Open Access
ARTICLE
An Optimized Approach to Deep Learning for Botnet Detection and Classification for Cybersecurity in Internet of Things Environment
Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin, 39524, Saudi Arabia
* Corresponding Author: Abdulrahman Alzahrani. Email:
Computers, Materials & Continua 2024, 80(2), 2331-2349. https://doi.org/10.32604/cmc.2024.052804
Received 16 April 2024; Accepted 28 June 2024; Issue published 15 August 2024
Abstract
The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets. Various ML and deep learning (DL) techniques have been developed to detect botnet attacks in IoT systems. To overcome security issues in an IoT environment, this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification (GTODL-BADC) technique. The GTODL-BADC technique follows feature selection (FS) with optimal DL-based classification for accomplishing security in an IoT environment. For data preprocessing, the min-max data normalization approach is primarily used. The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets. Moreover, the multi-head attention-based long short-term memory (MHA-LSTM) technique was applied for botnet detection. Finally, the tree seed algorithm (TSA) was used to select the optimum hyperparameter for the MHA-LSTM method. The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset. The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.Keywords
The production of IoT devices caused the stable growth of IoT-based attacks. Currently, dangerous IoT risks are nothing but IoT Botnet attacks that attempt to pledge effective, profitable and actual cybercrimes [1]. IoT botnets are groups of Internet-connected IoT devices infected by malware and accomplished slightly by attackers. IoT networks have essential tasks in providing models to identify safety vulnerabilities and attacks owing to the fast development of threats and the assortment of attack strategies [2]. If malware is implemented, there will be a growing number of advances in DL/ML based recognition models that use full-time series data. However, there is a requirement to employ full-time series data harshly parameters present functions efficacy [3]. In addition, early identification permits enhanced IoT Botnet response suggestions. As an outcome, it reduces injuries that are affected by probable assaults. The dynamic analysis method surveys how malware relates to its atmospheres when executed [4]. The use of botnets and bot malware supports other dangerous online actions like distributed denial of service assaults, click scams, and spam and virus distribution. IoT Botnet development contains propagation and extensive scan stage [5]. If it is viable to diagnose and distinguish bots beforehand, they initiate a definite assault, namely DDoS; IoT Botnet recognition solutions have a harsher effect. So, it is vital to classify dangerous activities of IoT Botnet modules as much as possible.
A botnet attack is one of the severe attacks recognized for spreading quickly among devices linked to the Internet [6]. There are chief gaps in prior techniques for discovering suitable and effective mechanisms to defend IoT devices from botnet assaults [7]. An IDS is the only dominant solution for dealing with botnet attacks. It utilizes artificial intelligence (AI) to discover novel botnet attack designs. An IDS is separated into dual kinds such as misuse and anomaly models [8]. These types are highly based on being signature-based. Many IDSs, like Suricata and Snort, are obtainable. AI techniques are employed to identify IoT attacks with further assured recognition. AI techniques can discover alterations in networks and approaches to attacks. This was one of the high tasks tackled by security solutions to handle IoT attacks [9]. Generally, hackers make slight variations in preceding attacks that security solutions cannot identify. Numerous researchers employ AI methods to prevent threats to the IoT environment by examining network traffic. DL and ML models are built into security systems to discover such assaults proficiently. DL is one of the AI developments used in real time to handle complex nonlinear data [10]. A deep recurrent neural network (DRNN) is executed to detect botnet assaults from IoT devices.
This article designs a gorilla troops optimizer with a DL-enabled botnet attack detection and classification (GTODL-BADC) technique. The GTODL-BADC technique follows feature selection (FS) with optimal DL-based classification for accomplishing security in the IoT environment. For data preprocessing, the min-max data normalization approach is primarily used. The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets. Moreover, multi-head attention-based long short-term memory (MHA-LSTM) methodology is applied for botnet detection. Finally, the tree seed algorithm (TSA) can select the optimum hyperparameter for the MHA-LSTM technique. The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.
