Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features

In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious ac...

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Veröffentlicht in:	Wireless networks Jg. 31; H. 2; S. 1255 - 1278
Hauptverfasser:	Rani, Y. Alekya, Reddy, E. Sreenivasa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2025 Springer Nature B.V
Schlagworte:	Algorithms Communications Engineering Computer Communication Networks Control systems Cybersecurity Data transmission Deep learning Electrical Engineering Engineering Heuristic methods Intrusion detection systems IT in Business Machine learning Networks Optimization Original Paper Performance enhancement Robust control Standard data Target detection Auto encoder Gated recurrent unit Detected intrusion outcomes Deep intrusion net Intrusion detection Improved chimp optimization algorithm Deep temporal convolution network
ISSN:	1022-0038, 1572-8196
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Abstract	In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious activities or threats as well as protect the insecurity among individual privacy. Moreover, many existing research works are explored to detect the network intrusion model but it fails to protect the target network efficiently based on the statistical features. A major issue in the designed model is regarded as the robustness or generalization that has the capability to control the working performance when the data is attained from various distributions. To handle all the difficulties, a new meta-heuristic hybrid-based deep learning model is introduced to detect the intrusion. Initially, the input data is garnered from the standard data sources. It is then undergone the pre-processing phase, which is accomplished through duplicate removal, replacing the NAN values, and normalization. With the resultant of pre-processed data, the auto encoder is utilized for extracting the significant features. To further improve the performance, it requires choosing the optimal features with the help of an Improved chimp optimization algorithm known as IChOA. Subsequently, the optimal features are subjected to the newly developed hybrid deep learning model. The hybrid model is built by incorporating the deep temporal convolution network and gated recurrent unit, and it is termed as DINet, in which the hyper parameters are tuned by an improved IChOA algorithm for attaining optimal solutions. Finally, the proposed detection model is evaluated and compared with the former detection approaches. The analysis shows the developed model is suggested to provide 97% in terms of accuracy and precision. Thus, the enhanced model elucidates that to effectively detect malware, which tends to improve data transmission significantly and securely.
AbstractList	In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious activities or threats as well as protect the insecurity among individual privacy. Moreover, many existing research works are explored to detect the network intrusion model but it fails to protect the target network efficiently based on the statistical features. A major issue in the designed model is regarded as the robustness or generalization that has the capability to control the working performance when the data is attained from various distributions. To handle all the difficulties, a new meta-heuristic hybrid-based deep learning model is introduced to detect the intrusion. Initially, the input data is garnered from the standard data sources. It is then undergone the pre-processing phase, which is accomplished through duplicate removal, replacing the NAN values, and normalization. With the resultant of pre-processed data, the auto encoder is utilized for extracting the significant features. To further improve the performance, it requires choosing the optimal features with the help of an Improved chimp optimization algorithm known as IChOA. Subsequently, the optimal features are subjected to the newly developed hybrid deep learning model. The hybrid model is built by incorporating the deep temporal convolution network and gated recurrent unit, and it is termed as DINet, in which the hyper parameters are tuned by an improved IChOA algorithm for attaining optimal solutions. Finally, the proposed detection model is evaluated and compared with the former detection approaches. The analysis shows the developed model is suggested to provide 97% in terms of accuracy and precision. Thus, the enhanced model elucidates that to effectively detect malware, which tends to improve data transmission significantly and securely.
