Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features
•Remora Whale Optimization Based-Hybrid deep model is used for detecting intrusions.•RWO is designed by the combination of ROA and WOA.•In the proposed approach features are selected by holoentropy process.•The developed approach achieves superior performance with testing accuracy of 0.93. Security...
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| Vydáno v: | Expert systems with applications Ročník 210; s. 118476 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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30.12.2022
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •Remora Whale Optimization Based-Hybrid deep model is used for detecting intrusions.•RWO is designed by the combination of ROA and WOA.•In the proposed approach features are selected by holoentropy process.•The developed approach achieves superior performance with testing accuracy of 0.93.
Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora Whale Optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1-score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively. |
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| AbstractList | •Remora Whale Optimization Based-Hybrid deep model is used for detecting intrusions.•RWO is designed by the combination of ROA and WOA.•In the proposed approach features are selected by holoentropy process.•The developed approach achieves superior performance with testing accuracy of 0.93.
Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora Whale Optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1-score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively. |
| ArticleNumber | 118476 |
| Author | Pingale, Subhash V. Sutar, Sanjay R. |
| Author_xml | – sequence: 1 givenname: Subhash V. surname: Pingale fullname: Pingale, Subhash V. email: Subhash.pingale@sknscoe.ac.in – sequence: 2 givenname: Sanjay R. surname: Sutar fullname: Sutar, Sanjay R. email: srsutar@dbatu.ac.in |
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| Cites_doi | 10.1007/s00500-020-05017-0 10.1109/ACCESS.2020.2972627 10.1109/ACCESS.2020.3048198 10.1016/j.jisa.2021.102804 10.1016/j.jnca.2017.02.009 10.1007/s10207-020-00508-5 10.1109/ACCESS.2018.2868993 10.1109/COMST.2015.2494502 10.1016/j.eswa.2021.115524 10.1016/j.eswa.2021.115665 10.1016/j.eswa.2005.05.002 10.1155/2018/3029638 10.1016/j.compeleceng.2021.107044 10.1016/j.advengsoft.2016.01.008 10.1016/j.cose.2020.101752 10.3390/s21020626 |
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infrastructure: A cross-layer feature-fusion CNN-LSTM-based approach publication-title: Sensors doi: 10.3390/s21020626 |
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