In [11], an innovative lightweight and generic NIDS with a 2-phase architecture was designed. This technique initially developed 21 statistical features, and depending on these features, a model has been devised according to an AE for filtering. Next, a new technique was developed to convert packet length sequences like a 3-channel RGB image for detection dependent upon a lightweight CNN technique. Hezam et al. [12] developed a DL method that includes 3 DL methods, such as CNN, LSTM-RNN, and RNN, to combat DDoS attack-targeted IoT environments. The methods are examined by applying an N-BaIoT database, which could be gathered by affecting nine IoT devices with two major serious DDoS botnets such as Mirai and BASHLITE. Haq et al. [13] designed two innovative architectures namely Deep Neural Network (DNN), DNNBoT1 and DNNBoT2, for identifying and categorizing botnet attacks, namely BASHLITE and Mirai. The application of PCA has been accomplished to feature extraction. The system could be presented depending on rigorous hyperparameter tuning with GridsearchCV. Khan et al. [14] considered a lightweight and robust DL method. This technique was to exhibit the scalability and attack detection effectiveness employed for training as well as testing. Besides, the developed Hybrid system was related to a benchmark Artificial Neural Network (ANN) model. In [15], the federated DL (FDL) technique was developed for zero-day botnet attack detection. An optimum DNN method was utilized for classification. A method parameter supports remote controls of the self-sufficient DNN architecture training at numerous IoT-edge devices. However, the federated averaging (FedAvg) technique could be exploited to combine local model updates. A global DNN was generated, followed by a count of communication iterations among the IoT-edge devices and architecture parameter server. Hasan et al. [16] planned a hybrid intelligent DL approach for protecting the IIoT environment from dangerous and difficult multi-variant Botnet attacks. The developed method was severely analyzed with a new database, normal and comprehensive efficiency assessment metrics, and standard DL methods. Also, cross-validation of these outcomes was further executed to exhibit overall effectiveness.
In [17], an ARP spoofing identification method was developed by applying an explainable DL method such as ARP-PROBE for IoT networks. This introduced algorithm depends on features removed in network packets for identifying ARP spoofing rapidly and efficiently employing an FS and extraction model, which recognizes and chooses the extremely significant features. In [18], cooperative game theory incorporating three methods, namely LSTM, AE, and SVM, has been implemented to recognize IoT botnet attacks. The developed methods depend upon the efficient FS through cooperative game theory and shapely values under a database collected at a 5 IoT device attacked with botnets and employing AE, LSTM, and SVM for recognizing IoT Botnet traffic. Nazir et al. [19] aimed to detect effectual ML and DL models for IoT Botnet recognition by evaluating standard datasets, metrics, and preprocessing models. In [20], a novel model by joining collaborative threat intelligence and blockchain (BC) technology with ML methods, this model also utilizes Random Forest (RF), Decision Tree (DT) classifier, Ensemble, CNN, and LSTM methods. Abualigah et al. [21] proposed a novel IPDOA model, which enhances the search procedure of the Prairie Dog Optimization Algorithm (PDOA) by integrating the initial upgrading mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). Sangaiah et al. [22] incorporated linear correlation feature selection techniques utilizing INTERACT and MLP, suggesting the uninterrupted employment of data balancing approaches. Javadpour et al. [23] proposed a novel distributed multi-agent IDPS (DMAIDPS) model, where learning agents execute a six-step recognition procedure for classifying network behaviour. Several DL methods comprising CNN, LSTM-RNN, and DNN, along with a rigorous hyperparameter tuning process, are utilized for detecting IoT botnets, while federated DL models and fusion intellectual DL methods improve cybersecurity in IIoT environments by accentuating effectual FS and model optimization.
This article designs a novel GTODL-BADC technique. The technique follows FS with optimal DL-based classification for accomplishing security in the IoT environment. It comprises four main processes: min-max data normalization, GTO-based feature subset selection, MHA-LSTM-based classification, and TSA-based hyperparameter tuning. Fig. 1 illustrates the entire flow of the technique.
Figure 1: Overall flow of GTODL-BADC technique
For data preprocessing, the min-max data normalization approach is primarily used. Min-max normalization is a critical preprocessing method applied in botnet recognition in IoT atmospheres to normalize and measure sensor data features [24]. This technique changes raw sensor values into a shared range, naturally among 0 and 1, by deducting the least value and separating by range (difference amid maximal and minimal values). This normalization safeguards that numerous sensor readings with dissimilar measures are carried to an even scale, permitting actual comparison and analysis. In botnet recognition, this standardized data becomes helpful for training ML methods. By simplifying consistent feature representation through various IoT devices, min-max normalization donates to creating robust methods to classify abnormal patterns related to potential botnet actions through multiple devices and sensors.