Author	Rani, Y. Alekya Reddy, E. Sreenivasa
Author_xml	– sequence: 1 givenname: Y. Alekya surname: Rani fullname: Rani, Y. Alekya email: alekya.rani14@gmail.com organization: Computer Science Engineering (AI & ML), CVR College of Engineering – sequence: 2 givenname: E. Sreenivasa surname: Reddy fullname: Reddy, E. Sreenivasa organization: Computer Science Engineering, Acharya Nagarjuna University
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Cites_doi	10.1109/ICIT58056.2023.10226123 10.1109/TITS.2020.3027390 10.1016/j.procs.2018.04.298 10.1016/j.comnet.2021.108117 10.1016/j.eswa.2020.113338 10.1109/ACCESS.2019.2933165 10.1109/ACCESS.2021.3051074 10.1016/j.jisa.2021.102899 10.1109/ACCESS.2019.2923814 10.1016/j.cose.2023.103567 10.1016/j.compag.2023.108583 10.1016/j.eswa.2021.115782 10.1109/ACCESS.2019.2905633 10.1109/TSMC.2020.2968516 10.1109/TITS.2021.3055351 10.1016/j.procs.2018.05.069 10.1109/TCSS.2021.3063538 10.1109/ACCESS.2020.2972627 10.1109/ACCESS.2019.2899721 10.26599/BDMA.2020.9020003 10.1109/ACCESS.2017.2780250 10.26599/TST.2020.9010022 10.1016/j.infsof.2023.107241 10.1109/LNET.2019.2901792 10.1109/ACCESS.2021.3137318 10.1109/JSAC.2021.3087242 10.1109/ACCESS.2019.2903723 10.1016/j.comcom.2021.07.016 10.1109/ICECOS.2018.8605181 10.1016/j.cam.2023.115532 10.1016/j.ins.2021.05.016 10.1007/s11831-021-09694-4 10.1016/j.csite.2021.101250 10.1109/TNSE.2020.2990984 10.1109/TII.2021.3133300 10.1080/25742558.2018.1483565 10.1007/s00500-020-04954-0 10.1109/ACCESS.2018.2863036 10.1016/j.jnca.2023.103784 10.1016/j.cose.2023.103587 10.1109/ACCESS.2020.3019973 10.1016/0893-6080(95)00025-U
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References	C Yin (3800_CR42) 2020; 52 H Jia (3800_CR14) 2021; 178 L Nie (3800_CR15) 2020; 7 L Yu (3800_CR19) 2021; 194 MW Goudreau (3800_CR36) 1995; 8 E Alhajjar (3800_CR20) 2021; 186 Z Wang (3800_CR3) 2021; 9 Y Zhang (3800_CR16) 2019; 7 T Su (3800_CR22) 2020; 8 J Nasiri (3800_CR34) 2018; 5 AV Turukmane (3800_CR37) 2024; 137 L Nie (3800_CR6) 2022; 9 NB Singh (3800_CR25) 2021; 61 S Naseer (3800_CR9) 2018; 6 Y Zhang (3800_CR11) 2019; 7 L Yang (3800_CR12) 2020; 8 HV Vo (3800_CR38) 2024; 136 J Alikhanov (3800_CR5) 2022; 10 W Wang (3800_CR24) 2018; 6 M Ghalambaz (3800_CR33) 2021; 27 J Shu (3800_CR8) 2021; 22 3800_CR27 P Hewage (3800_CR28) 2020; 24 S Otoum (3800_CR1) 2019; 1 SM Kasongo (3800_CR7) 2019; 7 MH Haghighat (3800_CR21) 2021; 26 FA Khan (3800_CR23) 2019; 7 AG Gad (3800_CR32) 2022; 29 B Hu (3800_CR13) 2022; 18 S Latif (3800_CR39) 2024; 221 X Zhang (3800_CR40) 2024; 438 S Indolia (3800_CR35) 2018; 132 H Yang (3800_CR10) 2019; 7 A Hrusto (3800_CR26) 2023; 160 G Andresini (3800_CR18) 2021; 569 3800_CR30 G Xie (3800_CR4) 2021; 22 D Han (3800_CR17) 2021; 39 G Shen (3800_CR29) 2018; 131 M Khishea (3800_CR31) 2020; 149 W Zhong (3800_CR2) 2020; 3 T Zhang (3800_CR41) 2024; 217
References_xml	– ident: 3800_CR27 doi: 10.1109/ICIT58056.2023.10226123 – volume: 22 start-page: 4519 issue: 7 year: 2021 ident: 3800_CR8 publication-title: IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2020.3027390 – volume: 131 start-page: 895 year: 2018 ident: 3800_CR29 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2018.04.298 – volume: 194 start-page: 108117 year: 2021 ident: 3800_CR19 publication-title: Computer Networks doi: 10.1016/j.comnet.2021.108117 – volume: 149 start-page: 113338 year: 2020 ident: 3800_CR31 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113338 – volume: 7 start-page: 119904 year: 2019 ident: 3800_CR16 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2933165 – volume: 9 start-page: 16062 year: 2021 ident: 3800_CR3 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3051074 – volume: 61 start-page: 102899 year: 2021 ident: 3800_CR25 publication-title: Journal of Information Security and Applications doi: 10.1016/j.jisa.2021.102899 – volume: 7 start-page: 82624 year: 2019 ident: 3800_CR10 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2923814 – volume: 136 start-page: 103567 year: 2024 ident: 3800_CR38 publication-title: Computers and Security doi: 10.1016/j.cose.2023.103567 – volume: 217 start-page: 108583 year: 2024 ident: 3800_CR41 publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.108583 – volume: 186 start-page: 115782 year: 2021 ident: 3800_CR20 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115782 – volume: 7 start-page: 38597 year: 2019 ident: 3800_CR7 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2905633 – volume: 52 start-page: 112 issue: 1 year: 2020 ident: 