Weighted salp swarm algorithm with deep learning-powered cyber-threat detection for robust network security
The fast development of the internet of things has been associated with the complex worldwide problem of protecting interconnected devices and networks. The protection of cyber security is becoming increasingly complicated due to the enormous growth in computer connectivity and the number of new app...
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| Vydáno v: | AIMS mathematics Ročník 9; číslo 7; s. 17676 - 17695 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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AIMS Press
01.01.2024
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| ISSN: | 2473-6988, 2473-6988 |
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| Abstract | The fast development of the internet of things has been associated with the complex worldwide problem of protecting interconnected devices and networks. The protection of cyber security is becoming increasingly complicated due to the enormous growth in computer connectivity and the number of new applications related to computers. Consequently, emerging intrusion detection systems could execute a potential cyber security function to identify attacks and variations in computer networks. An efficient data-driven intrusion detection system can be generated utilizing artificial intelligence, especially machine learning methods. Deep learning methods offer advanced methodologies for identifying abnormalities in network traffic efficiently. Therefore, this article introduced a weighted salp swarm algorithm with deep learning-powered cyber-threat detection and classification (WSSADL-CTDC) technique for robust network security, with the aim of detecting the presence of cyber threats, keeping networks secure using metaheuristics with deep learning models, and implementing a min-max normalization approach to scale the data into a uniform format to accomplish this. In addition, the WSSADL-CTDC technique applied the shuffled frog leap algorithm (SFLA) to elect an optimum subset of features and applied a hybrid convolutional autoencoder (CAE) model for cyber threat detection and classification. A WSSA-based hyperparameter tuning method can be employed to enhance the detection performance of the CAE model. The simulation results of the WSSADL-CTDC system were examined in the benchmark dataset. The extensive analysis of the accuracy of the results found that the WSSADL-CTDC technique exhibited a better value of 99.13% than comparable methods on different measures. |
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| AbstractList | The fast development of the internet of things has been associated with the complex worldwide problem of protecting interconnected devices and networks. The protection of cyber security is becoming increasingly complicated due to the enormous growth in computer connectivity and the number of new applications related to computers. Consequently, emerging intrusion detection systems could execute a potential cyber security function to identify attacks and variations in computer networks. An efficient data-driven intrusion detection system can be generated utilizing artificial intelligence, especially machine learning methods. Deep learning methods offer advanced methodologies for identifying abnormalities in network traffic efficiently. Therefore, this article introduced a weighted salp swarm algorithm with deep learning-powered cyber-threat detection and classification (WSSADL-CTDC) technique for robust network security, with the aim of detecting the presence of cyber threats, keeping networks secure using metaheuristics with deep learning models, and implementing a min-max normalization approach to scale the data into a uniform format to accomplish this. In addition, the WSSADL-CTDC technique applied the shuffled frog leap algorithm (SFLA) to elect an optimum subset of features and applied a hybrid convolutional autoencoder (CAE) model for cyber threat detection and classification. A WSSA-based hyperparameter tuning method can be employed to enhance the detection performance of the CAE model. The simulation results of the WSSADL-CTDC system were examined in the benchmark dataset. The extensive analysis of the accuracy of the results found that the WSSADL-CTDC technique exhibited a better value of 99.13% than comparable methods on different measures. |
| Author | Escorcia-Gutierrez, José Althobaiti, Maha M. |
| Author_xml | – sequence: 1 givenname: Maha M. surname: Althobaiti fullname: Althobaiti, Maha M. organization: Department of Computer Science, College of Computing and Information Technology, Taif University, Taif, 21944, Saudi Arabia – sequence: 2 givenname: José surname: Escorcia-Gutierrez fullname: Escorcia-Gutierrez, José organization: Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia |
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| Cites_doi | 10.1016/j.compbiomed.2023.107389 10.1109/DCOSS-IoT58021.2023.00042 10.3390/su151612563 10.1109/COMST.2021.3127267 10.1109/JIOT.2021.3130156 10.1016/S0168-9274(96)00048-7 10.1007/s13201-022-01815-z 10.1109/IEC52205.2021.9476132 10.1109/ACCESS.2021.3091427 10.1016/j.compbiomed.2023.106659 10.32604/csse.2023.030188 10.1016/j.aej.2023.12.018 10.1016/j.aiia.2021.05.002 10.1109/ACCESS.2021.3118642 10.22667/JISIS.2022.05.31.095 10.1007/s00779-021-01578-5 10.3390/s22041396 10.1007/s12293-016-0212-3 10.1155/2022/2608798 10.1109/TNNLS.2018.2868980 10.1016/j.future.2020.03.055 10.1016/j.neucom.2023.126467 10.1109/JIOT.2022.3229722 10.1016/j.iot.2021.100365 10.1016/j.compbiomed.2023.107838 10.1016/j.jksuci.2019.04.002 10.1016/j.dcan.2022.09.008 10.1109/JIOT.2021.3106898 10.1016/j.neucom.2018.09.093 10.1016/j.jksuci.2019.07.005 10.1007/s42235-021-0050-y 10.1016/j.eswa.2021.114864 10.1016/j.neucom.2023.02.010 10.1109/MPRV.2018.03367731 10.1016/j.cosrev.2020.100317 10.1016/j.scs.2020.102655 10.1016/j.eswa.2022.116516 10.1093/comjnl/bxy144 10.1016/j.future.2019.02.028 10.1007/s10489-022-04427-x 10.1016/j.knosys.2018.10.029 |
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| SubjectTerms | convolutional autoencoder cyber threat intrusion detection systems network security salp swarm algorithm |
| Title | Weighted salp swarm algorithm with deep learning-powered cyber-threat detection for robust network security |
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