The GTODL-BADC technique uses the GTO model to elect optimal feature subsets at this stage. The GTO model relies entirely on many separate performances of gorillas that are arithmetically replicated. Five behaviours are taken in this state to improve gorillas’ behaviour, such as 3 for the exploration and 2 for the exploitation phases [25]. These actions include migration to a weird area, migration to other gorillas, travel near a definite spot, challenges for adult females and conducting silverback. The 2 phases signify the mentioned planned choices which are separated into exploitation as well as exploration phase demonstrated in the following sub-sections.
Exploration stage. In this stage, three distinct behaviors are explained: the 1st one is to manifest GTO exploration, whereas 2nd tactic signifies migrant behaviour to other gorillas. Besides, 3rd plan goals at cheering GTO’s abilities in defining countless computing spaces denotes movement near a definite spot. Eq. (11) signifies three behaviours arithmetically, where action to unknown endpoint approach in this equation. Suppose an arbitrary number
Whereas
Exploitation phase: 2 strategies are projected in this phase when factor
Whereas
If the value of
where
If the fitness value of
Enhanced GTO combining tangent flight approach. An improved GTO (IGTO) includes this section’s Tangent Flight Strategy (TFS). Cauchy calculated below, and its tangent function is similar to TFS:
Meanwhile,
The fitness function (FF) reflects classification accuracy and the number of nominated features. It increases classification accuracy and reduces the set size of the nominated feature. So, FF is employed to estimate individual solutions as given in Eq. (13).
Whereas ErrorRate denotes the classification error rate employing a particular feature. ErrorRate is intended as a percentage of improper categorized to the amount of classification prepared, conveyed as a value amid 0 and 1 (ErrorRate is the complement of classification accuracy),
3.3 Botnet Detection Using MHA-LSTM
For the classification process, the MHA-LSTM model can be applied. LSTM is comprised of a memory unit called a cell
Eq. (14) signifies the entry-wise multiplication of prior data and present input that relies on the existing values of the forget gate. Zero and non-zero values of the forget gate imply throwing away and passing the data individually. At the same time, input implements data and keeps it in a memory unit. Next, the input gate (
Lastly, the new memory unit is combined with the output gate to determine the existing value of LSTM, in which the output gate exploits sigmoid activation to elect which condition in the existing cell serves as an outcome and the novel memory unit exploits
Among the difficulties experienced in this field is the capability to address tasks with longer-term dependency. LSTM is an effective model for forecasting accurate time series. Despite addressing challenges such as gradient expansion and vanishing problems, LSTM is widely adopted for applications that heavily depend on prior information.
MHA-LSTM is a refined neural network structure that integrates the strength of multi-head attention mechanisms and LSTM. In this hybrid method, multi-head attention is combined into the LSTM framework to increase the network’s capability to capture longer-range needs and instantly appear to dissimilar portions of the input sequence. Fig. 2 depicts the infrastructure of MHA-LSTM.
Figure 2: Architecture of MHA-LSTM
The multi-head attention mechanism permits the method to concentrate on dissimilar places within the input sequence in parallel, allowing it to capture difficult relationships and dependencies more efficiently. This is mainly beneficial for challenges involving sequential data where definite elements have varying levels of significance at dissimilar time steps.
Finally, the TSA can be applied to optimize the hyperparameter selection of the MHA-LSTM model. The TSA is simulated by nature, as presented by Kıran in 2015 [27]. TSA has designed the connection of positions of seeds and trees from searching space. An optimum tree in population or arbitrarily elected tree position was utilized for all seed productions. An essential parameter of the TSA technique is the ST control parameter. This parameter ensures a variety of seed production. This variety has been recognized by employing the formulas in Eqs. (19) and (20). Once the arbitrarily elected number is lesser than the ST parameter value, the 1st formula is utilized, and once it is greater, the 2nd formula is employed.
