org/10.1038/s41598-021-83350-6 doi: 10.1038/s41598-021-83350-6
    [25] M. Salb, L. Jovanovic, N. Bacanin, M. Antonijevic, M. Zivkovic, N. Budimirovic, et al., Enhancing Internet of Things network security using hybrid CNN and XGBoost model tuned via modified Reptile Search Algorithm, Appl. Sci., 13 (2023), 12687. https://doi.org/10.3390/app132312687 doi: 10.3390/app132312687
    [26] Y. Meidan, M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, et al., N-BaIoT—Network-based detection of IoT botnet attacks using deep autoencoders, IEEE Pervas. Comput., 17 (2018), 12–22. https://doi.org/10.1109/MPRV.2018.03367731 doi: 10.1109/MPRV.2018.03367731
    [27] L. Almuqren, H. Alqahtani, S. S. Aljameel, A. S. Salama, I. Yaseen, A. A. Alneil, Hybrid Metaheuristics with Machine Learning based Botnet Detection in Cloud Assisted Internet of Things Environment, IEEE Access, 2023. https://doi.org/10.1109/ACCESS.2023.3322369 doi: 10.1109/ACCESS.2023.3322369
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