3800_CR42 publication-title: IEEE Transactions on Systems Man and Cybernetics Systems doi: 10.1109/TSMC.2020.2968516 – volume: 22 start-page: 4467 issue: 7 year: 2021 ident: 3800_CR4 publication-title: IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2021.3055351 – volume: 132 start-page: 679 year: 2018 ident: 3800_CR35 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2018.05.069 – volume: 9 start-page: 134 issue: 1 year: 2022 ident: 3800_CR6 publication-title: IEEE Transactions on Computational Social Systems doi: 10.1109/TCSS.2021.3063538 – volume: 8 start-page: 29575 year: 2020 ident: 3800_CR22 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2972627 – volume: 7 start-page: 30373 year: 2019 ident: 3800_CR23 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2899721 – volume: 3 start-page: 181 issue: 3 year: 2020 ident: 3800_CR2 publication-title: Big Data Mining and Analytics doi: 10.26599/BDMA.2020.9020003 – volume: 6 start-page: 1792 year: 2018 ident: 3800_CR24 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2780250 – volume: 26 start-page: 484 issue: 4 year: 2021 ident: 3800_CR21 publication-title: Tsinghua Science and Technology doi: 10.26599/TST.2020.9010022 – volume: 160 start-page: 107241 year: 2023 ident: 3800_CR26 publication-title: Information and Software Technology doi: 10.1016/j.infsof.2023.107241 – volume: 1 start-page: 68 issue: 2 year: 2019 ident: 3800_CR1 publication-title: IEEE Networking Letters doi: 10.1109/LNET.2019.2901792 – volume: 10 start-page: 5801 year: 2022 ident: 3800_CR5 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3137318 – volume: 39 start-page: 2632 issue: 8 year: 2021 ident: 3800_CR17 publication-title: IEEE Journal on Selected Areas in Communications doi: 10.1109/JSAC.2021.3087242 – volume: 7 start-page: 31711 year: 2019 ident: 3800_CR11 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2903723 – volume: 178 start-page: 131 year: 2021 ident: 3800_CR14 publication-title: Computer Communications doi: 10.1016/j.comcom.2021.07.016 – ident: 3800_CR30 doi: 10.1109/ICECOS.2018.8605181 – volume: 438 start-page: 115532 year: 2024 ident: 3800_CR40 publication-title: Journal of Computational and Applied Mathematics doi: 10.1016/j.cam.2023.115532 – volume: 569 start-page: 706 year: 2021 ident: 3800_CR18 publication-title: Information Sciences doi: 10.1016/j.ins.2021.05.016 – volume: 29 start-page: 2531 year: 2022 ident: 3800_CR32 publication-title: Archives of Computational Methods in Engineering doi: 10.1007/s11831-021-09694-4 – volume: 27 start-page: 101250 year: 2021 ident: 3800_CR33 publication-title: Case Studies in Thermal Engineering doi: 10.1016/j.csite.2021.101250 – volume: 7 start-page: 2219 issue: 4 year: 2020 ident: 3800_CR15 publication-title: IEEE Transactions on Network Science and Engineering doi: 10.1109/TNSE.2020.2990984 – volume: 18 start-page: 4286 issue: 6 year: 2022 ident: 3800_CR13 publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2021.3133300 – volume: 5 start-page: 1 issue: 1 year: 2018 ident: 3800_CR34 publication-title: Cogent Mathematics and Statistics doi: 10.1080/25742558.2018.1483565 – volume: 24 start-page: 16453 year: 2020 ident: 3800_CR28 publication-title: Soft Computing doi: 10.1007/s00500-020-04954-0 – volume: 6 start-page: 48231 year: 2018 ident: 3800_CR9 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2863036 – volume: 221 start-page: 103784 year: 2024 ident: 3800_CR39 publication-title: Journal of Network and Computer Applications doi: 10.1016/j.jnca.2023.103784 – volume: 137 start-page: 103587 year: 2024 ident: 3800_CR37 publication-title: Computers and Security doi: 10.1016/j.cose.2023.103587 – volume: 8 start-page: 170128 year: 2020 ident: 3800_CR12 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3019973 – volume: 8 start-page: 793 issue: 5 year: 1995 ident: 3800_CR36 publication-title: Neural Networks doi: 10.1016/0893-6080(95)00025-U
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SubjectTerms	Algorithms Communications Engineering Computer Communication Networks Control systems Cybersecurity Data transmission Deep learning Electrical Engineering Engineering Heuristic methods Intrusion detection systems IT in Business Machine learning Networks Optimization Original Paper Performance enhancement Robust control Standard data Target detection
Title	Deep intrusion net: an efficient framework for network intrusion detection using hybrid deep TCN and GRU with integral features
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