Meanwhile,
In this case,
Fitness selection is a considerable factor influencing the performance of TSA. The hyperparameter selection procedure includes a solution encoding method to estimate the effectiveness of candidate solutions. In this work, TSA reflects accuracy as the main principle for designing FF, as expressed below:
From the above mentioned expression, TP and FP signify true positive and false positive values, respectively.
4 Result Analysis and Discussion
This section examines the performance of the GTODL-BADC technique under the Bot-IoT Database [28]. It includes 900 samples and two classes, as represented in Table 1.
Fig. 3 displays the confusion matrices accomplished by the GTODL-BADC method under 80:20 and 70:30 of the training phase (TRPH)/testing phase (TSPH). The attained outcomes indicate proficient recognition under two classes.
Figure 3: Confusion matrices of GTODL-BADC model (a, b) 80:20 TRPH/TSPH and (c, d) 70:30 of TRPH/TSPH
In Table 2 and Fig. 4, the botnet recognition analysis of the GTODL-BADC technique can be illustrated on 80:20 of TRPH/TSPH. The results depict that the GTODL-BADC technique achieves effectual botnet detection results. With 80% of TRPH, the GTODL-BADC technique gains average
Figure 4: Average outcomes of the GTODL-BADC approach on 80:20 TRPH/TSPH
Table 3 and Fig. 5 show the botnet recognition analysis of the GTODL-BADC technique under 70:30 of TRPH/TSPH. The acquired outcomes show that the GTODL-BADC technique gets successful botnet detection outcomes. According to 70% of TRPH, the GTODL-BADC technique achieves average
Figure 5: Average outcome of the GTODL-BADC approach with 70:30 TRPH/TSPH
The
Figure 6:
Fig. 7 specifies a wide-ranging overview of the TR and TS loss values to the GTODL-BADC methodology with 70:30 TRPH/TSPH in diverse epochs. This TR loss constantly lessened as the model grew in weight to reduce classification errors with these datasets. These loss curves considerably indicate the model’s alignment with the TR database, underscoring proficiencies for capturing patterns. The continuous parameters are modified in the GTODL-BADC technique to minimize discrepancies between actual and predicted TR labels.
Figure 7: Loss curve of the GTODL-BADC approach on 70:30 TRPH/TSPH
As regards the PR curve shown in Fig. 8, the findings confirm that the GTODL-BADC technique on 70:30 TRPH/TSPH reliably achieves boosted PR values in every class. These outcomes underscore the model’s efficient capacity for discerning among many classes, emphasizing its effectiveness in recognizing class labels precisely.
Figure 8: PR curve of the GTODL-BADC approach under 70:30 TRPH/TSPH
Additionally, Fig. 9 reveals ROC curves generated by the GTODL-BADC technique with 70:30 TRPH/TSPH, signifying its proficiency in differentiating amongst classes. These curves give valued insights into how the trade-off between FPR and TPR varied at diverse classification epochs and thresholds. The acquired outcomes emphasize the model’s exact classification efficiency in diverse class labels, emphasizing its effectiveness in addressing several classification challenges.
Figure 9: ROC curve of the GTODL-BADC approach with 70:30 TRPH/TSPH
Table 4 demonstrates a comparison analysis of the GTODL-BADC technique with recent approaches [29]. In Fig. 10, a brief analysis of the GTODL-BADC technique in terms of
Figure 10:
In Fig. 11, a comprehensive analysis of the GTODL-BADC technique concerning
Figure 11: Comparative analysis of the GTODL-BADC approach with other models
These achieved results ensured the accurate and automated botnet detection results of the GTODL-BADC technique.
In this article, a novel GTODL-BADC methodology is presented. The GTODL-BADC methodology follows FS with optimal DL-based classification for accomplishing security in an IoT environment. For data preprocessing, the min-max data normalization approach is primarily used. The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets. Moreover, the MHA-LSTM-based classification model can be applied for botnet detection. Finally, TSA can be used to select the optimum hyperparameter for the MHA-LSTM technique. The experimental validation of the GTODL-BADC technique was tested on a benchmark dataset. The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process. The GTODL-BADC approach may comprise scalability threats with large-scale IoT utilization and the requirement for additional analysis across various IoT environments. Future studies may explore incorporating further security layers and improving real-time threat response abilities to reduce growing botnet outbreaks effectually.
Acknowledgement: None.
Funding Statement: None.
Availability of Data and Materials: Data sharing does not apply to this article as no dataset were generated during the current study.
Conflicts of Interest: The author declares that he has no conflict of interest.
References
1. S. Alrayes et al., “Modelling of botnet detection using barnacles mating optimizer with machine learning model for internet of things environment,” Electronics, vol. 11, no. 20, pp. 3411, 2022. doi: 10.3390/electronics11203411. [Google Scholar] [CrossRef]
2. W. Ding, M. Abdel-Basset, and R. Mohamed, “DeepAK-IoT: An effective deep learning model for cyberattack detection in IoT networks,” Inf. Sci., vol. 634, no. 3, pp. 157–171, 2023. doi: 10.1016/j.ins.2023.03.052. [Google Scholar] [CrossRef]
3. R. Sudhakar and S. Kumar, “ABBDIoT: Anomaly-based botnet detection using machine learning model in the internet of things network,” in Int. Conf. IoT, Intell. Comput. Secur.: Sel. Proc. IICS 2021, Singapore, Springer Nature Singapore, 2023, pp. 235–245. [Google Scholar]
4. R. Kalakoti, S. Nõmm, and H. Bahsi, “In-depth feature selection for the statistical machine learning-based botnet detection in IoT networks,” IEEE Access, vol. 10, pp. 94518–94535, 2022. doi: 10.1109/ACCESS.2022.3204001. [Google Scholar] [CrossRef]
5. M. Sabir, J. Ahmad, and D. M. Alghazzawi, “A lightweight deep autoencoder scheme for cyberattack detection in the internet of things,” Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 57–72, 2023. doi: 10.32604/csse.2023.034277. [Google Scholar] [CrossRef]
6. I. Apostol, M. Preda, C. Nila, and I. Bica, “IoT botnet anomaly detection using unsupervised deep learning,” Electronics, vol. 10, no. 16, pp. 1876, 2021. doi: 10.3390/electronics10161876. [Google Scholar] [CrossRef]
7. Y. Masoudi-Sobhanzadeh and S. Emami-Moghaddam, “A realtime IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier,” Comput. Netw., vol. 217, pp. 109365, 2022. doi: 10.1016/j.comnet.2022.109365. [Google Scholar] [CrossRef]
8. M. Cui, K. Wang, X. Ding, Z. Xu, X. Wang and P. Shi, “Multi-view stable feature selection with adaptive optimization of view weights,” Knowl.-Based Syst., vol. 299, no. 10, pp. 111970, 2024. doi: 10.1016/j.knosys.2024.111970. [Google Scholar] [CrossRef]
9. M. M. Alani, “BotStop: Packet-based efficient and explainable IoT botnet detection using machine learning,” Comput. Commun., vol. 193, no. 7, pp. 53–62, 2022. doi: 10.1016/j.comcom.2022.06.039. [Google Scholar] [CrossRef]
10. G. Kirubavathi and U. K. Sridevi, “Detection of IoT botnet using machine learning and deep learning techniques,” 2023. doi: 10.21203/rs.3.rs-2630988/v1. [Google Scholar] [CrossRef]
11. C. Wei, G. Xie, and Z. Diao, “A lightweight deep learning framework for botnet detecting at the IoT edge,” Comput. Secur., vol. 129, pp. 103195, 2023. [Google Scholar]
12. A. A. Hezam, S. A. Mostafa, A. A. Ramli, H. Mahdin, and B. A. Khalaf, “Deep learning approach for detecting botnet attacks in IoT environment of multiple and heterogeneous sensors,” in Int. Conf. Adv. Cyber Secur., Singapore, Springer Singapore, Jul. 28–31 2021, 2021, pp. 317–328. [Google Scholar]
13. M. A. Haq and M. A. R. Khan, “DNNBoT: Deep neural network-based botnet detection and classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1729–1750, 2022. doi: 10.32604/cmc.2022.020938. [Google Scholar] [CrossRef]
14. S. Khan and A. B. Mailewa, “Discover botnets in IoT sensor networks: A lightweight deep learning framework with hybrid self-organizing maps,” Microprocess. Microsyst., vol. 97, pp. 104753, 2023. [Google Scholar]
15. S. I. Popoola, R. Ande, B. Adebisi, G. Gui, M. Hammoudeh and O. Jogunola, “Federated deep learning for zero-day botnet attack detection in IoT-edge devices,” IEEE Internet Things J., vol. 9, no. 5, pp. 3930–3944, 2021. [Google Scholar]
16. T. Hasan et al., “Securing industrial Internet of Things against botnet attacks using hybrid deep learning approach,” IEEE Trans. Netw. Sci. Eng., vol. 10, pp. 5, 2022. [Google Scholar]
17. M. M. Alani, A. I. Awad, and E. Barka, “ARP-PROBE: An ARP spoofing detector for Internet of Things networks using explainable deep learning,” Internet of Things, vol. 23, pp. 100861, 2023. [Google Scholar]
18. M. Asadi, “Detecting IoT botnets based on the combination of cooperative game theory with deep and machine learning approaches,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 12, pp. 5547–5561, 2022. doi: 10.1007/s12652-021-03185-x. [Google Scholar] [CrossRef]
19. A. Nazir et al., “Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets,” J. King Saud Univ.—Comput. Inf. Sci., vol. 35, no. 10, pp. 101820, 2023. doi: 10.1016/j.jksuci.2023.101820. [Google Scholar] [CrossRef]
20. A. Nazir et al., “Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration,” J. King Saud Univ.—Comput. Inf. Sci., vol. 36, no. 2, pp. 101939, 2024. doi: 10.1016/j.jksuci.2024.101939. [Google Scholar] [CrossRef]
21. L. Abualigah et al., “Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems,” Multimed. Tools Appl., vol. 83, no. 11, pp. 32613–32653, 2024. doi: 10.1007/s11042-023-16890-w. [Google Scholar] [CrossRef]
22. A. K. Sangaiah, A. Javadpour, and P. Pinto, “Towards data security assessments using an IDS security model for cyber-physical smart cities,” Inf. Sci., vol. 648, no. 1, pp. 119530, 2023. doi: 10.1016/j.ins.2023.119530. [Google Scholar] [CrossRef]
23. A. Javadpour, P. Pinto, D. Ja’fari, and W. Zhang, “DMAIDPS: A distributed multi-agent intrusion detection and prevention system for cloud IoT environments,” Cluster Comput., vol. 26, no. 1, pp. 367–384, 2023. doi: 10.1007/s10586-022-03621-3. [Google Scholar] [CrossRef]
24. N. A. Hikal and M. M. Elgayar, “Enhancing IoT botnets attack detection using machine learning-IDS and ensemble data preprocessing technique,” in Internet of Things—Appl. Future: Proc. ITAF 2019, Singapore, Springer Singapore, 2020, pp. 89–102. [Google Scholar]
25. A. Shaheen, R. El-Sehiemy, A. El-Fergany, and A. Ginidi, “Fuel-cell parameter estimation based on improved gorilla troops technique,” Sci. Rep., vol. 13, no. 1, pp. 8685, 2023. doi: 10.1038/s41598-023-35581-y. [Google Scholar] [PubMed] [CrossRef]
26. F. Shahid et al., “1D convolutional LSTM-based wind power prediction integrated with PkNN data imputation technique,” J. King Saud Univ.—Comput. Inf. Sci., vol. 35, no. 10, pp. 101816, 2023. doi: 10.1016/j.jksuci.2023.101816. [Google Scholar] [CrossRef]
27. M. Beşkirli and M. S. Kiran, “Optimization of butterworth and bessel filter parameters with improved tree-seed algorithm,” Biomimetics, vol. 8, no. 7, pp. 540, 2023. doi: 10.3390/biomimetics8070540. [Google Scholar] [PubMed] [CrossRef]
28. The Bot-IoT Dataset. Accessed: Jan. 12, 2024. [Online]. Available: https://research.unsw.edu.au/projects/bot-iot-dataset [Google Scholar]
29. F. Alrowais, M. M. Eltahir, S. S. Aljameel, R. Marzouk, G. P. Mohammed and A. S. Salama, “Modelling of botnet detection using chaotic binary pelican optimization algorithm with deep learning on internet of things environment,” IEEE Access, vol. 11, pp. 130618–130626, 2023. doi: 10.1109/ACCESS.2023.3332690. [Google Scholar] [CrossRef]
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